Integrating data analytics and blended quality management to optimize higher education systems (HEES)

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Content Running head: DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 1
INTEGRATING DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT TO
OPTIMIZE HIGHER EDUCATION SYSTEMS (HEES)
by
Nick Vyas
A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION
August 2019
Copyright 2019 Nick Vyas
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 2
ACKNOWLEDGEMENTS
I would first like to acknowledge the committee chair, Dr. Rob Filback, and committee
members, Dr. Ruth Chung and Dr. Ravi Kumar. Your guidance, support and mentorship helped
me throughout this journey. At Miami University of Ohio, I would like to thank Al Ryan,
Rosanne Gulley, and Dr. David Creamer for your hospitality and graceful ways to integrate me
with all of the various stakeholders to understand the phenomenal work of Mu-lean. At the
University of Southern California, I would like to thank the staff, faculty and support office for
Global Ed.D for their amazing execution of every details.
I would like to thank my incredible wife Hemali for continuing to encourage me to
pursue my dreams and stretch my boundaries - you are a blessing in my life. To my children
Siddhartha and Pooja who have been incredibly supportive. Your passion for life and social
consciousness inspires me to focus on what excites me more than words can describe. I also want
to thank my late parents for their unconditional love and nurturing, which has shaped me into
who I am today. And I also want to thank my sisters Anju and Uma, and my brothers-in-law
Rohan and Rakesh, for their selfless gestures of support throughout the writing process.
I’m blessed to have so many friends, followers, and supporters that I cannot name here –
thank you for your continued support throughout my journey leading to this doctoral dissertation.
Lastly, I’d like to send sincere appreciation to my mentor and friend, Dr. Ravi Kumar for
believing and trusting in my ability to enter into the higher education space.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 3
TABLE OF CONTENTS
Acknowledgements 2
List of Tables 6
List of Figures 7
Chapter One: Introduction 8
Background of the Problem 9
The Long-Term Impact of Student Debt 10
The Rising Cost of Higher Education 11
The Draw of International Enrollments 11
HEI's Return on Investment (ROI) 12
Funding in HEI 13
American Dream, on Paper 14
Importance of Examining Promising Practices 15
Conceptual and Methodological Framework 16
The MU-Lean Approach 17
Organizational Context and Mission 17
Organizational Performance Goal and Current Performance 18
Description of Stakeholder Groups 19
Purpose of the Project and Questions 20
Chapter Two: Review Of The Literature 22
History of Quality Management 22
Concepts of Lean Thinking 24
Lean Six Sigma 26
Principles of Total Quality Management (TQM) 27
Background of the Blended Quality Management System in HEI 30
Challenges of Blended Quality Management 31
Analyzing Student Debt 32
Cost-containment and Competing Theories 32
Strategic Framework Model in HEIs 33
Understanding the Higher Education Excellence System (HEES) principles. 35
The Need for Higher Education Optimization System (HEES) 36
Knowledge, Motivation and Organizational Influences for HEES 38
Knowledge Attributes of HEES 41
Motivation 42
Expectancy – Value Theory (EVT) 43
Organizational Influences 46
Cultural Model and Settings 46
Resource Alignment 47
Chapter Three: Methods 50
Methodological framework 51
Participating Stakeholders 52
Survey Sampling Strategy and Rationale 52
Interview and/or Focus Group Sampling (Recruitment) Strategy and Rationale 54
Data Collection and Instrumentation 55
Surveys 56
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 4
Interviews 57
Data Analysis 57
Credibility and Trustworthiness 58
Ethics 59
Limitations and Delimitations 60
Chapter FOUR: Data and Findings 62
Location and Participants 63
Research Question One 64
Cost–Containment Driven by the 2008 Recession and Subsequent Funding Cuts 64
Receptiveness to New Ideas 65
Receptiveness to Change Management 66
Engagement of All Stakeholders 67
Committed Leadership 67
Summary of Findings of Research Question One 67
Research Question Two 69
Training, Recognition and Reward Systems 69
Empowerment and support from leadership 72
Experience and Expertise 73
Summary of findings of Research Question 2 75
Research Question Three 76
Knowledge Assets 76
Motivational Assets 78
Organizational Assets 78
Summary of Findings of Research Question Three 82
Research Question Four 82
Communication 82
Emerging Technologies 83
Awareness of Lean principles 83
Motivation Sets Differ With Roles 84
Summary of findings of Research Question Four 85
Research Question Five 86
Predictive and Descriptive Data Analytics Are Critical 86
Summary of findings of Research Question Five 87
Stratification analysis of KMO Attributes 88
Summary 89
Chapter Five: Recommendations and Implementation 91
Phase 1: Launch of HEES with Lean/TQM transformation 93
The Pre–launch Sequence (0–12 months) 93
In Session Launch Sequence (18–24 months) 102
Post–launch sequence (Past 24 Months to 120 Months Plus) 103
Summary for Phase 1 105
Phase 2 – Sequence 1 (P2S1): Launch of HEES Six Sigma Initiative 105
Pre–Launch Sequence 105
Pre–Launch Sequence (0–12 Months) 106
Getting Started. 107
DMADV (Define, Measure, Analyze, Design, and Verify). 107
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 5
In Session Launce Sequence (18–24 months) 109
Post–Launch Sequence (Past 24 Months to 120 Months Plus) 111
System Validation 111
Summary for Phase 2 111
Phase 3 (P3S1 and S2): Launch of Advance Data Analytics, AI ML, and RPA within
HEES 112
Pre–Launch Sequence (0–12 months) 112
Step 7: Platform Specifications. 117
Recommended Reference API Example 117
Project Plan 117
Phases 118
Summary for Phase 3 118
HEES/ AI/ML/Advance Data Analytics Model Implementation Plan. 119
Implications for Future Research 119
Conclusion 120
References 123
Appendix A: Survey Items 141
Appendix B: Interview Protocol 148
Appendix C: Definitions 152
Appendix D: Informed Consent/Information Sheet 155
Appendix E: Why’s Model 156
Appendix F: Literature Review Summary 157
Appendix G: Survey Questions and its Relationship Stage 172
Appendix H: Abbreviations 177
Appendix I: Checklist 178
Appendix J: Project Charter Worksheet 180
Appendix K: Stakeholder Analysis Worksheet 183
Appendix L: API Example 184
Appendix M: Tables and Figures 185
Appendix N: Prototype Wireframe 231
Appendix O: Google Cloud Platform 234
Appendix P: Developing the Business Case 236
Appendix Q: Attribute Description 241

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 6
LIST OF TABLES
Table 1: Description of Stakeholder Groups 19
Table 2: Description of Stakeholder Attributes 20
Table 3: Four Concepts Integral to Lean Thinking 25
Table 4: Seven Areas of Waste 25
Table 5: Causes of Lean Six Sigma 27
Table 6: Best Practices of TQM 28
Table 7: Deming’s 14 Principles Revisited 28
Table 8: Challenges of Blended TQM System 32
Table 9: Attributes of HEES 35
Table 10: Assumed Knowledge Influences for MU-Lean Stakeholder Group 42
Table 11: Assumed Motivation Influences for MU-Lean Stakeholder Group 45
Table 12: Data Collection Summary 56
Table 13: The HEES Implementation Plan 91
Table 14: Aspects of DISC Project 96
Table 15: DMAIC Project 107
Table 16: DMADV Project 108

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 7
LIST OF FIGURES
Figure 1: Six Sigma measurements standard. 23
Figure 2: Defect per Million Opportunity Sigma Level Conversion. 24
Figure 3: Blended Quality Management System. 31
Figure 4: Cost index comparison of tuition cost, and other key categories. 33
Figure 5: Strategic Framework of HEI – foundation for a new, robust model. 34
Figure 6: The HEES Model. 38
Figure 7: Segmentation of MU-Lean initiative. 40
Figure 8: Cultural Model and Cultural Settings Influences. 49
Figure 9: Gap Analysis Process Flow (Yates, 2013). 51
Figure 10: Model for data instruments and various inputs and their linkage to HEES central
delivery systems along with intended outcomes. 55
Figure 11: Survey Participants Perceptions being rewarded for efforts for working on MU–Lean
projects. 71
Figure 12: Survey Participants Perceptions being recognized for efforts for leading on MU–Lean
projects by the number of years of Six Sigma experience before MU. 72
Figure 13: Survey Participants Perceptions towards how important it is for MU–Lean to continue
to deliver the cost savings for the next 10 years as a part of the cost–containment initiative. 77
Figure 14: Survey Participants Perceptions towards MU–Lean has been able to collaborate with
faculties. 79
Figure 15: Survey Participants Perceptions towards MU–Lean has been able to collaborate with
various departments. 80
Figure 16: HEES Organizational Chart. 94
Figure 17: Focus and Goal of Various Project Types. 95
Figure 18: HEES Gantt Chart. 106

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 8
CHAPTER ONE: INTRODUCTION
In a progressively competitive global economy, obtaining a college degree has become
increasingly important. The United States has a stellar record of having the world’s top
universities and producing millions of graduates each year. However, the Higher Education
Institutions (HEI) in the U.S suffers from lack of cost-containment efforts. The inefficiencies,
redundancies, and lack of system collaboration are costing the institutes millions of dollars,
impacting the future of students, families and society at large (Archibald & Feldman, 2008). The
lack of cost-containment is evident in rising tuition, soaring student loan debt, reduced access
and socio-economic disparity.
The steady increase in the cost of higher education is an age-old problem. According to
Clotfelter (1996), there have been significant cost increases in both public and private
universities in the U.S. for over a century. He examined a data set spanning over 100 years
highlighting the positive trend in the cost of education. The trend accelerated after 1970, a
phenomenon that continued through 2018 (Clotfelter, 2007). Clotfelter argues that the upward
trends do not indicate any noticeable measures on part of HEI to contain costs for the period of
analysis. Pew Research also points to many other studies showing that for over 85 years after the
World War II, the curious coupling of increased revenue and higher costs have not been
prevalent in any industry globally, as it has been at HEI (Baum, 2016). There is the question of
the relevance of HEI in society if cost-containment is not made a top priority.
The rising student debt is among the worst fallouts of the lack of cost-containment at the
HEI (Wilbert, 2014). Public and private universities alike have seen close to 3% annual inflation
on average in the cost of tuition (Popescu, 2017). Even more, the rate of inflation in college
tuition exceeds every consumer category, except prescription drugs (Wellman, 2010). Many
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 9
research studies have suggested that the current rate of growth in domestic student debt and the
rate of inflation in university tuition are not sustainable. The current $1.6 trillion of student debt
not only creates a financial burden for the student and families creating equtity, access, and
standard of living (Chirs, 2013). Many of these trends are symptomatic of significant structural
issues facing the HEI. The rise of access inequity in top-tier schools, the tuition inflation across
HEI, increases in student loans – both for private, for-profit and community colleges -- combined
with a lack of innovation in the education delivery system can render the HEI obsolete (Martin,
2010).
Background of the Problem
Cost-containment has two economic components; inputs (student enrollment,) a supply-
side variable, and outputs (meaningful employment), a demand-side variable, which measures
the return on investment (ROI) for the student. On the supply side, student enrollments at the
HEIs have seen a tremendous growth, a big part of which is coming from the emerging markets
(Wright, 2013). Therefore, American students must work with a reduced quota of enrollment as
HEIs are aiming to increase enrollment for internationals students to maximize revenue.
According to Pew Research (2016), the numbers of international students doubled between 2008
and 2016. On the demand side, employers increasingly seek a much higher level of education
from the workforce. The demand has reached such a point that education has become a lifelong
process for workers who wish to stay relevant in the marketplace (Ewell, 1998). These market
conditions have increased competition for entry into the top-tier schools, where the global
demand along with the higher cost of access for top-ranked universities has caused further stress
to students who are citizens of the U.S (Rusinko, 2005).
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 10
The Long-Term Impact of Student Debt
According to Pew Research (2017) the socio-economic impact of a student with debt
versus that of students without liability is telling. The student group with debt seems to have a
weak outlook on the value of education and social equality as the high-interest loans consume
them. The American dream has always incorporated the ideology of families sending children to
HEI, helping them find meaningful jobs and subsequently purchasing a home and starting a
family. Indeed, the value of hard work and working one's way to earn a degree was status quo,
whereas carrying debt was not. In fact, the most ubiquitous source of debt would be a mortgage
on a home. The cost of education, associated lifestyle expenses, books, and other miscellaneous
expenses have created a significant burden on students and families (Chirs, 2013). The
Millennials’ acceptance of living with debt and making the minimum payments (Wilbert, 2014)
is further accentuating the problem.
A New York Times article published in 1987 had a headline stating, "Tuitions Hit a New
Peak, Igniting a Bitter Debate." The report portrayed HEI as a self-serving, greedy, self-centered
and too-focused-on-research entity that was focused on brand building rather than on student
learning and outcome. The article also documented the cost increases in HEI and noted that they
were even higher than medical costs, thus underlining that HEI was laden with bureaucracies,
inefficiencies and outdated practices (Skyes, 1988). Despite the considerable outrage and
debates, thirty years later the problem has snowballed into a much more significant crisis. There
is clear evidence that the system cannot sustain this for another thirty years. The current cost
spikes extrapolated over 30 years would suggest that tuition fees would go up by at least 250%
(Martin, 2012). Maintaining of status quo will result in the substantial segment of the population
unable to afford access to universities. In fact, the only way the system will sustain itself will be
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 11
by continuing their dependence on emerging markets to provide over 60% of the revenue
(Martin, 2012). However, the demand from these countries is likely to fall as our immigration
policies change and they build their education infrastructure.
The Rising Cost of Higher Education
The leadership within colleges, in general, does not view the cost increase as the most
pressing issue (Akdere, 2007). Universities, on their part, have surprisingly been able to elude it
thus far even when the average cost has increased 3% annually year-over-year. In fact, HEI’s
cost inflation outpaces the cost inflation of any other industry, including that of healthcare
(Martin, 2012).
The Draw of International Enrollments
Higher education at a global scale has significantly changed over the past seven decades,
from the methods of teaching, to training and funding. Globalization has played a vital role in
driving this change by bringing in more foreign students into U.S. HEIs. Thus, making it
increasingly difficult for U.S applicants to gain acceptance and pursue degrees (Anderson, 2016).
Between 2005 and 2013, the number of international students enrolling in undergraduate
programs in the U.S. increased by over 50% (Sen, 2016). However, a high influx of foreign
students was seen immediately after the 2008 financial crisis. Years following the 2008
recession, the enrollment of foreign students in U.S. HEIs grew by a total of 104% (Radford &
Ruiz. 2017). This increase was reported primarily from public colleges and universities that
faced budgets cuts (Radford & Ruiz, 2017). Public schools experienced a 107% increase of
foreign students, compared to 98% enrolling in private institutions (Radford & Ruiz, 2017). Most
international students pursuing higher education degrees in the U.S. were from China (30%) and
India (18%), and South Korea (6%) by 2016 (Radford & Ruiz, 2017). Graduate degrees in
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 12
particular were the most commonly pursued programs. In 2016, nearly 50% of international
students were enrolled in masters or doctorate degrees (Radford & Ruiz, 2017).
Universities prefer to admit more of these foreign students, as they provide better
economic benefits such as higher revenues (Field,. 2006). In 2016, international students spent
over $15 billion on higher education; the costs include tuition fees and living expenses,
compared to $5.5 billion in 2008 (Radford, J., Ruiz, G. 2017). Due to visa restrictions, the
majority of foreign students are forced to leave the U.S. post-graduation. This negatively impacts
the labor market in the country and the economic growth in the long term (Sen, 2016).
HEI's Return on Investment (ROI)
Higher education in the U.S. has a strong reputation worldwide. Over half of the world’s
top 100 universities are based in the U.S., including eight of the top ten. However, there is a
growing fear in the U.S. about higher education. External forces such as industry disruptions are
requiring students to learn new skill sets to survive these challenges. Therefore, it is imperative
that post-graduate employment is linked to amicable outcome in which the cost of education is
reasonably repaid in eight years. College educated individuals expect to earn more post-
graduation and aim to receive better returns on their investments. According to Georgetown
University Center on Education and Workforce, a bachelor’s graduate earned 75% higher than a
high school graduate in 1999 (Oreopoulos & Petronijevic, 2013). In 2009 the premium numbers
grew to 84%, and with higher earnings young adults were encouraged to enroll in a college
education (Oreopoulos & Petronijevic, 2013). However, currently the imbalance in the cost-
benefit calculus of higher education is deterring prospective college students from applying.
While institutions and parents try to motivate the youth to pursue college, the cost of earning a
degree has grown exponentially. Therefore, it has led to increases in borrowing and higher
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 13
student debts. With more bills to pay, the current generation is taking longer to complete their
programs. Present youth interested in enrolling in colleges are also unaware of the financial
elements of investing in higher education (Oreopoulos & Petronijevic, 2013). The lack of
information impacts their ability to make strategic decisions on which schools and programs to
invest in. Some higher education institutions have tried to reduce the student debt crisis through
the tuition reset model (Seltzer, 2017). These price cuts in tuition fees are predicted to encourage
more local students to enroll; however, the reality is far more complex. With tuition resets,
colleges and universities would experience a decrease in revenue, which could potentially result
in reductions in financial aid (Bloom, 2017). A decrease in financial aid will discourage the
middle class and minority groups from applying to universities, thus resulting in higher
unemployment in the country.
Funding in HEI
Funding in HEI has been a vital factor in providing a quality education and maintaining
costs in colleges and universities (Mitchell, Leachman, & Masterson, 2016). Over the past
decade public HEIs have faced deep cuts in funding. These cuts in state funding have driven up
tuition costs, reduced faculty staff, and have caused decreases in course offering and campus
closings. These impacts have made colleges and universities less affordable and accessible for
young adults who need higher education to succeed in today’s economy (Mitchell et al., 2016).
Most states cut funding post-recession. It took years to restore funding to pre-recession
levels. Dramatic cuts were made in many states including Louisiana, Arizona, New Hampshire
and Mississippi (Doyle, 2013). In 2015, the U.S. Census Bureau revealed that 29 states were
providing less total school funding per student than they were in 2008 (Leachman, Masterson, &
Figueroa, 2017).
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 14
American Dream, on Paper
The American Dream has long been the gold standard of a life people wish to pursue in
the U.S. Student debt is hindering the American Dream. An average college graduate leaves
university with $34,000 in education loans (Lynn, 2017). It doesn’t help that most young adults
entering universities and colleges are not financially literate. Studies conducted indicate that
more than 70% of the youth lack the basic knowledge of financial concepts such as interest rate
calculations, risk diversification and inflation (Lusardi, Mitchell, & Curto, 2009). Minorities,
such as African-American and Latino families, tend to have far less knowledge about college
financing and expense options (Gast & Jackson-George, 2015). This lack of knowledge has led
to a serious student debt crisis (Baum, 2016).
In summary, the issue of the cost-containment needs to be addressed post-haste, because:
• The rising student debt of over $1.6 trillion is not only unsustainable, but is also
detrimental to society.
• The rise of international students and its contribution towards HEI’s revenue helps avoid
the sense of urgency for cost-containment issues.
• The increase in higher education demand and the need for frequent resets in skills and
knowledge because of market forces. The ROI of the many educational degrees for
students and families do not provide meaningful employment to be able to repay the debt
in a timely fashion.
• The macro-outlook of lack of cost-containment on new generations has a huge shift in
being able to maintain the “American dream”, where the most substantial debt post-
graduation should be on the first mortgage of a house rather than student loans (Wellman,
2010).
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 15
Importance of Examining Promising Practices
This higher education crisis is not unique, it is reminiscent of a recent crisis in the
healthcare industry, which ran a similar course: increased costs, decreased patient access and a
lack of impact on disease outcomes. The cost of health insurance has been rising all the while,
whereas the benefits have been reducing and deductibles have been going up. This issue had
begun to alienate a large population from the healthcare system. This trend continued for almost
35 years. To deal with the underlying problem of cost inflation, the healthcare industry in recent
years adopted TQM (Total Quality Management) and LSS (Lean Six Sigma) principles into
health insurance and hospital administration. This promising practice study proposes that
blended quality management principles using LSS and TQM will similarly help eliminate waste
and reduce the cost associated with higher education through optimizing resource allocations and
increasing efficiency throughout educational systems. The overall effect would be a streamlined
process from enrollment to matriculation, generating a value-oriented degree that places
universities and their students in more excellent proximity to the workforce and industry needs.
The exemplary practices of Miami University with the use of MU-Lean initiative have
delivered a much-needed Lean system. The MU-Lean initiative provided a ecosystem for
continued improvement (CI) and sustained results with the help of training, sharing of
knowledge and best practices as well as getting the entire organization to buy into the
improvement journey.
Lean principles primarily focus on process - resource harmonization, bottlenecks,
redundancies, waste and speed of execution. The promising practice of MU-Lean can have
broader applications at other HEIs. In fact, the USC Marshall School of Business could prototype
a similar solution and benefit from Lean practices. MU-Lean would have helped the launch of
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 16
first online Master of Science in Global Supply Chain Management at the Marshall School of
Business, University of Southern California (USC). The curriculum design, approval cycle,
marketing, and linking the learning outcomes to pedagogy, the launch of the program structure –
all of these functions could have benefited through applying principles from both LSS and TQM.
As articulated in "Decoding the DNA of Toyota Production System (TPS)" (Spear and Bowen,
1999), the Lean system allows for customer-centric design framework that can make many of the
administrative processes simple and effective. The student/industry-centric program launch could
have been expedited and the go-to-market plan cycle time could have been less than the typical
16-month period. Numerous industries have perfected TQM and LSS practices. The
deployment of "Kaizen" which means change for good (Kato & Smelly, 2011), can create a
stakeholder-centric model to help establish better outcomes and results.
Conceptual and Methodological Framework
The Clark and Estes (2008) gap analysis provides a systematic, analytical framework that
helps to understand organizational accomplishments. This analytical framework was utilized as
the conceptual framework within a promising practice study. The methodological framework is
a mixed-method case study with descriptive and inferential statistics. Assumed knowledge,
motivation, and organizational assets were generated based on personal experience, an industry
benchmark, various use cases and related literature. The study utilized multiple methods such as
surveys, interviews and content analysis. Research-based solutions were recommended and
evaluated comprehensively to ensure that conceptual model is easy to emulate in other
organizations with ease-of-use and learning curve. The paper will evaluate other gap analysis
models in specific to driving enterprise wide operations transformation and systems. The
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 17
concept of data integration, synthetization and building system infrastructure will be analyzed
within the data modeling.
The MU-Lean Approach
The blended quality management model is a system consisting of best applicable body of
knowledge using tools and techniques from the TQM, Lean, and Six Sigma. Miami University’s
MU-Lean, the successful implementation of a Lean quality system, MU-Lean was examined.
Miami University adopted MU-Lean in 2009 as a business strategy centered on the student as the
customer.
The objective of MU-Lean was to meet the university’s goals without diminishing the
Miami student's experience. Lean is a well-proven quality management system that holistically
focuses on critical organizational dimensions such as cost, quality, stakeholder satisfaction,
employee engagement, collaboration and culture of continuous improvement (Kumar, Cullen,
Krishan & Anthony, 2017). The quality management principles have been successful in the auto,
insurance, banking, and biomedical research industries and others with exceptional outcomes. In
the last decade, healthcare has successfully deployed a Lean system known as "Lean Healthcare
(LH)" to drive cost-effective patient-centric healthcare excellence systems. HEI can also benefit
from a quality management system within the bounds of educational systems requirements.
Organizational Context and Mission
Miami University (MU) of Ohio is a public university located in Oxford, Ohio. It has a
student body of 16,000, with six colleges offering a variety of majors. The US News and World
Report, Kiplinger, Princeton, the FISKE guide to college, and Forbes have all recognized Miami
University for its value and focus on the growth of students. Miami University is recognized
nationally for its Lean system implementation, having earned the reputation of being the best
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 18
public university for ROI for students seeking to go to a public university. The MU-Lean
framework resides within a vision to organize human activities to deliver more benefits while
eliminating wastes – and also to reduce and control expenses to meet university goals while
improving student's experience. Through the MU-Lean initiative, the university has been able to
generate over $83 million of savings by completing over 1,200 Lean projects and training over
2,700 Lean staff members.
Organizational Performance Goal and Current Performance
The MU-Lean initiative appears to have created an entire ecosystem to deploy quality
management systems. It has completed over 1,200 Lean projects to date. In 2018, over 146 Lean
initiatives and 32 improvement ideas for process improvement have registered for completion at
Miami University. In comparison, in 2014 there were 67 active Lean initiatives. Over 359 Lean
ideas for improvement came from employees who are an integral part of establishing the culture
of continuous improvement. The MU-Lean initiative has certified over 2,500 employees with
over 157 Lean-certified members. They have also prepared over 59 senior Lean leaders that are
engaged in executing various Lean initiatives. This group of stakeholders includes faculty,
administrative staff, senior leadership, support staff and dedicated Lean specialists. The senior
Lean staff is working with over 200 Lean team leaders and over 700 Lean team participants.
Regarding the financial accomplishment to date, MU-Lean has delivered over $35 million in cost
avoidance and $15 million in cost reduction. MU-Lean has also generated over $7 million in
revenue totaling over $83 million to date savings for the university. Savings realized from Lean
initiatives can attribute reduction in the program fee, allocation of more resources for the student
and potentially capping of the tuition increases.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 19
Description of Stakeholder Groups
The study focused on Lean staff administrators (LSA). The primary contact within LSA
was the Director of MU-Lean as well as other Lean initiative administration members. This
group consisted of divisional Lean Leader-Academic Affairs, senior department Lean leaders at
the various schools, the office of research and scholarships, graduate school, corporate and
community, global initiatives, academic personnel and the enrollment management and student
success department. Within the Lean staff administration, the auxiliary group consisted of the
police department, finance department, internal audit, treasury and budget, housing, dining,
recreation and business services, human resources, IT services, advancement, student affairs as
well as some of the certified senior Lean leaders. The LSA stakeholder group was able to shed
light on each of the three dimensions of the KMO model. Collectively, they have delivered
results at Miami University through MU-Lean initiatives. In total, there were 8-10 members from
Lean staff administrator's stakeholder group to provide the end-to-end perspective of their
exemplary Lean journey at Miami University.
Table 1
Description of Stakeholder Groups
Organizational Mission

Improve operational efficiency through the organization by streamlining resources, eliminating waste and
redundancies as well as improving collaboration. Realized potential savings will be
reinvested in staff training, classroom resources and reduction in program fees.

Organizational Goal

The MU-Lean model will help identify opportunities that will enhance students learning the outcome,
faculty engagement and streamline administrative responsibilities. MU-Lean will help deliver the
following key outcomes:

• Create structure, program, and projects to implement the Strategic Plan
• Assist Miami University departments with creating a culture of continuous improvement
• Introduce the Lean strategy and tools to operations and assist with project execution
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 20
Table 1, continued
Organizational Goal

• Develop the internal capability for Miami to provide its own Lean training and development
program
• Develop leaders who can sustain a Lean culture at Miami University

Table 2
Description of Stakeholder Attributes
Lean Partners
Proficiencies/
Competencies Necessary
To Reach the Organization’s
Goal
Lean Faculty Members
Proficiencies/
Competencies Necessary
To Reach the Organization’s
Goal
Lean Administrators
Proficiencies/
Competencies Necessary
To Reach the Organization’s Goal
1) Identify critical to
classroom metrics
2) Gain application
submission to graduation
life-cycle and value
analysis
3) Create auxiliary
facilities, services, and
other delivery systems
effective and efficient
1) Gain perspective on
non-value-added task
performed outside of the
class room
2) Synchronize the student
learning outcomes and
associated best practice
1) Train the staff with Lean concepts and
certify them with
essential knowledgebase
2) Create a cross-functional Lean
organization
3) Identify and stratify process
improvement opportunities by
department, school
4) Launch SC-Lean initiative with high
intensity with clear mission, vision and
value proposition

Purpose of the Project and Questions
The goal of this study was to examine the performance of Miami University and its
quality management system, MU-Lean. The study focused on the eco-system the university has
created supporting the deployment, maintenance and sustaining the rubric areas of knowledge,
motivational strategies employed, and organizational commitment at all levels made available.
While a complete study would focus on all key stakeholders, the research focused on the MU-
Lean administration. As such, the questions that guide this study are the following:
1. What circumstances prompted the launch of MU-Lean?
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 21
2. What support structure was provided in preparation for the launch?
3. What knowledge, motivation and organizational assets are essentials to launch the
MU-Lean initiative and sustained over the years to deliver over $83 million in
savings?
4. What were the foundational elements of organizational culture and context,
stakeholder knowledge and motivation necessary to foster the philosophy of Lean?
5. How can emerging technologies such as Artificial Intelligence (AI), Machine
Learning (ML), and Advance Data Analytics help deliver efficient and effective
learning outcomes for student?

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 22
CHAPTER TWO: REVIEW OF THE LITERATURE
The literature review section was divided into two parts. The first section analyzed three
methodologies; Total Quality Management (TQM), Lean, and Six Sigma. These concepts have
gained considerable popularity as a robust method of holistically improving business efficiency
and effectiveness (Marksberry, 2011).
The second section applied the knowledge, motivation, and organizational (KMO)
framework to examine MU-Lean initiatives at Miami University. This section examined the
types of knowledge that are necessary to succeed in effectively implementing quality concepts,
the responsibilities of different stakeholders, and the skills and motivation needed to innovate
and to be part of the transformation efforts within organizations.
History of Quality Management
In the 1970s many Japanese organizations embraced Deming's 14 TQM principles. Most
notable was Toyota, which spawned several improvement practices including Just in Time
Business (JIT) and Total Quality Management (TQM; Marksberry, 2011). Consequently, the use
of blended quality management methodologies—such as Kaizen, quality circles, Lean, and Six
Sigma began to emerge in businesses throughout the United States (Francis, 2014).
Six Sigma’s roots can be traced back to its use as a measurement standard to Carl
Frederick Gauss (1777- 1855), who introduced the concept of the normal curve. However, it was
not until a Motorola engineer, named Bill Smith, coined the term “Six Sigma” that the concept
began to gain popularity (Misra & Ravinder, 2016).
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 23

Figure 1. Six Sigma measurements standard.
In the early and mid-1980s, with Chairman Bob Galvin at the helm, Motorola engineers
decided that the traditional quality benchmarks easured defects in thousands of opportunities but
didn’t provide enough granularity (Misra, & Ravinder, 2016). With this new benchmark, defects
per million opportunities, Motorola created the foundation for what is now the current-day Six
Sigma methodology and added cultural change tactics to generate sustainability around this new
process (Misra & Ravinder, 2016). At that time, the robust bottom-line result was more than $16
billion in savings for the organization, all because of the implementation of Six Sigma. However,
it was Jack Welch who made Six Sigma renowned when he made it central to GE’s core
operating principles (Lakshminarayanan, 2014).
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 24
The Sigma measurements helped more accurately compare the performance of one
process to another. As the chart below signifies, a six-sigma process is one in which 99.9997%
of the products/services manufactured are statistically expected to be free of defects (3.4
defective parts/million).

Figure 2. Defect per Million Opportunity Sigma Level Conversion.
Concepts of Lean Thinking
Lean Thinking, in essence, is a process that looks to maximize customer value while
minimizing waste (Comm & Mathaisel, 2005). The latter is identified as anything that does not
add value for the customer. The term "Lean" was coined to describe Toyota's business during the
late 1980’s by a research team headed by Jim Womack, Ph.D., at MIT's International Motor
Vehicle Program (Francis, 2014). The term “Lean transformation” is often used to characterize
an organization that is moving from an old or current way of thinking to Lean thinking. Lean
thinking Lean requires an enterprise-wide change in thinking that takes a long-term perspective
and perseverance to achieve success (Kumar, Cullen, Krishan, & Anthony, 2012). There are four
concepts integral to Lean thinking:
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 25
Table 3
Four Concepts Integral to Lean Thinking
Concepts Goal
Value Identify the value of each activity, target
“non-value added” activities for removal, and
optimize “value-added” work
Flow

Align capacity with demand so that the
product/service flows at the right speed

Pull
Design information and ordering processes so
that actual customer orders trigger production
(i.e., not forecasting!)
Perfection Systematize the management of processes to
deliver optimum customer value

Value-analysis, one of the main categories of Lean practice, known as a, identified seven
“deadly” areas of waste (“Muda” in Japanese) within an organization (Kumar et al., 2012). The
important identification was to verify how a university’s day-to-day operations can be measured
through value analysis. The seven areas of waste include:
Table 4
Seven Areas of Waste
No. of Waste Type of Waste Example of usage within HEI
1) Excess motion How often paperwork and processes are
moved around before the final disposition
2) Over-production Creating more resources than needed
3) Excess transportation Items not located in ideal location thus
creating additional need for transportation
4) Waiting time Delays caused by wait time
5) Excess processing Too many decisions protocol introduced in
process causing additional delays, resources
and wait time
6) Defects and rework Paper work filed out correctly the first time
being returned for more clarification
7) Excess inventory On-hand supplies inventory more than life
cycle reorder point
8) Underutilization of people Staff, administration and shared resources
not well cross-trained outside of their silos
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 26
According to American Society of Quality (ASQ), Lean manufacturing techniques and
activities differ according to the application at hand, but they have the same underlying principle,
the elimination of all non-value-adding activities and waste from the business.
Value Added Activities are all those activities that transform or shape raw material or
information to meet customer requirements. ‘Waste’ is any activity that does not add value to the
product or service or activity for which the customer is not willing to pay (Douglas, Anthony &
Douglas, 2017).
Lean Six Sigma
Lean Six Sigma (LSS) is a combination of Six Sigma methodology and Lean thinking
developed by Toyota to eliminate defects in processes that led to improved customer satisfaction,
reduced defect and improved quality (Marksberry, 2011). It has a strong emphasis on both the
practical and the creative elements and can be tailored to any process, industry or problem space.
Its methods and tools are designed to be customer-focused, and it has a prime directive to seek
out and eliminate waste and wasteful practices continually. At its core, LSS is a way to measure
and define the capability of a process, with a goal to improve to near perfection (Holmes, Kumar,
& Lawrence, 2008). It is a system of management to achieve lasting business leadership and top
performance—applied to benefit businesses, customers, employees, and shareholders. Most of
all, it is a strategy for turning manufacturing and business processes into competitive forces. It
achieves all this by merely producing what is needed, when it is needed, and with a minimum
number of defects, materials, equipment, labor, and space (Alrifai, 2008). In the realm of LSS,
all processes have variations that stem from different causes.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 27
Table 5
Causes of Lean Six Sigma

Consequently, too much variation equates to trouble in business processes. To seek out
and diminish variance, LSS utilizes the DMAIC cycle—which stands for Define, Measure,
Analyze, Improve and Control.
Principles of Total Quality Management (TQM)
Similar to the previous discussion of Lean and Six sigma, TQM is another discipline
within the blended quality management area. Organizations have adopted TQM practices
worldwide, and what started as a quality movement in manufacturing, has now spread to other
industries including banking, insurance, healthcare, government and NGOs (Todorut, 2013).
With its growing success across these service institutions, the education sector is the prime
candidate for complete implementation of TQM systems. However, to survive in today’s
marketplace, HEIs need to make fundamental changes to their management of all resources
involved. Ensuring quality management throughout these changes are essential to remaining
competitive (Redmond, Curtis, Noone, & Paul Keenan. 2008). Therefore, institutions need to use
TQM models that have principles such as “teamwork, top management leadership, customer
focus, employee involvement, continuous improvement tool, training and more (Murad &
Rajesh, 2010).
Number of Causes Type of Causes
1) People

2) Information and metrics
3) Equipment and materials
4) Processes and procedures
5) Environment
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 28
Studies have indicated that schools with the following best practices can significantly
improve quality management in their respective institutions (Mehrotra, 2012).
Table 6
Best Practices of TQM

• Increasing the use of technology to enhance innovation
• Use a Kaizen approach to improve the management, leadership and resources
• Encourage an environment more input from teachers, students and parents
• Develop systems to measure performance in areas of activities
• Benchmark the school’s performance internally and externally
• Study the success and practices of other rivals or players in different sectors
• Establish an achievable timeline to measure the success of the system.

In addition, the renowned leader of quality management thinking, W. Edwards Deming
introduced 14 principles that can transform a service into a quality process (Prasad, 2017). These
14 principles can be implemented by HEI to deliver quality services that are reconceptualized to
demonstrate how the blended quality principles can be adopted for HEIs (See Table 7 below).
Table 7
Deming’s 14 Principles Revisited
Deming’s Original Principles Repurposed Principles for HEI
Create constancy of purpose for improving
products and services.
Improve students, society, research, staff with
an aim to become competitive and stay
relevant in HEIs.
1. Adopt the new philosophy.

We are in a new age of a HEI environment,
thus leadership must recognize the challenge
and create HE excellence as a part of strategic
vision.
Cease dependence on inspection to achieve
quality.

Cease dependency in measurement, such as
graduation rates, ranking, and job placement
alone rather than focusing on the whole
student. It is vital to understand the long-term
impact of personal, professional and socio-
economic lifestyles.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 29
Table 7, continued
Deming’s Original Principles Repurposed Principles for HEI
End the practice of awarding business on
price alone; instead, minimize total cost by
working with a single supplier.
Make cost-containment a part of every
stakeholder responsibility and deliver
reductions in total cost to students in society.
Improve constantly and forever every
process for planning, production and
service.
Make TQM apart of HEIs DNA and look to
create an ecosystem that supports cost-
containment, quality and agility apart of
cultural norms.

Institute training on the job.

Train every stakeholder with the
understanding of quality, cost control and
agility.
Adopt and institute leadership. HEI’s leadership should become the
champion of cost-containment by creating
learning systems that are meaningful and
relevant for students.
Drive out fear.

Promote collaboration between departments
and improve communication between faculty,
staff, students, administration and external
stakeholders.
Break down barriers between staff areas.

Breakdown barriers between administration,
academics, research, job placement and other
committees in charge of either approving or
processing various education initiatives.
Eliminate slogans, exhortations and targets
for the workforce.
Eliminate graduation and target phrases, but
rather promote the philosophy of student
centric HE systems that deliver optimal
solutions for each student in achieving their
optimum potential.
Eliminate numerical quotas for the
workforce and numerical goals for
management.

Create stakeholder centric goals and use big
data and data analytics; both predictive and
prescriptive to help shape the procedures and
policies.

Remove barriers that rob people of pride
of workmanship and eliminate the annual
rating or merit system.

Empower staff and other internal stakeholders
to identify and initiate continuous
improvement opportunities.

Institute a vigorous program of education
and self-improvement for everyone.

Initiate vigorous training programs and
encourage to break down cultural barriers to
help understand that every person has a
critical role to play and can make an impact
on student’s experiences and outcomes.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 30
Table 7, continued
Deming’s Original Principles Repurposed Principles for HEI
Put everybody in the company to work
accomplishing the transformation.

Create every stakeholder; admin, faculty,
staff, trustees, alumni and all other external
stakeholders to work together in creating a
seamless and synchronized education
excellence system that promotes an excellent
place to learn and provides meaningful
employment post-graduation. Provide
research ecosystem to promote productive,
meaningful, and impactful research outcomes.
Making resources available for teaching
faculties to help remove barriers while
engaging staff members to align their
processes to drive excellence in HEI.

Background of the Blended Quality Management System in HEI
The concept of Blended Quality Management, which is the blended principles of TQM,
Lean, and Six Sigma, goes back to the early 19th century under the body of knowledge of
scientific management (Taylor, 1911). Blended TQM is now widely adopted by financial
institutions, governments, retailers, hospitals and other segments of the services industry
(Benjamin, 2015). The combinations of these methodologies have helped numerous
organizations create faster processes, lower costs, and higher quality.
HEIs are no exception when it comes to encountering cost-containment challenges.
However, HEIs are complex and deeply rooted into historical paradigms. As such, the motivation
of this study is the development of a cost-containment system by understanding the constraint,
limitation, boundaries, and structure of education systems, focus on cost-containment and deliver
sustainable student-centric learning outcomes. A system that is designed and integrated with the
best-in-class practices of TQM, Lean, and Six Sigma, conceptualized as a Blended Quality
Management System.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 31

Figure 3. Blended Quality Management System.
The research inquiry will probe into the degree to which the Miami University
administrators are familiar with the concepts such as a basic knowledge of the blended quality
management framework, tools, application and the needed resources to help execute TQM
initiatives across the organization.
Challenges of Blended Quality Management
Blended TQM systems can benefit from reduced friction, improved collaboration and
seamless communication between various departments providing reduced cost, an improved
learning outcome for students. It can also deliver efficient systems for other stakeholders:
faculty, administrators, staff and industry-engagement coordinators. However, it appears that
there are some challenges of Blended TQM systems widely adopted in HEIs according to
(Anthony et al., 2012):

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 32
Table 8
Challenges of Blended TQM System
Limitations of Blended TQM System
• The terminology of TQM, Lean, and Six Sigma and its principles are not a direct fit
for a non-business-focused entity like a university.
• They effectively help those working in silos within departments, schools and
universities rather than those who wish to design as an integrated system.
• A lack of a consensus onon who the internal and external customers are and the need
for customer-centric delivery system.
• Adoptions display a lack of management commitment on cost-containment,
communication, transformative leadership, and commitment to excellence going
beyond accreditations, fund raising, research output and ranking.

Analyzing Student Debt
The investment in education is one of the most critical investments in life. For the first
time in America history, the current generation will have student loans, not the home loans, as
the most substantial debt post-graduation (Baum, 2016). According to Baum, in 2011-12, 7% of
graduates with master’s degrees had an average of $110,000 in debt. According to the Consumer
Finance Report 2012, of $1.56 trillion dollars of student debt, 47% is held by the high-income
group of $90,000. The middle-income group at $48,000 owns 25% of the debt. The 17% of debt
is owned by people making $25,000, and 11% of debt by the low-income group making less than
$24,000 per year (Baum, 2016). These numbers are staggering considering that student debt is
projected to reach over $2.5 trillion dollars by 2030.
Cost-containment and Competing Theories
The revenue theory cost is where the whole unit cost of education is determined by the
revenue available for education that can be found per student unit (Archibald & Feldman. 2008).
HEIs have no incentive to adopt innovative solutions. In fact, if the demand continues to rise
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 33
either domestically as well as internationally, there is no reason to feel pressure to drive
innovation (Archibald & Feldman. 2008).

Figure 4. Cost index comparison of tuition cost, and other key categories.
Many research studies have suggested that the current rate of growth in domestic student
debt and the rate of inflation in university tuition are not sustainable. For one, the rising tuition
cost results in higher student debt. The current $1.6 trillion of student debt not only creates a
financial burden for the students and families, but also impacts equality, access, and meaningful
lifestyle post-graduation due to monthly payments with a high-interest rate (Chirs, 2013).
Strategic Framework Model in HEIs
There are numerous framework models that could be used to address these challenges.
For instance the Toyota way, which is represented in Blended TQM, can be re-conceptualized
within the HEI framework in the following way:
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 34

Figure 5. Strategic Framework of HEI – foundation for a new, robust model.
The research of this model focused on the first three of the seven pillars defined in
strategic framework:
• Learning Excellence
• Operations Excellence
• Financial Excellence
However, based on literature review there is a lack of system thinking in HEI. Exhaustive
literature reviews further highlight clear evidence of partial deployment of Blended Quality
Management in HEI. In some cases, one or few of the elements in the strategic framework have
been used. Integrations of all seven elements have not been evident at the institutional level.
Furthermore, quality initiatives, not just finding operational excellence and eradicating waste,
need to be brought together cohesively and collectively. HEIs need to benchmark how many of
those pillars in strategic framework are evident in their organization. A collective solution to this
need can be viewed through a new model, known as the Higher Education Optimization System
Learning Excellence - Creative environemt that is engaged
and participitory in delivering effective learning outcomes
Operational Excellence - Doing it right the first time and every time
Financial Excellence - Doing more with less. Striving for equity and
access
Research Excellence -Meaningful, impactful and relevant
Stakeholder Excellence - Internal and external stakeholder focus
groups
Governance Excellence - Ethical, socially responsible and
community centric strategic framework
Innovation Excellence - Use Robotics Process Automation (RPA),
Artifical Intelligence (AI), and Machine learning (ML)
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 35
model (See Figure 6) from Input, Blackbox and intended Output. Higher Education Excellence
System (HEES) is an integrating mechanism that uses advanced data analytics and Blended
Qualtiy Management principles to provide an education delivery system that is efficient,
excellent and sustainable.
Understanding the Higher Education Excellence System (HEES) principles.
The foundation of HEES prepares an individual with the set of tools and methodology to
become a capable “change agent” to significantly improve business processes (Vyas &
Campbell, 2015). HEES helps reduce process variation, eliminate waste and defects, accurately
measure and analyze data for process improvement, identify and eliminate process variation
sources, and implement process control to sustain project improvement. Some of the attributes of
HEES system would be as follows:
Table 9
Attributes of HEES
• Understand the context of Lean Six Sigma and how to utilize the methods
in a university environment.
• Understand and learn to optimize organizational process management and the
metrics to measure cost, quality in student learning, research, learning efficacy
and placement performance.
• Understand how to manage a team and how to facilitate teamwork
• Understand and practice the utilization of the DMAIC methodology
• Understand and utilize Six Sigma frameworks and methodologies.

As with any organizational change initiative, stakeholders must know how to identify the
critical levers for change, and the skills individuals and leaders need to innovate effectively
(McDermott & Prajogo, 2005). The successful effort of Lean Six Sigma launch requires a blend
of both “art” and “science,” meaning it will require the combination of knowledge of HEES,
creating trained HEES certified individuals, and an organization that helps maintain the entire
ecosystem. Although the procedural and factual knowledge may provide an essential toolbox,
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 36
the conceptual and metacognitive expertise will help enable leadership to lead a successful
organizational transformation. Without the right level of knowledge, projects can face resistance.
In fact, the lack of leadership is proportionate to the level of resistance (Munro, 2014).
Often, a three-pronged approach should be applied to all matters with the HEES. It
includes the knowledge and skills of how to use HEES tools, leadership skills to know when and
how to influence stakeholders as well as project team members, and the wisdom to understand
the interdependencies of these components (Vyas & Campbell, 2015).
The Need for Higher Education Optimization System (HEES)
Upon extensive literature review analysis (Refer to Appendix F for Literature review
table summary), it is apparent that HEI has not adapted Blended Quality Management principles
in a systematic way. Rather, there is clear evidence that many institutes have adapted parts of
Lean, Six Sigma, and TQM - but not as a whole integrated system.
HEES presents an educational excellence model for the HEI in the same way that the
Toyota Production System, Lean Healthcare and Lean Finance have served as systems of
improvement and excellence for the automobile, healthcare, and finance sectors respectively.
The HEIs need a working system that can provide cost, quality and agility to all the key
stakeholders; students, faculties, staff, administrators, society, industry, and contributions to
advanced knowledge and research. Each of the quality systems has unique value propositions;
TQM, Lean, and Six Sigma can be properly adapted to HEI that can deliver cost, quality and
agility for the all of the key stakeholders. This system will be designed, systematized, and fully
adopted to HEI and can be called HEES. It is vital for strategists to keep in mind that when
implementing HEES, there is a difference between quality and cost management in education
and businesses. A university is not a factory and the student is not a product, “the education of
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 37
the student in the product” (Mehrotra, D. 2008). Thus, the best education can earn by ensuring
that students participate in a co-managing learning environment. HEI’s further need to focus on
the different processes of teaching and learning between education and businesses. The
education institutions need to relate teaching to management rather than a supervision of tasks,
and learning to more research and development that an assembly process. Drawing from
literature and industry use cases, the HEES model as illustrated in Figure 6 can be utilized in
higher education (Vyas, 2018). HEES is customized to HEI while adopting various lessons from
other industries. How well they manage the Blackbox to control the input determines how well
the output will be.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 38

Figure 6. The HEES Model.
Knowledge, Motivation and Organizational Influences for HEES
This section examines the types of knowledge, motivation and organizational factors
necessary for HEI administrators to successfully implement HEES concepts. The research
inquiry studied was the cost containment in higher education. Based on extensive research, it
appears that Miami University (MU) is one of the few universities that has conceptualized,
launched, and sustained their Lean transformation efforts (MU-Lean) over five years at the
university level, resulting in $83 million plus in savings. In the process, they have also been able
to able train and certify over 2700 associates across various schools, administrative supporting,
and auxiliary service creating knowledge of continuous improvement.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 39
The MU-Lean system has been able to create an essential ecosystem providing a further
hands-on experience by working on process improvement projects using conceptual knowledge,
empowering self-directed teams, and pursue further using their metacognitive skills. In the
traditional HEES deployment, administrators need to have the specific fundamental
understanding as well as conceptual knowledge to engage large stakeholder groups across the
organization.
This section examines the types of knowledge assets that are necessary to succeed in
effectively implementing HEES concepts organization-wide. The work of Anderson and
Krathwohl (2001) will be used as a conceptual framework to provide a comprehensive
examination of knowledge. According to Anderson and Krathwohl, optimal performance
requires four kinds of knowledge: factual, conceptual, procedural and metacognitive. The HEES
system started to find momentum from its manufacturing roots and gain momentum along the
many other sectors including healthcare recently.
To understand the MU-Lean initiative holistically, the MU-Lean transformation journey
is segmented into three sections: Pre-launch, In-progress Launch and Post-launch, which is an
important phase for sustainment. Each of the phases will be treated with two unique dimensions
of inquiry:
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 40

Figure 7. Segmentation of MU-Lean initiative.
First, understanding the antecedent of the MU-Lean journey will be key to assessing the
critical ingredient and environment for a conceptual design. In quality management
transformation journey, many organizations do not see the need to change until it is too late.
Mayer (2011) describes learning as a two-way street. A reciprocal relationship must exist
between theory and practice. For an MU-Lean-based initiative to take place, the stakeholders’
mindset, their ability to take the risk, limitations, cultural acceptance, and the ability to make the
upfront investment have to be analyzed and understood. The study will deploy the survey and
interview of Lean leadership, Lean administrators and partners. This stakeholder group has been
responsible for conceptualizing, designing, launching and successfully managing it since 2012.
Lastly, the research will try to understand the details of their $83 million savings by analyzing
the datasets. The objective here is to understand and bifurcate which areas, what types of
initiatives were focused on, and how the savings were actualized. The quantitative part of the
Mu-lean
Pre-launch preperation(conceptualtizion)
In-progress launch (initialization)
Post-launch reinforcement(sustainment)
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 41
data analysis will be useful to normalize the limitations of HEIs and understand how it differs
from other industries.
Knowledge Attributes of HEES
HEES project initiatives can fail in the process improvement cycle or in sustaining
momentum because organizations fail to recognize the importance of holistic engagement from
the various stakeholder groups. Often, the focus is merely on knowledge attributes; tools and
techniques of the initiative. The organization relies on outside experts to push through changes.
In other cases, the knowledge and leadership dimensions are viewed as critical elements to a
successful project. That typically tends to have a top-down directional force, creating a strategic
launch without engaging various levels of organizational stakeholders such as faculties, staff,
administration, and others. However, knowledge and leadership by themselves are not enough to
drive the sustained HEES transformation (Vyas & Campbell, 2015). In many instances, the
short-lived success of transformation stems from the misunderstood dimension of the wisdom.
Wisdom in HEES is knowing when and how to press forward, where to place the focus, how to
create self-directed teams, getting key stakeholders to move past the project mindset into making
the practice of continuous improvement a part of the day-to-day practice. It is an ability to
expertly navigate the continuous improvement journey of HEES with metacognitive capabilities
by combining knowledge, motivation, and organization assumed influences to work through the
complicated people issues that arise with change management and how Lean administrators at
Miami University handled these issues.
As illustrated below in table 10, there are various knowledge attributes critical in the
evaluation of end; conceptualization, initial launch, and post-launch sustainment of the HEES
initiative.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 42
Table 10
Assumed Knowledge Influences for MU-Lean Stakeholder Group
Assumed Knowledge Influence Knowledge Type
In-depth understanding TQM and HEES
principles
Declarative/Procedural
Knowledge of how to identify, initiate, and
drive Lean projects
Declarative/Procedural
Skill to create a Lean organization and an
essential eco-system

Declarative/Procedural
Skills to transfer knowledge to all the
colleges, administrative department and
auxiliary services
Declarative/Procedural
Ability and skill to deploy right methodology
and tools to successfully deliver the goals
Procedural
Ability to engage various stakeholders:
faculties, staff, support services, and senior
executives
Procedural
Teams are self-directed and capable of
identifying, initiating and leading successfully
to deliver the results
Metacognitive

Motivation
According to Pintrich and Schunk (2008) motivation is “the process whereby goal-
directed activity is instigated and sustained.” Pintrich and Schunk further illustrate critical
dimensions of motivation as a choice, persistence, and mental effort. To implement the HEES,
team members should have a self-desire to be part of the transformation efforts within the
various levels of the organization. The stakeholders must feel that they have a personal
investment in driving HEES to make an impact. They feel invested in wanting to learn new
quality management methodology and participate willingly with enthusiasm. Persistence, on the
other hand, is the fortitude to see that an initiative is not only launched but also brought to
successful completion. HEES certification process requires mental effort to generate new
learning and knowledge while cementing the gains from the completed project. In seeking to
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 43
understand which motivational influences have enabled Lean administrators to succeed in the
effort, the following sections will present Expectancy-value theory (EVT) and a self-efficacy
(SE) theory. The survey and interview protocol will help understand what factors helped
motivate Lean partners and administrators from the pre-Launch phase through the current
sustainment phase.
Expectancy – Value Theory (EVT)
Expectancy-Value Theory (EVT) helps explain why individuals engage in the dimensions
of motivation: choice, persistence, and mental effort. The history of the ETV dates to earlier
work of Lewin (1938) and Tolman (1932). According to Lewin and Tolman, the value of the task
is influenced by the importance of the task to that person, meaning the how important the
participant thinks the cost-containment initiative (HEES) is essential for the organization.
Further, work in this field solidifies the theory of reasoned actions and theory of planned
behavior worth of the commodity based on individual belief (Ajzen, 1991). Although the modern
day EVT work stems from the Atkin’s (1957, 1964) work, it is unique in a few ways. Primarily,
the definition and depth of understanding of expectancy and value belief are much greater tied to
various dimensions: cultural, social and psychological as opposed to having choice, persistence,
and mental effort. These models are validated in the real-world applications rather than control
experiments.
Importance of HEES using MU-Lean framework. HEES typically has a few
foundational ingredients; knowledge, leadership, change management receptivity, organizational
support and participants’ motivation (Emiliani, 2005). Implementing EVT in analyzing
motivation in this study helps to gain a deeper perspective on how to gain buy-ins from various
key stakeholders, getting the commitment of essential resources, and setting the foundation for
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 44
building an eco-system essential before the launch. It can provide insight into Lean
administrator’s MU-Lean’s pre-launch phase success. If Lean administrators think they may not
succeed with the MU-Lean initiative launch, the essential motivation across the organization will
be missing. Also, if the MU-Lean team members do not think that their work is valued in cost
containment efforts, they may not fully engage with the necessary mindset and change
management. Historically, the Lean transformation success is influenced by the organization’s
change management culture, ability to adopt the new methodology, tools, and believing that
there is an efficient way the operation can be executed (Svenssen, 2015).
Based on the initial dashboard review, Lean administrators appear to gauge, understand
and emphasize the role of motivation and producing results in an excess of $83 million. Beyond
the savings, Lean administrators have placed importance on the entire effort, respecting the
intrinsic value of the cost containment, understanding the utility value, and assessing the cost
associated with the cost containment within MU-Lean.
The self-efficacy theory. The concept of self-efficacy refers to an individual’s belief
about their capabilities to perform specific tasks. Self-efficacy theory helps predict the choice,
persistence and mental effort as an essential component for the successful HEES launch.
According to Bandura (1982), strong self-efficacy quotient has a direct correlation to human
performance. He believed that people with high assurance and self-confidence in their
capabilities tend to rise to the challenge rather than shying away from it. In fact, self-efficacy
theory suggests that strong self-efficacy coupled with assurance persevere one’s efforts despite
challenges. In contrast, the converse is also true – a person with low self-efficacy constantly self-
doubts and avoids challenging situations. Bandura’s work highlights further details on how the
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 45
sources of self-efficacy, efficacy-activated processes; cognitive, motivation, effective and
selection processes influence both at the personal and professional level.
MU-Lean team’s self-efficacy. The MU-Lean team has generated over $83 million in
savings year to date. Based on Bandura’s (1982) conceptualization of self-efficacy as a critical
predictor of successful performance, it is assumed that Lean administrators and Lean certified
team members must have self-confidence, self-motivation, and self-control to be on the
committee of Lean administrators. It shows that to initiate, launch and successfully continue to
be on a Lean transformation journey, MU has been able to create a feeling of self-efficacy,
assurance, self-confidence, and self-motivation within each of the Lean certified members, Lean
administrators as well as senior leadership. In the context of this study, it is crucial to determine
the sources of Mu Lean administrator level of confidence in being able to implement the
initiative. Lean administrators appear to have tremendous confidence and motivation to continue
to deliver strong results; delivering over $5 million in cost savings year to date.
Table 11
Assumed Motivation Influences for MU-Lean Stakeholder Group
Motivation Construct Assumed Knowledge Influence
Lean Administrators see value in driving
Lean Six Sigma (HEES) transformation as an
organization wide initiative to help support a
part of cost containment
initiative.
Utility Value
Lean certified members see value in driving
HEES transformation to make Miami
University the most cost-effective University.
Utility Value
Lean administrators are confident in their
ability to identify, initiate and execute Lean
projects throughout the university by building
Lean teams, driving consensus and certifying
750 university associates in Lean
methodology.
Self-efficacy

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 46
Organizational Influences
Along with knowledge and motivation influences, MU-Lean transformation using
Blended Quality Management Principles underpins its dependency on organization’s influences
which is the foundation of driving change management. MU-Lean adopted by all the schools,
administrative departments, auxiliary service units and senior leadership is a testament to having
a solid foundation. According to Clark and Estes (2008), beyond process and resource
management, it is essential to understand the organization’s culture. To holistically understand
the MU-Lean initiative, it is imperative to understand it in stages; a pre-launch stage, launch
stage, and post-launch stage. Each of the three stages has a unique set of qualifying KMO
attributes and assets. The requirement of an organization will vary within each stage as well as
during the transition. Many theoretical constructs address the practices and policies of the
organizational learning in the deployment of transformation initiative. This section discusses the
work of Gallimore, and Goldberg’s (2001), which focuses on the cultural model and setting-
related influences. The work by Collins (2001), Dickeson (1999), and Clark and Estes (2008)
focuses on resource alignment theory.
Cultural Model and Settings
According to Gallimore and Goldenberg (2001), cultural models consist of values,
beliefs, and attitudes as an underlying fabric of the organization. The philosophy of continuous
improvement and customer-centric focus becomes the DNA of an organization. Majority of the
time, those working within the organization fail to either notice it or do not recognize the impact
of organizational values and beliefs. On the other hand, the cultural settings are the symptoms of
the cultural models, which are palatable and felt by an individual within the organization and the
organization itself.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 47
Reviewing the monthly dashboard, newsletter, and quarterly updates it appears that there is
a consistent value, belief, attitude and mindset in place for MU-Lean to help drive cost-
containment as a part of their organizational fabric.
Resource Alignment
Clark and Estes (2008) recommends that for optimal performance, an organization should
align its processes, procedure, and systems to the goals. In Lean management, this concept is
known as a “value analysis,” meaning if the task or step performed does not enhance the output
for the customer then that step or task is non-value add and considered as a waste (Francis,
2014). A typical organization generates over 40% waste on average (Svensson et al., 2018). If
higher education institutions were to pursue cost-containment as their core objective, then
resource, process and system alignment were essential. The MU-Lean administration has a
structure in place to have precise alignment on how they have certified Lean staff at the process
level, supported by Lean projects, resources, tools and necessary empowerment. It is also
supported by Lean senior leadership tied to the MU’s core vision, value and mission statement
on driving cost-containment.
The Lean certification program is available to all the staff members across the campuses;
administrative, faculty, restricted and unrestricted staff members. The certification has three
levels; a Lean partner, Lean leader and senior Lean leader with a clear objective that Lean
leaders will sustain the Lean transformation culture at MU and support the cost-containment
initiatives. As defined by Gallimore and Goldenberg (2001), the models can be viewed as
“encoded shared environmental and event interpretations, what is valued and ideal, what settings
should be enacted and avoided, who should participate, the rules of interaction, and the purpose
of the interaction” (p.46). It appears that MU-Lean has been able to create the necessary support
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 48
structure in identifying the various levels of certifications to staff and leadership on a
volunteering basis.
The completed projects’ inventory ranges from streamlining contract negotiation
processes to improving the learning outcomes. On the surface, an assessment of the monthly
newsletter, financial updates, and business dashboard, MU-organization has created a supportive
structure both horizontally and vertically across the organization. This study probes into how this
structure is experienced by Lean administrators, the primary stakeholder group of the study.
HEES models underpin a robust cultural setting for the success of the post-launch phase
of the transformation, providing a stable ecosystem for Lean sustainability. In many industry
application of Lean transformation, lack of sustainability is correlated to lack of cultural settings.
In fact, lack of communication, transparency, tools, resources and organizational foundation
causes the gains to be short-lived. The MU-Lean initiative has defied the norms and appears to
be on track to deliver robust cost-containment outcomes. As illustrated in Figure 8, the set of
cultural model and cultural setting influences with related organizational influences.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 49

Figure 8. Cultural Model and Cultural Settings Influences.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 50
CHAPTER THREE: METHODS
The objective of this chapter is to enumerate and describe the methodological framework,
the survey sampling strategy and the rationale behind it, the data collection tools and establish
the ethics and trust territories of the survey methods. The study employs mixed methods using
both qualitative and qualitative sampling using interviews, surveys, and potentially
document/data analysis for validation of the performance assets. The conceptual framework for
inquiry questions use a gap analysis methodological framework developed by Clark and Estes
(2008) model; known as a Knowledge, Motivation, and Organization (KMO).
The study focuses on following questions:
1. What circumstances prompted the launch of MU-Lean?
2. What support structure was provided in preparation for the launch?
3. What knowledge, motivation and organizational assets are essentials to launch the
MU-Lean initiative and sustained over the years to deliver over $83 million in
savings?
4. What were the foundational elements of organizational culture and context,
stakeholder knowledge and motivation necessary to foster the philosophy of Lean?
5. How can emerging technologies such as Artificial Intelligence (AI), Machine
Learning (ML), and Advance Data Analytics help deliver efficient and effective
learning outcomes for student?
The promising practice used mixed methods using interviews and a survey for the key
stakeholder group – Lean Administrators and Partners. The study tries to understand the assumed
influences of knowledge, motivation, organization and its impact on MU-Lean initiative. It will
understand the role of Lean administrators, and Lean partner volunteers on how their role as a
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 51
change agent influences driving cost-containment projects. The instruments for interview and
survey will ensure relevant data measurement captured with proper validation, trustworthiness,
ethics, analysis, limitation, and delimitation along with the criticality of the role of the
investigator.
Methodological framework
The Clark and Estes (2008) gap analysis provides a systematic, analytical framework that
helps to understand organizational accomplishments. This framework helps conceptualize the
straw man within a promising practice study.

Figure 9. Gap Analysis Process Flow (Yates, 2013).
This gap analysis looks at the critical dimensions such as knowledge, motivation, and
organization. Each of the elements has a unique role in conceptualization, launch, and
sustainment of MU-Lean.
Further, the methodological framework is a mixed-method study with the use of
descriptive and inferential statistics. Assumed knowledge, motivation, and organizational assets
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 52
were based on personal experience, industry benchmark, various use cases and related literature.
The study utilizes multiple methods such as surveys, interviews, literature review and content
analysis. Research-based solutions will help evaluate comprehensively to ensure that conceptual
HEES model is easy to emulate in other HEIs with ease-of-use and diffused learning curve.
Participating Stakeholders
To evaluate the efficacy of the MU-Lean initiative at Miami University, research works
with Lean leadership consisting of administrators, senior department Lean leaders, and certified
senior Lean partners. In total, there were 27 Lean administrators in MU organizations. This
stakeholder group was able to share their views on intricacy of initiation of MU-Lean journey. In
addition, they shared the challenges encountered as well as critical factors essential to cement the
sustainment of MU-Lean initiatives delivering savings in excess of $83 million. The group
represents various schools within the university, central support staff, and the senior leadership.
All the participants from this stakeholder group are Lean-certified and are actively involved in
making the cost-containment the highest priority at the MU.
Survey Sampling Strategy and Rationale
Overall, the survey covered the Lean partners and Lean certified staff that were from
faculties, administration, and staff. There were over 2,500 Lean-certified partners associated
with MU-Lean initiatives at the time of taking the survey. Although the Qualtrics survey was
sent to the population of 2,500 with 95% confidence interval and 5% margin of error, the sample
size of 334 participants were sought after. This stakeholder group shared the background of how
the MU-Lean initiative was conceptualized, successfully launched, and sustained till now,
delivering results to date of over $83 million in savings. The survey instruments were sent out
few weeks before the visit to MU campus for a period of a week. A large sample of Lean-
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 53
certified partners and leaders were used to gain perspective on how stakeholders feel about the
engagement in driving continuous improvement at MU. The following criteria were selected to
narrow the survey participants.
Criterion 1. Lean administrator and partners have a clear understanding of the MU-Lean
initiative. They clearly understand the need and reason for its importance at MU.
Criterion 2. Lean administrator and partners understand the industry best practice on the
Lean transformational nuances and how it is adopted into HEI.
Criterion 3. Survey respondents are fully aware of the roles, responsibilities, and Lean
leadership, to be able to share the details on each of the three phases of MU-Lean.
The survey instrument was designed to understand how Miami University has
successfully conceptualized, launched and sustained the Lean transformation initiative (Mu-
Lean). The Lean initiative has resulted in over $83 million in savings for the university. The
survey instrument was also designed with the idea that other institutions can adopt at their
benchmark if there was a strong reliability index. The analysis of reliability tests resulted in
Cronbach Alpha of 0.92. This instrument will be utilized for future research and/or by an
organization to create a baseline assessment. In order to achieve the reliability of the instrument,
the 378-sample size was calculated at 95% confidence interval. Within various stakeholder
groups with populations of 1,800 plus, the research was able to get 438 participants to take the
full survey. The research sponsors were very instrumental in getting great participation rates. The
study was also able to achieve a relative minimum threshold of each of the key stakeholder
groups within the populations.
Each of the survey questions were directly linked to the research questions and
underlying knowledge motivation organizational assets. Survey was also designed to gain
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 54
perspective from the participants to understand their views about the Lean transformation
journey by asking them to provide their comments to an open-ended question at the end.
Interview and/or Focus Group Sampling (Recruitment) Strategy and Rationale
Fourteen confidential interviews were conducted. Interviews were set up face to face at
the Miami University during the week 10/01-10/10. Semi-structured interviews helped in gaining
more detailed perspectives from the interviewees. The consent forms and non-disclosure
agreements were signed beforehand, as per the medium and form of the interview. If
interviewees gave consent to record the session, all the interviews were audio recorded. The
recorded interviews were transcribed. An introductory email was sent out to all the members of
Lean administrators to ensure the group availability during my on-site trip. Interview participants
are selected based on following criteria:
Criterion 1. Lean administrators (LA) constitute the fundamental stakeholder group
driving MU-Lean. Stakeholders have in-depth knowledge, metacognition, and leadership
attributes to discuss the detail for each of the phases of MU-Lean initiative; conceptualization,
launch, and sustainment.
Criterion 2. The Lean administrators have a comprehensive view of end-to-end MU-
Lean journey to be able to share the details on how it motivates Lean partners and other
stakeholders within the organization, keeping everyone engaged with the initiative. Stakeholders
should be able to answer the initial questions as well as focused follow up questions to provide a
more in-depth perspective on each of the questions.
Criterion 3. The use of semi-structured interview will be continued until the saturation
point is reached. To have in-depth discussions, surveyors should be able to secure one and half-
hour time slots with each of the interviewees.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 55
The overview of the data instruments and various inputs and their linkage to HEES
central delivery systems along with intended outcomes can be visualized as below:

Figure 10. Model for data instruments and various inputs and their linkage to HEES central
delivery systems along with intended outcomes.

Data Collection and Instrumentation
The objective of the survey was to understand the attitude, desire, appetite for change
management and infrastructure in terms of knowledge, organization and change management
towards the subject of blended quality management principles; Total Quality Management
(TQM), Lean, and Six Sigma (LSS) within HEI. The primary tenant of HEES application is to
create a customer-centric organization linking end-to-end process and systems to exceed
customer expectation without waste and fully engaging both internal and external stakeholders.
The secondary objective of the survey was to determine if there was a pre-existing bias from the
inquiry on the applicability of Blended Quality Systems in HEI. The purpose of the study was to
evaluate the hypothesis, “There is a poor attitude within Higher Education Institutions (HEI)
about HEES to drive system efficiency, student efficacy and culture of continuous improvement
(CI) in support of cost containment.”
The overall effect would be a streamlined process from enrollment to matriculation,
generating a value-oriented degree that places universities and their students in prominent
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 56
proximity to the workforce and industry needs. Table 12 depicts the sampling strategy and
associated timeline.
Table 12
Data Collection Summary
Sampling
Strategy (e.g.,
census,
purposeful
with max.
variation)
Number in
Stakeholder
population
(e.g., There are
a total of 50
teachers)
Number of
Proposed
participants
from
stakeholder
population

Start Date for
Data
Collection
End Date for
Data
Collection
Purposeful
with max.
variation
27 Lean
administrators
2500 –
Certified
Lean partners

Maximum
participation
rate
09/15 10/10
Interviews: 27 12 or
saturation
point

10/01 10/10
Surveys*: 27 Lean
administrator,
2500 certified
Lean partners
12 Lean
administrator,
250 certified
Lean partners
(expected
participation)
10/01 10/10

Surveys
The MU-Lean administration in charge of managing the Lean initiatives consisted of 27
individuals. There were over 2500 Lean certified partners. An online Qualtrics survey was sent
out to all of the partners aiming to get about 250 responses. The survey results helped benchmark
the results from Lean administrators and help measure the gap if any from the Lean leadership to
the frontline. The executive director of the MU-Lean was asked to help facilitate the initial
introductory meeting with the entire group to debrief the purpose of the study. The survey
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 57
questions had the assets forms for Knowledge (K), Motivation (M) and Organization (O). The
survey instrument was the same for both Lean administrators as well as certified-Lean associates.
As defined by Muijs (2010), the sample size had two elements determining the size of the
significance level of research:
1. The size of the relationship or difference found in the sample;
2. The sample sizes.
3. The survey instrument depicted in Appendix G highlights KMO attributes of each
question as well as its relationship to the three stages of the Mu-Lean initiative.
Interviews
The Clark and Estes (2008) gap analysis provides a systematic, analytical framework that
helps to understand organizational accomplishments. This analytical framework was utilized as
the conceptual framework within a promising practice study.
First, the interview instrument was used to understand how the journey of MU-Lean
started. Understanding the foundational trigger points was key to assessing the critical ingredient
and environment for a successful launch. Semi-structured interview format with the ability to ask
follow-up questions helped us gain a more in-depth perspective on the organization’s knowledge,
motivation and organizational assets and attributes. Also, since this stakeholder group was
responsible for conceptualizing, designing, launching and successfully managing it since 2012, it
was helped to understand the group’s issues and challenges within each of the three transitional
phases.
Data Analysis
The normal census sample size for the survey responses ranged from 250-350 based on
an anticipated 10% response rate. This dataset was allowed to perform various inferential as well
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 58
as descriptive statistics. For stakeholder groups of fewer than 20, the percentage of stakeholders
who strongly agreed or agreed was presented for those who strongly disagreed or disagreed. For
larger stakeholder groups, means and standards deviation were shown to identify average levels
of responses. The descriptive statistical analysis was conducted after the submission of all the
survey results.
For interviews and observations, data analysis began during data collection. Analytic
memos were written after each interview and each observation. Thoughts, concerns, and initial
conclusions were documented about the data about conceptual framework and research
questions. After leaving the field, interviews were transcribed and coded. In the first phase of
analysis, open coding was utilized, looking for empirical codes and applying a priori codes from
the conceptual framework. The second phase of the investigation focused on empirical and a
prior code aggregation into an analytic/axial code. In the third phase of data analysis, pattern
codes and themes were identified that emerged from the conceptual framework and study
questions. Documents and artifacts were analyzed for evidence consistent with the concepts in
the conceptual framework.
Credibility and Trustworthiness
The efficacy of the HEES model is dependent on both the credibility and trustworthiness
of the data. The interviews, surveys and review of datasets could help triangulate the results.
According to Merriam (2009), triangulation provides a credibility as well as stability on findings.
Both the interviews and surveys were voluntary and confidential. Cross-reference between Lean
partners and Lean administrators brought out the commonalities and differences.
The stakeholder group of Lean certified partners, managers, and administrators carries a
great deal of insight. The study aimed to understand the view of the population of 2,700 Lean
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 59
partners, with the goal to receive responses from at least 10% of the participants, the minimum
required sample size. To produce a statistical significance inference, study used the census
sampling method ensuring that it was random, represented various subgroup strata, consisting of
staff, faculty, auxiliary services, administrative managers, and senior leadership. An online
survey tool was used as it helped fetch the data into a table without any manual data entry, which
improved data reliability. The online survey was sent out two weeks in advance of site visits.
Ethics
In the entire data gathering and analysis process, informed consent was obtained, the
confidentiality of data and the anonymity of the surveyed participants was maintained and it was
ensured that their participation was. The data collected from interviews was coded to correspond
to the assumed assets identified using the Clark and Estes (2008) knowledge, motivation, and
organization factors.
To layout the permission for the promising practice, the president of the university was
contacted who is Lean certified and a champion of the MU-Lean initiative as well as a part of the
MU-Lean senior leadership team. The key stakeholder's group was very receptive to the idea of
us being able to examine their work and being able to evangelize the MU-Lean within HEIs.
As defined by Punch (1995), the researcher should be cognizant of issues that can arise in
codes of professional conduct. The participants in the study signed informed consent forms
beforehand. This form had standard data elements protecting the rights including some of the
attributes below (Satantakos, 2005):
• Identification of the researcher
• Identification of Sponsoring Institution
• Identification of purpose of the study
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 60
• Identification of benefits for participating
• Identification of the level of participant involvement
• Any risks for the participants
• Guarantee of confidentiality to the participants
• Assurance that the participants can withdraw at any time
Since there has been a predisposed affiliation with this subject as a faculty, consultant,
and a practitioner, one is cognizant of the underlined challenges faced by the HEIs. It was
ensured that the research methods guarded against the dismissal or acceptance of specific
hypothesis and allowed objective analysis to shed light on the issue, while ensuring the
qualitative research questionnaire was defined according to Schultz, 1988 guideline. The study
approached the research data measurement, analysis, subsequent inferences observing a strict
code of conduct and ethics.
Limitations and Delimitations
The objective of this study was to understand the best practice of blended quality
management; TQM, Lean and Six Sigma within HEI and understand how each of the elements of
the gap analysis helped MU-Lean. The survey on its own was limited by the perspective of Lean
partners and administrators within MU. Since the study was benchmarked as a promising
practice, participants providing information could be biased with their preconceived view. As
such, some of the real issues and challenges faced by MU-Lean’s three stages may not have
reflected in data.
The delimitation of this study is that other HEI institutions may be able to learn the best
practices cost containment initiatives and use it as a blueprint for their organization. If the HEES
can integrate the lesson learned from the three stages of successful MU-Lean, it would provide a
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 61
plan for other HEIs to use it as a plug-and-play instrument. An organization may also use the
Clark and Estes (2008) gap analysis model to identify the gaps within their organization and use
the tools from the study to establish the baseline.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 62
CHAPTER FOUR: DATA AND FINDINGS
This chapter is pivotal to the dissertation as it reports the data and presents the research’s
findings – the fruits of our labor, if you will. These findings, many of which were unexpected,
some of them eye–opening, emerged from a two–step process, detailed in Chapter 3. To
recapitulate, the first step was collecting responses through surveys and in–person interviews, to
five key inquiry questions:
1. What circumstances prompted the launch of the MU–Lean initiative?
• Understand the inflection point and all the contributing factors leading up to the
launch of HEES.
2. What support structure was provided in preparation for the launch?
• Understand the essential elements of the eco–system needed for a successful
launch.
3. What knowledge, motivation and organizational assets are essential for the launching the
MU–Lean initiative and how to sustain it over the years to deliver over $83 million in
savings?
• Validate KMO assets, attributes and assumed influences.
4. What are the foundational elements of organizational culture and context, stakeholder
knowledge and the motivation necessary to foster the philosophy of Lean transformation?
• Gain a more in–depth perspective on culture, leadership and the proclivity for
change management needed for a successful HEES implementation.
5. How can emerging technologies such as Artificial Intelligence (AI), Machine Learning
(ML), and Advance Data Analytics help deliver efficient and effective learning outcomes
for students?
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 63
• Conceptualize the HEES model keeping in view the technological enablers that
can help drive innovative ways to deliver learning outcomes.
The responses obtained through the inquiry questions were assigned the KMO
(knowledge, motivation and organizational) attributes to examine the underlying KMO barriers
and solutions. The inquiry questions also had fields for comments and feedback to understand
their larger perspective about the lean transformation journey.
The second step was to qualitatively analyze the responses using established statistical
and analytical procedures such as the Clark and Estes (2008) data analytic framework as the
foundational framework to evaluate the KMO barriers, the One–way Analysis of Variance
(ANOVA), and the Natural Language Processing (NLP) tool to understand the ‘sentimental
magnitude’ of the responses. In addition, the analysis utilized literature review, use cases, and
benchmark data to distill the key findings of the research.
Location and Participants
A diverse group of stakeholders including the leadership of Miami University of Ohio
were surveyed and interviewed. The stakeholder group that undertook the survey included Lean–
certified, mid–level associates, and frontline employees.
The approved survey and interview instruments were used for each of the stakeholder
groups to understand how the latter interacted and coordinated during the HEES journey at the
time of implementation of each of the three phases: Pre–launch, In–session launch and Post–
launch. During the interview researchers put up follow up questions such as, “So you just
mentioned this…” and “I want to ensure I understand you correctly…,” to ensure the accuracy of
data, validate whether answers were in alignment with the respective questions, and understand
the comprehension of respondents (Mariam, 2009).
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 64
To summarize the findings, other stakeholders outside Miami University were engaged,
who shared their perspective of how one can best utilize the findings of the study at the primary,
middle and high–school levels respectively and approach Lean transformation to enhance the
value proposition of the same.
The approach of a semi–structured interview proved beneficial as conversations provided
the details on how HEES, implemented at the institutional and enterprise levels, provides an
opportunity for significant transformational opportunities.
Research Question One
This question was designed to get an understanding of the inflection point where the
decision to implement MU–Lean was made, and the contributing factors leading up to the launch
of HEES. Following were the findings that emerged.
Cost–Containment Driven by the 2008 Recession and Subsequent Funding Cuts
The HEES journey demands a strong sense of urgency on part of the leadership to bring
about cost containment as an underlying constant. The motivational assets analysis of paired
sample t–test (Appendix M) and descriptive analysis (Appendix M) show that there was a
tremendous desire to undertake the phase one of the HEES at the Miami University (MU),
primarily driven by the 2008 recession and the subsequent funding cuts made by the State of
Ohio.
In addition, 74.7% agreed that MU–Lean has been beneficial to cost–containment
initiatives for the university as indicated in Appendix M. As one interviewee elaborated,
We have seen a consistent decline in State funding in over last 20 years and there is no hope for
this trend to change. We realized that our mindset to do nothing was no longer an option. You
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 65
have to think outside of the box and try things that other industries have implemented during
their time of crisis.
Another stated,
[Higher education] costs keep rising and budgets don't match inflation, so finding and
eliminating waste is key. That can be the difference between moving forward to the next
level and maintaining the status quo, [and save the institute from] ultimately falling
behind.
To understand if there is a need for HEIs to implement cost–containment initiatives (such
as MU–Lean), a one–way analysis was carried out. Participants who identified as Lean Certified
Associate strongly agreed that there was such a need, as shown in Appendix M. They have been
proven right. The year to date (YTD) savings of $83 million have helped MU reduce the fee
increases on students and family, showing clearly that MU–Lean transformation has been able to
effect change throughout the organization.
Receptiveness to New Ideas
A frequency analysis showed that there was more receptiveness at MU for new ideas that
could help reduce cost and improve student-learning outcomes (Appendix M).
The data stresses the importance of cultivating a culture of creative thinking, capturing
new ideas, and making the submission process easier. To quote a participant,
Make the process for submission of ideas not cumbersome or time–consuming.
Sometimes it takes [one] more time and energy to submit an idea [including cost–
analyses, deliverables, etc.] than it does to make the change. Do not just pick one or two
departments on the campus to do Lean; all should be equally responsible for being
involved with it.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 66
Analysis revealed that the majority of the participants agreed that there was receptiveness
for new ideas as well as a willingness to find ways to address funding and the student debt issue.
Data from surveys and interviews also suggested a need for an incentive–based system to reward
new ideas for motivation. “Finding ways to reward folks for ideas rather than creating quotas for
ideas per quarter could prove effective in both generating ideas as well as boosting morale,” said
a participant.
Receptiveness to Change Management
The results indicated that the receptiveness for change management (M = 2.18, SD =
0.70) was significantly greater than the receptiveness for simply maintaining the status quo (M =
2.04, SD= 0.69), t(301) = –5.02, p = .000 (Appendix M).
A frequency analysis also revealed that the majority of the participants agreed that there
was receptiveness for change management (Appendix M). Participants stated, “Foster a culture
which is open to change and invest heavily in change management.”
To understand if Miami University has a strong appetite for new ideas of driving HEES
and change management within the different stakeholder groups, a one–way analysis was done to
evaluate which roles agreed more. Participants who fell into the category of “Lean Certified
Associate” strongly agreed.
On the test conducted to evaluate which roles agreed more on the statement “there is
receptiveness for change management.” The ANOVA was non–significant, F(3, 299) = 1.85, p =
.138 as shown in Appendix M. “Lean Certified Associate” category strongly agreed on the
statement. This finding was significant as this group represented hourly and/or frontline associate
in charge of executing projects within lean initiative. For this group to embrace and believe that
the new processes and changes are essential was a key success factor behind Miami University’s
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 67
ability to not only launch successfully, but also sustain the drive and momentum for a longer
period of time with the Lean initiative.
Engagement of All Stakeholders
One of the key findings of the research analysis was that engagement of all key
stakeholders is critical from the start (Appendix M), that organizations must find ways to engage
both the students and the faculty at the time of the launch of Lean initiatives.
Committed Leadership
There was a positive correlation of this inquiry to the leadership’s desire and commitment
to drive Lean transformation as a strategic initiative. A frequency analysis revealed that 62.8% of
survey participants agreed that the top leadership valued MU–Lean initiatives, as shown in
Appendix M. Lean experts suggested that, “The top leadership and the faculty have to be 100%
in, or meaningful changes will not happen. Lots of small projects and initiatives [that will add
up] may take place but not truly transformational change.”
Summary of Findings of Research Question One
Between the descriptive, inferential and benchmarking analyses, there seemed to be an
inflection point within HEI, generated by prevailing micro and macro trends. The 2008 recession
was the macro–economic event that engendered the seismic shift within the HEI business model.
Miami University’s top leadership felt that public universities in the State of Ohio had
continued to see a decline in State funding as cited by a project leader,
From 1980 to 2018, the State allocation for public education has gone from the upper
70% down to a single digit figure. This decline in resource funding along with an
overcapacity of education institutes in Ohio have resulted in a crisis. This was one of the
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 68
major reasons why the leadership at Miami University post–2008 financial crisis opted to
think outside the box and launched MU–Lean.
Historically, similar cost–containment trends could be found in automobile and financial
services sectors, government organizations, and the healthcare sector. However, any such
appetite for enterprise–wide transformation has not been seen at the HEIs in the US or other
countries.
It is interesting to see that student debt did not come up as a major contributing factor of
the launch of the Lean initiative at Miami University of Ohio. Rather it was the budget shortfall
that necessitated the MU–Lean launch.
The research survey, interviews, literature reviews and data analysis show that the need
for this level of transformation must come from the top. Executives, trustees and governing
boards all have to be inspired in having a sense of urgency to launch and drive the HEES
transformation. Many participants in the study emphasized the need to have a top leadership
support.
Start with the top leadership and educate and manage down instead of managing and
educating from entry level up.
Gain buy–in from the top leadership, or at a minimum, secure a champion in top
leadership who will provide resources and remove barriers.
There needs to be a commitment from the top leadership to provide resources to
plan and implement the Lean initiative. This should include a budget to hire, at least for
part–time engagement, an experienced Lean professional to lead the initiative as well as a
budget for training.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 69
Research Question Two
The second research question was designed to address not only the leadership’s vision to
drive change and introduce MU–Lean, but also to understand the essential elements of the eco–
system needed for a successful launch.
Training, Recognition and Reward Systems
Among the 429 participants surveyed at Miami University, there was a significant
positive relationship between trained MU–Lean respondents and the reward structure for their
efforts (r = .67, p= .000). People certified and trained in MU–Lean were rewarded for their work
on MU–Lean projects. Participants echoed this finding in the interviews.
Training is key and educational efforts are essential to prepare staff with the toolsets they
need to be successful.
The more involved and educated [you are] in Lean processes and equipped with
the tools to track progress and metrics, the better will be your understanding and
participation in projects.
Gain alignment in the scope and scale of a lean initiative on the front end. Invest
in training with senior leaders of the institution, with the goals of enlisting support and
creating ambassadors for the program.
Survey participants stated that the investment in training is imperative: “Regular lean training
should be offered to all employees and when implementing lean at an institution of higher
education, adapting the process to fit into how higher education operates”
They strongly indicated that it is not helpful to “present lean as it is implemented in the
manufacturing environment and ask the participants to make the connections.”
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 70
If you are serious about wanting to adapt and implement lean principles in higher
education, which can be helpful if implemented correctly, then take the time to write the program
and modify the training materials to fit your organization. Do not get a presenter from industry
or manufacturing to present Six Sigma to your employees.
The correlation showed that the launch not only provided a proper training and a support
structure, but also rewarded participants with a proper reward and recognition. The
benchmarking and literature analysis also showed a strong correlation between the associates’
efforts and the appropriate reward and recognition mechanisms that were put in place. It is
evident that the success of MU–Lean in generating $83 million savings was brought about
through proper training, an investment in cost–containment training, and recognition and reward
systems (Appendix M).
A failure analysis of historical use case datasets made it evident that Lean transformation
projects cannot succeed without if an institution does not provide the necessary training,
certification, and the empowerment to participants to execute Lean transformation projects.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 71

Figure 11. Survey Participants Perceptions being rewarded for efforts for working on MU–Lean
projects.

Survey participants advised that the institute could “consider a program to reward all
participants of a lean project.” For instance,
A person that submits a Lean idea, that becomes a successful project, may not get any
credit or recognition if that person is not actively involved as a member of the program.
Often the project leader and participants are recognized, but the person that initiated the
idea is not. The person that submits the idea should be a member of the project as it
moves forward.
Survey participants also recommended that universities financially reward individuals
who submit ideas that save the University money:
If a reward system is not put in place, then participants and leaders don’t feel the value of
their work and this will start to reflect in their efforts. Furthermore, there needs to be an
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 72
ongoing communication across all levels so that students, faculty, staff are aware of the
benefits and rewards to institution.
You need a two–way communication for approving the projects. Have a plan
closely tied to academic success and involve faculty and students. Have a strong rewards
program tied to this.
Another frequency analysis revealed that the majority of the participants were rewarded
for their efforts for working on MU–Lean projects.

Figure 12. Survey Participants Perceptions being recognized for efforts for leading on MU–Lean
projects by the number of years of Six Sigma experience before MU.

Furthermore, Lean experts suggested that institutions, “reward employees with generous
pay increases and/or a percent of the cost savings initiative.”
Empowerment and support from leadership
The analysis revealed that there was a significant positive relationship between
respondents having full support from their managers to work on MU–Lean projects and
respondents feeling confident in their ability to initiate such projects (r = .70, p= .001). A one–
way analysis evaluated if the level of confidence in initiating MU–Lean projects differed
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 73
depending on the role of the participant. The ANOVA was significant, F(3, 302) = 9.25, p <
.001.
Analysis further evaluated if the level of confidence in executing MU–Lean projects
changed depending on the role. The ANOVA result was significant, F(3, 302) = 10.9, p < .001,
which showed that the role certainly mattered. In terms of the level of confidence, Lean–certified
associates were shown to the most confident of the lot in starting MU–Lean projects (Appendix
M).
This analysis further provided the evidence that the success of MU–Lean permeated
beyond the leadership to get the needed buy–in from frontline associates, and was further
strengthened through training and support.
Experience and Expertise
The confidence of being able to initiate an HEES phase 1 project had a strong positive
correlation (r = 0.86) with the numbers of years of experience in the quality management field.
They further validated that MU–Lean was cognizant towards investing in resource acquisitions,
expertise and training to build the necessary knowledge base. As a senior lean administrator
stated, “We had to find ways to bring outside expertise to help us understand what the
prerequisite of launching the lean transformation would be.” Another Lean administrator said,
We made a conscious decision to bring in experts and create an organization simply
focusing on the Lean initiative that certainly helped us become very successful. Those
organizations helped certify hundreds of employees have you taken over and continue to
deliver lower sixty million dollar in savings.
Similarly, a one–way ANOVA done to identify if the participants’ ability to sustain MU–
Lean projects differed depending on the number of years of Lean experience prior to Miami
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 74
University. The result was significant, F(4, 301) = 3.55, p = .008, indicating that participants
who had 11 and more years of experience in Lean were more confident in being able to sustain
MU–Lean projects (Appendix M).
A one–way ANOVA test was done to understand if the confidence in initiating MU–Lean
projects depended on the number of years of Six Sigma experience prior to Miami University.
The ANOVA result was significant, F (4, 301) = 2.99, p = .019 as shown in Appendix M.
Individuals who had no prior experience but were certified in LSS were more confident.
While the number of years of experience mattered in identifying the level of confidence
in initiating MU–Lean projects, survey participants expressed the importance of having “support
from senior leadership and to make sure that the employees are empowered to make
suggestions.” Also, “a culture and understanding of Lean must be developed for continuous
improvement to be successful. This cultural shift needs to begin with the top leadership and
percolate down.”
A one–way ANOVA test was conducted to understand which certified MU–Lean
participants had the necessary tools to work on their process improvement initiatives had a
significant outcome. The result suggested that Lean–certified associates felt they had all the
necessary tools to work on their process improvement initiatives as indicated in Appendix M.
Furthermore, encouragement for those at the “director level and above to engage with
lean in at least a minimal level would be ideal.”
Overall, 71.3% also agreed that they have all the necessary tools to work on their process
improvement initiatives (Appendix M).
It would be easy for Lean to be the flavor of the month and then be abandoned as not
practical or effective. Steps must be taken at the highest levels to continually set
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 75
expectations and provide support for initiatives. It needs to be continually supported at
the highest level of management; all employees need to be made felt that their ideas and
participation is valued.
Lean participants emphasized that
An organization should assign a senior–level Lean champion and management support at
all levels to those who value the program and encourage their teams to think Lean so they
feel supported to be able to share ideas and solutions. It needs to be continually provided
at the highest level of management, all employees need to be made felt that their ideas
and participation are valued.
You must have support from senior leadership, that's the only way to really see a
culture shift.
Among all Lean–certified participants, 63.2% agreed that they had full support from their
manager to work on MU–Lean projects as shown in (Appendix M).
An additional one–way variance was done to identify which roles had the most support
from their managers. The test showed that entry–level Lean participants had the most support
from their managers (Appendix M).
Summary of findings of Research Question 2
This research question honed in on a critical element for Lean transformation initiatives.
There was a significant correlation between various participant groups in their view of
understanding how much of support in terms of training and tools was provided in preparation
for the launch. Research showed that Miami University leadership was cognizant of not having
Lean’s core competencies within its management team.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 76
The research further highlighted ambiguity and provided a lesson for other institutions
during their pre–launch initiative phase. The results showed that sometimes the management
tends to ignore the resource requirement of the certified group because the former assumes that
the group knows what to do and where to look for the resources. However, the analysis shows
the opposite. It revealed a significant discontentment within the certified group if their
management team did not support them. This example emphasizes that each stakeholder group
within an organization is identified clearly and the training program is designed and curated to
meet the individual needs for resources as well as recognition.
Research Question Three
Survey and interview instruments were designed to ask specific questions to understand
essential attributes to launch, sustain it and make it a part of the institution’s DNA during the
post–launch phase.
Knowledge Assets
Interview participants advised that an HEI should make its
Lean team diverse, in that you include someone who actually works in the areas that will
be implementing the new lean initiatives. There is nothing worse than someone
brainstorming and coming up with new policies and procedures when they actually have
no experience in that area and expect people to happily enforce something that they had
no real working knowledge of. Keep people informed, give everyone the opportunity for
their input and let them know their input is appreciated.
The number of years of TQM experience also had an impact on the participants’ ability to
sustain (Appendix M) and execute MU–Lean projects. Participants who had 0–2 years of
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 77
experience were better prepared when they had Lean certification, validating the importance of
certification (Appendix M).
In order to identify if participants’ ability to initiate (Appendix M), execute (Appendix
M) and sustain (Appendix M) MU–Lean projects depended on the number of years of Lean
experience, one–way tests were conducted (Appendix M). Results showed that participants
without any experience were the most prepared of the lot, Lean employees with 11 and more
years of experience were more confident in sustaining initiatives, and respondents with 0–2 years
were better at executing projects.
To find out if Lean–certified respondents’ ability to initiate, execute (Appendix M) and
sustain (Appendix M) Lean projects depended on the number of years of Six–Sigma experience,
a one–way analysis was carried out (Appendix M). Outputs showed that participants who had no
experience were more confident in all three categories.

Figure 13. Survey Participants Perceptions towards how important it is for MU–Lean to
continue to deliver the cost savings for the next 10 years as a part of the cost–containment
initiative.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 78

Lean certified employees and (M = 1.90, SD = 0.70) and Lean Partners (M = 1.88, SD =
0.71) also admitted that it is vital for MU–Lean to continue to deliver the cost savings for the
next 10 years as a part of the cost–containment initiative. Similarly, a majority of the Lean
Partners and bottom–line Lean employees believed that MU–Lean had been beneficial to cost–
containment initiatives of the university (Appendix M).
Motivational Assets
To evaluate motivational assets essential to the launch and sustainment of MU–Lean
initiatives, various frequency tests were conducted. Most Lean experts admitted that they were
recognized for their efforts for working (Appendix M), leading (Appendix M) and managing
(Appendix M) Lean projects.
In a similar manner, frequency outputs also revealed that the majority of Lean–certified
individuals stated they were rewarded for their efforts for working (Appendix M), leading
(Appendix M) and managing (Appendix M) MU–Lean initiatives.
Organizational Assets
As defined in literature review (Appendix F), there is a strong evidence of the failure of
initiative’s launch due to poor planning during the pre–launch phase. There are numerous
examples and citations to support a strong pre–launch phase planning and the need to make
investments in the right infrastructure to ensure the successful launch of initiatives. The use of
literature and use cases was utilized to gain a deeper perspective of the Lean transformation
journey in each of these industries and its relevant applicability to our education institutes.
Although it is obvious that higher education and education in general is more unique than any of
the business domains the research study examined. Survey participants also suggested that “the
faculty must be first brought on board.” As one participant stated, “The faculty members I speak
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 79
with feel they should not have to worry about money, [even though] their departments are all
underfunded. They pass this information onto the students, and the students believe what they are
told.” Also. “In addition, the top leadership and the faculty have to be 100% in or meaningful
changes will not happen. Lots of small projects and initiatives (that will add up) may take place
but not truly transformational change.”

Figure 14. Survey Participants Perceptions towards MU–Lean has been able to collaborate with
faculties.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 80

Figure 15. Survey Participants Perceptions towards MU–Lean has been able to collaborate with
various departments.

Teamwork and continuous collaboration were important to sustaining MU–Lean
initiatives. Analysis revealed that the majority of the participants agreed that MU–Lean has been
able to collaborate with faculties (47.5%). If a university wants to implement Lean initiatives,
survey participants suggest, “it would be helpful to include all relevant departments and staff, as
well as communicate with those in the facilities, what you are attempting to achieve.”
Frequency tests also revealed that the 63% believed that MU–Lean has been helped them
to collaborate with various other departments. Universities need to find better ways to
communicate with other departments throughout the departments. One participant stated,
Other departments [reach me via] email, either to use up a product or to find a product,
and I'm not always at my desk. I sometimes don't have any time during the day and only
check my email once a day while at work.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 81
One survey respondent claimed that Lean initiatives, “do not seem to fit as well with the
administrative departments as it does with things like physical facilities, dining halls, housing
and recreation centers…I think they should look at how they calculate their metrics. Also, be
aware that some projects may show cost reduction for their department when they have just
distributed work (and cost) to other departments.”
Survey participants also suggest that the implementers,
make best efforts to include and consult with the people who will be affected (i.e.
working directly) by the new system(s) before implementing them. I feel that not all of
the right questions are asked when Lean committees look into a situation. They are
discovered after it's too late to go back.”
Also, consulting with workers will help to keep the management from
implementing initiatives that result in more work. Make the process to submit ideas not
cumbersome or time–consuming. Sometimes it takes more time and energy to submit an
idea (including cost–analyses, deliverables, etc.) than it does to make the change. Don't
just pick on one or two departments on campus to do lean, all should be equally
responsible to be involved with it.”
Furthermore, interviewees strongly advised one, “not to try and start lean without extensive IT
support and commitment.” In addition, it is vital for implementers to “make sure [that] the
projects are actually requested prior to commencement.”
Frequency tests conducted showed that Lean–certified participants agreed that students,
faculty and top leadership see improvement in learning outcomes through MU–Lean initiatives
as shown in Table 52, Table 53 and Table 54 (see Appendix M).

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 82
Summary of Findings of Research Question Three
There were clear indications that reward, management acknowledgment, investments in
training, and having an ecosystem in place were crucial to the launch and sustainment of Lean
transformation initiative. Fewer than half of the participants felt strongly about the type of
recognition and works associated with the engagement in a Lean transformation project. The data
analysis had a strong correlation with the overall engagement with various stakeholder groups
within the university and the organization’s ability to successfully integrate Lean into the cultural
DNA.
The administrative staff felt somewhat marginalized due to their inability to work on
projects while still being called to provide the recommendation for improvement. Historically,
the motivational– and organizational–asset influences caused Lean transformation to collapse.
Research Question Four
This research question was designed to understand the underlying forces and factors that
are necessary for the attainment of efficiency through HEES. The literature review strongly
indicated that there is a lack of interest for innovation within higher education institutes. Lean is
viewed as a concept that is good for the business world, especially for manufacturing, and is not
found to be useful in the educational space. Various tests were conducted to understand the
foundational elements of organizational culture and context, stakeholder knowledge and the
motivations necessary to foster the Lean philosophy.
Communication
A one–way analysis conducted found communication to be the most relevant part of
MU–Lean (M = 4.34, SD = 0.98) as shown in Appendix M. The mean value was significantly
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 83
different from the midpoint value of 3.0, t(290) = 23.6, p = .001. As one survey participant
stated,
I feel communication is one of the most important factors when implementing a lean
project. My understanding of lean should have a more transparent communication and
more regular checkpoints. An annual assessment process or the like could be beneficial.
It seems our initiatives are approved, implemented and then never revisited. I think this
is a major detriment to how we have implemented this protocol.
Emerging Technologies
A similar test was carried out to understand what role the emerging technologies played
in driving excellence at HEI. The outcome showed that Lean–certified employees in general
found Predictive and Descriptive Data Analytics as the most important type of emerging
technology for the future higher education (M = 4.49, SD = 1.01) as illustrated in Appendix M.
Awareness of Lean principles
There were various parts of MU–Lean that different stakeholders find to be most relevant
and important. To gain a clearer picture of which principles each participant found to be the most
vital, numerous one–way analysis tests were conducted. Among the first attributes were Lean
Principles, the outcome was significant, F(3, 288) = 4.20, p = .006. Lean Senior Administrators
(M = 4.15, SD = 1.07) found Lean Principles to be the most relevant and important part of MU–
Lean as indicated in Appendix M.
Survey and Interview participants stated, “It would be best to look at the Lean principles
and adjust them for every individual project, using a one–size–fits–all approach of doing every
step every time was a waste of time and energy for my department.”
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 84
There were learning outcomes but a good leader could have focused us more on the
outcomes and not on each time–consuming step. We need to trust the process. An intense
effort for all staff to be educated in Lean principles and terminology before starting any
implementation. You must also develop a culture of Lean and an understanding of
continuous improvement to be successful.
Motivation Sets Differ With Roles
Lean Senior Administrators found Internal Camaraderie (Appendix M), Team Building
(Appendix M), Clearly Defined Project Charter (Appendix M) Drive cost–containment efforts
(Appendix M) and Project Management (Appendix M) to be the most vital part.
As stated by one the interviewees, “The teams need to understand the ROI processes and
financial spreadsheets. Keep all staff in the know every step of the way.” In addition, it is
necessary to make lean teams diverse and inclusive drawing participants from various part of the
organization as echoed by over 35% of the participants, “Needs someone who actually works in
the areas that will be implementing the new lean initiatives.”
There is nothing worse than someone brainstorming and coming up with new policies
and procedures when they actually have no experience in that area and expect people to
happily enforce something that they had no real working knowledge of.
Keeping people informed gives everyone the opportunity to provide their input
and let them know their input is appreciated.
Survey participants also encouraged the leaders, “to be clear about the goals of the lean effort to
the front–line staff” and “Defining Lean can be tricky to an employment base that equates a lean
initiative with potential jobs reduction. Have a clear understanding of the goal and make goals
measurable.”
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 85
Lean partners found Training and Coaching (Appendix M), Six Sigma Principles
(Appendix M), Impact on Learning Outcome (Appendix M), Management Support and
Collaboration (Appendix M) and Recognition and Reward (Appendix M) to be the most
important parts of MU–Lean. Lean–certified participants recommended universities to
Have group work to understand and implement the processes in training before leading a
project. The training gets all employees on the same page, moving forward working
together as a team. Offer training regardless of class size to get as many people trained as
possible. Use the Lean ‘belt’ system for recognition of staff members. Have an online
system for tracking projects and training classes. Training is key, educational efforts to
prepare staff with the toolset they need to be successful.
On the other hand, Lean Managers (M = 4.52, SD = 0.85) found Communication to be
most vital attribute when executing MU–Lean projects as shown in Appendix M.
In order to analyze how important lean participants thought emerging technologies are for the
future of higher education excellence, one–way tests were carried out. The results indicated that
out of all the stakeholders, Lean Managers found all four technologies; Machine Learning
(Appendix M), Predictive and Descriptive Data Analytics (Appendix M), Artificial Intelligence
(Appendix M) and Robotic Process Automation (Appendix M) to be the most vital.
An analysis of organizational– and motivational–assets also indicated that most Lean–
certified individuals agreed that MU–Lean has provided ways to collaborate with faculties
(Appendix M), various departments (Appendix M) and administrators, as shown in Appendix M.
Summary of findings of Research Question Four
The most important findings for research question 4 resulted in a strong reliability
analysis along with an effective and reliable Cronbach’s Alpha. The 13–question instrument (See
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 86
Appendix A) was designed to understand how organizations could take a quick inventory to plot
their organizational readiness index in relation to its ability to adopt and launch a Lean
transformation initiative. The finding highlighted that the senior administration was very keen to
use emerging technologies such as artificial intelligence, machine learning, advanced data
analytics, and robotic process automation. The use of these technologies can help reduce waste,
improve student learning and help curate essential resources for students. It is important to know
that the Higher Education Optimization System (HEES) can provide an incredible advance
operation solutions and innovative outlook for universities.
Research Question Five
The last research question was designed to understand the implications of deploying
emerging technologies such as artificial intelligence, machine learning and advanced data
analytics capabilities on the future of the HEIs.
The data–based decision–making capabilities of these emerging technologies can power
the implementation of HEES at HEIs to help improve learning outcomes, student experience and
provide meaningful job search opportunities. Enhanced algorithmic capabilities can help curate
resources pro–actively and most efficiently for each student, all without creating a huge
overhead. The research question analyzed the feasibility question: Can the HEES model make
higher education institutions sustainable for future generations, while delivering a cost–effective,
impactful and positive return on investment (ROI) for students?
Predictive and Descriptive Data Analytics Are Critical
In order to understand how emerging technologies such as AI, ML, RPA, and advance
data analytics can help deliver efficient and effective learning outcomes for students, numerous
tests were conducted. Frequency analysis was done to evaluate where stakeholders had the most
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 87
experience amongst the four technologies. The results indicated that participants selected the
category “None” and “Predictive and Descriptive Data Analytics” as shown in Appendix M. A
one–sample t test evaluated that lean participants in general found Predictive and Descriptive
Data Analytics as the most important type of emerging technology for the future of driving
higher education excellence (M = 4.49, SD = 1.01).
According to a senior project leader,
We have so many relevant and important datasets available. Unfortunately, they are
fragmented and not readily available. We tend to rely on the old–school reporting
structure of Excel and systems–generated reports rather than the ability to use the
intelligence within data sets. I believe that if we can find ways to use important data to
help us make critical decisions to help the students, it would immensely help the
organization to improve the matriculation outcomes.
In fact, the conversation went in–depth into how the current capabilities of data analytics
can provide HEI with new capabilities:
“It would revolutionize the way we run the university. We have the predictive data
capabilities, we can tailor the resources and improve their experience, learning outcomes
as well as focus on reducing the cost. We can look at their keycard data to predict the
lifestyle–related trends and provide resources for physical and mental health in a
proactive but non–intrusive manner. I see the need for the model to help manage the role
of the university in a much different way than [it was done for] the last 150 years.”
Summary of findings of Research Question Five
Survey results and tests strongly indicated that data analytics capabilities will play a
significant role in the implementation of HEES hereon after. As one of the leaders mentioned,
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 88
I wished we had an opportunity to integrate the data analytics capabilities to help us
improve learning outcomes, understand student behaviors and its impact on their ability
to achieve greater results as well as understand how we can curate the resources within
the university to enhance their learning experience. I wished we had a core competency
to be able to integrate emerging technology into our lean initiative. I believe this is where
the Holy Grail of future higher education is.
There is no need to pursue the traditional Lean initiative to succeed. It can be modified and
integrated with new technologies to be customized for HEI: “It is imperative to develop a culture
where new ideas and continuous improvement is expected, not just suggested.”
Stratification analysis of KMO Attributes
During the previous part of the analysis, the focus was on understating the data from
three different instruments; survey, interview and literature review. Through triangulation, each
of the research questions was answered. Further analysis within the research questions were
designed to summarize the knowledge, motivation and organization assumed influences
impacted during each of the phase of transformation; pre–launch, during launch and post–launch.
Clark and Estes (2008) recommended that for optimal performance, an organization
should align its processes, procedure, and systems to the goals. In Lean management, this
concept is known as a “value analysis,” meaning if the task or step performed does not enhance
the output for the customer, then that step or task is a non–value add and considered as a waste
(Francis, E.D. 2014). According to various quality management journals, a typical organization
generates over 40% waste on average (Weckenmann, 2015). If higher education institutions were
to pursue cost–containment as their core objective, the resource, process and system alignment is
essential. MU–Lean administration has a structure in place to have precise alignment on how
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 89
they have certified the MU–Lean staff at the process level, and provide support by way of
resources, tools and the necessary empowerment for decision–making.
The ability of the administration to impart training and harness the necessary skills
proved to be the biggest knowledge assets of the initiative. Mechanisms put in place to
recognize and reward successful participation and empowerment and for change management
were the strongest motivation assets. The strongest organizational assets were the investments
that were made into building the foundation and acquiring the resources for the commencement
and sustainment of the initiative. Fostering a culture that promotes communication was another
strong organizational asset.
Summary
Research analysis showed that for the success of the HEES initiative, the most important
dimension is leadership’s commitment and a keen sense of urgency to drive cost containment as
an important part of their strategic value and vision framework. Once the commitment at top
leadership has been established, data suggested that finding ways to bring the outside expert to
help establish initial HEES system foundations is necessary. The foundational eco–system setup
step is critical, and institutions should not take a shortcut or avoid to save time and money.
The second most important finding post infrastructure/eco–system establishment should
come from setting up layered training programs at all levels of organization. The establishment
of HEES hierarchal organization chart can help drive the three stages of the HEES
transformation journey. Establishment of Lean Enterprise System (LES) can help establish
project initiation, tracking resources, associated benefits, and progress as well as project
management. Being able to quantify the progress, communicating success and integrating
rewards was key in sustaining the program. In addition, establishing a knowledge base with
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 90
various certification levels; white, yellow, green, black and master black belt help solidify the
momentum and required knowledge base. This ensures that the organization is self–sustained in
launching and sustaining the HEES based practices. The research analysis was also able to
validate key knowledge, motivation and organizational attribute assets along with discovering
new assets to enhance the future research studies. This study’s contributions also include a
survey instrument with high Cronbach alpha of 0.92, which can be utilized as a survey
instrument to understand organization’s baseline readiness. The analysis was able to find a
strong correlation between key stakeholder groups and their perception of lean transformation. In
each of these stakeholder subgroups, the participants had meaningful means with significant
variants on their attitude, perception, and likelihood of continuing focus and engagement
emphasizing that HEES should be viewed as multi–dimensional initiatives and bringing all of the
stakeholder groups together with effective communication, motivation and support is key to
success.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 91
CHAPTER FIVE: RECOMMENDATIONS AND IMPLEMENTATION
The purpose of this chapter is to provide a comprehensive list of recommendations to
help Higher Education Institutes (HEI) to implement HEES. The proposed launch of HEES
follows a phased approach to mitigate the risk of failure.
The first phase focuses on the Lean initiative. The second phase focusing on the
application of Six Sigma. Lastly the third phase is about taking advantage of the emerging
technologies such as AI, ML, RPA (Robotics Process Automation) and Data Analytics. The
integrative use of each of the phases within HEES can help deliver improved learning outcomes
achieved through proactive engagement with stakeholders and an efficient curation of resources.
It is recommended that the organizations follow a three–phased approach with sequential
milestones as illustrated in Table 13. This process allows for a gradual launch and the adaptation
of HEES across the organization.
Table 13
The HEES Implementation Plan
Phase Description Sequence
Milestone
Timeline (Months)
1 Lean Initiative Pre–launch
In–session
Post–launch
0–12

12–24
24+
2 Six Sigma Initiative Pre–launch
In–session
Post–launch
0–16
6–18
18+
3 Advance Analytics
& AI, MI and RPA
Pre–launch
Post–launch
0–12
12–24+
–––––––End–to–End HEES Integration––––––

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 92
The research study, detailed in previous chapters, has shown a high risk of failure for the
organizations that do not take a phased approach and possess a mindset of “getting it done or
getting there faster”. The methodical process of the recommended sequence from
conceptualization, pre–launch planning, setting discipline through in–session, to the post–launch
phase can help HEIs undertake a graceful journey to HEES implementation.
The three phase recommendation steps for HEES are designed to help build the necessary
foundation and the eco–system by creating an essential inventory of knowledge, motivational
and organizational assets. Leadership should view the HEES initiative as a way of instituting a
new culture and using this opportunity to change the organization’s DNA. It helps organizations
change the culture and resetting of an operating system by creating data–driven, efficiency laden
HEES organization rather than making it as an event or project. The HEES implementation
process is divided into three phases:
Phase 1: Launch of HEES with basic Lean/TQM transformation
Phase 2: Launch of HEES with advance Six Sigma transformation
Phase 3: Launch of HEES with Advance Data Analytics, AI, and ML, fully
integrated with phase 1 & 2.
The phase–based approach is easy to prepare, plan, execute and designed to help the
HEES leadership drive associated change management. Each phase is further sub–segmented
into three distinct sequences as illustrated in Table 87.
§ Sequence 1: Pre–launch
§ Sequence 2: In–session launch
§ Sequence 3: Post–launch

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 93
Phase 1: Launch of HEES with Lean/TQM transformation
It is recommended that the organization should first start with a simple, straightforward
process of adoption of Lean. Based on the literature review, benchmarking, interviews and
survey, it is recommended that a minimum of 12 months should be allocated for the pre–launch
sequence. In some cases, based on the organization’s prevailing state of system capabilities,
establishing a strong foundation for HEES can even take up to 18 to 24 months. A rush to launch
the initiative without undertaking a pre–launch system check sequence can have a detrimental
impact on both the short–term and the long–term outcome of HEES as it could adversely affect
the morale of the stakeholders and workplace productivity. Based on the feedback from key
stakeholders, building the foundation and the proper infrastructure for the Lean initiative allows
phases 2 and 3 to be successful.
The Pre–launch Sequence (0–12 months)
Step 1: Perform feasibility and organizational readiness assessment. Establish the
“as–is” state of operation by creating a baseline assessment of the organization’s key
performance indexes. Provide survey instruments that measure the knowledge, motivation, and
organization capabilities. It is important to have a clear inventory of an organization’s existing
state of the operating system and process capabilities as well as limitations before initiating the
HEES implementation plan. In order to accomplish this, a sample survey instrument template,
designed to help take inventory of the baseline assets, has been provided (see Appendix A).
Along with the baseline assessment, the organization should go through a “5–Whys”
exercise as shown in Appendix E for top 20 critical initiatives. These initiatives typically come
from a value mapping of critical challenges faced by organizations. For example, if an
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 94
organization is facing an issue of an unacceptable matriculation rate, it can take a deeper dive to
understand the underlying reasons.
Step 2: Establishing HEES organizational structure. As illustrated in Figure 16 the
structure consists of the senior leadership, key stakeholders, subject matter experts, (SME) as
well as a student council.

Figure 16. HEES Organizational Chart.
The organization should create some of the key roles and responsibility to help support
the organizational project team including Lean Partner, Lean Manager/Lean Senior
Administrator and Lean–Certified Associates.
Step 3: Project Identification and Selection Process. One of the most important steps
of a Lean and Six Sigma project is choosing the project itself. As illustrated in Figure 17, the
focus and the goal of various project types can be stratified for phase 1 and phase 2. The project
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 95
chosen must be both meaningful and manageable. Meaningful projects should have a direct
business impact, such as creating efficiencies, reducing cost, or increasing quality.

Figure 17. Focus and Goal of Various Project Types.
One way to improve the deployment of Lean is to improve how Lean projects are
identified and selected. Oftentimes, Lean project identification and selection are heavy on
selection techniques but light on identification techniques.
Project opportunities can be found by looking at business strategy, goals, or the direction
communicated by the senior leadership. Also, it is possible to identify a project through the
customer feedback in the form of complaints, praise, or requests. These are often referred to in
Lean as the “Voice of the Customer” (VOC). Nonetheless, other projects can arise from
problems identified in a process (Voice of the Process), from employee complaints and
frustrations (Voice of the Employee) or through current metrics and systems that highlight a
problem.
Define, Investigage, Streamline and Control (DISC). Cycle time, resource management
and waste reduction challenges typically demand a purely Lean approach. DISC is a Lean project
that utilizes Lean thinking and is usually applied when cycle time and waste reduction are critical
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 96
objectives. Define, Investigate, Streamline, and Control constitute the four phases of a DISC
project. The following table describes each aspect of a DISC project.
Table 14
Aspects of DISC Project

Kaizen—FastTrack. “Quick Win” or “FastTrack” projects aim to achieve solutions to
known problems and attain faster results when risks are manageable. Oftentimes, a Kaizent, the
Japanese word for “continuous improvement”—is used to identify this business philosophy or
system that makes gradual, unending improvements by setting and achieving increasingly higher
standards.
Typically, implementation of Kaizen begins with a week–long meeting that includes
decision makers, key stakeholders, and employees knowledgeable of the process and the issue. It
is a focused, intense, collective approach and when carried out effectively it can improve a
process or operation very quickly, generate enthusiasm and energy, and support a culture of
innovation. Prior to holding a Kaizen meeting, the project team and team leader will have clearly
defined the project, the problem or opportunity, and will have collected all the data to identify
and quantify the problem or opportunity. During the Kaizen event, the team will utilize phase 1
and phase 2 tools to analyze the data and recommend improvements.
Phase Action
1. Define Describe the process, opportunity, and goal
2. Investigate Examine the process & available data
3. Streamline Identify solutions, implement, and verify results
4. Control Establish monitoring to maintain the gain
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 97
Step 4: Create Training Programs. Create a training plan to focus on the learning from
gap analysis, skill–set–attribute analysis and inventory analysis. Identify the first batch of
employees to participate in basic Lean training. This group should comprise high performing,
highly motivated individuals who can become the change agents for the organization. The
training should impart the following skill sets:
• Knowledge Attributes

• Technical Competencies
• Lean Skill Set –Certification/Recertification
• White Belt – Certification/Recertification
• Yellow Belt – Certification/Recertification
• Green Belt – Certification/Recertification
Step 5: The Role of Key Stakeholders. Key Stakeholders are those individuals who
control critical resources, break down barriers, have an influence on others in key roles, and own
an essential work process that will be impacted by the project. Once the key stakeholders are
identified, perform a stakeholder analysis to determine their level of influence on the success of
the project and the team’s assessment of the level of support. For each stakeholder, identify the
level of involvement in the project. Ask the following questions. Is the stakeholder:
• A decision–maker?
• Someone with any degree of influence on the decision–maker?
• Directly impacted by the change initiative?
Step 6: Execute Stakeholder Analysis. Perform the stakeholder engagement analysis
and determine the engagement score of each of the stakeholder. Following stakeholders are
highly recommended to be included in the analysis at a minimum.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 98
• The organization’s President and his/her cabinet
• The Provost and his/her administrative cabinet
• The Dean’s cabinet at each university
• The faculty’s cabinet
• Student Council
• Administrative heads across the universities
• Others as deemed necessary based on the university’s structure. Look for the allocated
budget and determine the need for human resources accordingly
• Development/Auxiliary services
• The Board of Trustee/Governance committee
Step 7: Stakeholder Analysis. The stakeholder analysis worksheet as shown in
Appendix M is a tool to identify and assess the importance of stakeholders that may significantly
influence the success of the project. One can then develop strategies to engage the stakeholders
and remove any obstacles. The primary purpose of this analysis tool is to determine the impact
and attitude of a stakeholder. The following steps should be included in Stakeholder Analysis:
• Brainstorm the project with all the people, functions, and organizations that will affect or
be affected by the project. Review and assign ranks (High, Medium, Low) for the impact,
power, and importance of the stakeholder’s interests to the success of the project.
• Identify the attitude of every stakeholder towards the project and select correct options
(+,0,–,?) accordingly.
• Devise possible issues and actions that can be taken to get stakeholder support and reduce
opposition.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 99
Step 8: Project Communication Plan. It is essential to keep the team, stakeholders and
others informed throughout the Lean and Six–Sigma project, by defining what should be
communicated, the purpose, frequency, the audience, and the method of communication. The
Project Communication Plan as illustrated in Appendix M has details of the project like Project
Name, Manager, Leader, Sponsor, and an updated revision date.
After selecting the right stakeholder, enter:
• The Contents – What
• Frequency – How Often
• Medium – How to communicate
• Feedback Loop – Does the communication go full cycle
• Owner – Who is responsible for the communication
Step 9: Create an RACI (Responsible – Accountable – Consulted – Informed)
Matrix. An RACI matrix is prepared to assign roles and responsibilities to project stakeholders
as shown in Appendix M.
Step 10: Creating a Project Charter. Every project needs to be focused on a clearly–
defined mission, which can be easily accomplished through the creation of a project charter
(Appendix M). It is a living document that will be updated throughout the project as the
problems become better defined. Although there is no one correct format for a project charter,
every project charter should:
• Include a business case
• Include a problem/an opportunity statement
• Clearly define the goal for the project
• Define the scope
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 100
• Identify the project team
The best practice is to develop the project charter collaboratively with the project team.
The draft is then shared with the project champion for feedback and approval. Any changes made
to the project charter at any time during the project must be reviewed and approved by the Lean
champion. Detailed steps for project charter are illustrated in Appendix P.
Step 11: Create Baseline Knowledge Attributes Inventory. This step is critical for
assessing the technical competencies of the organization. Assess and identify the knowledge
level and the Lean skill sets that exist in the organization.
Step 12: Project Status Worksheet. In order to keep executives and team members
informed about the status of the project, a Project Status Worksheet (Appendix M) should be
used to illustrate weekly highlights on the project progress. The worksheet comprises a four–
quadrant diagram that includes:
• Activities Performed during this period
• Actions Planned for the next period
• Risks, issues and concerns
• Mitigation actions planned
Step 13: Voice of the Customer & CTX. The Voice of the Customer (VOC) is a term
used to describe what is essential to the customer. A project that does not deliver what is
important to the customer may end up having no business impact. The VOC seeks to understand
what is essential to the customer and keeps customer requirements at the forefront of all
discussions, decisions, and actions undertaken during every phase of the project. Customers can
be either external or internal. External customers are individuals or organizations outside the
business who buy the product or service the business sells. Internal customers are fellow
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 101
employees who receive products, services, support or information from the process. The VOC is
used to help describe customer requirements.
Step 14: VOC – Persona Exercise. The Voice of the Customer Persona exercise is a tool
used to assign goals to stakeholders. First, identify stakeholders and work with the stakeholders
to determine the goals. The VOC Matrix gains stakeholder information and goals from the VOC
Persona exercise and assigns priority to each of them. There are three priority levels: High (H)
when the value is 9, Medium (M) when the value is 5, and Low (L) when the value is 1. See
VOC Persona Matrix in Appendix M.
Step 15: Create Baseline Motivation Attributes Inventory. During this step, establish
baseline associate, management, and leadership engagement scores. If any previous data is not
available, perform a quick assessment to gauge the employee engagement scores (EES). If the
organization has never done any annual associate or faculty satisfaction survey, approach this
exercise in partnership with HR and other supporting organizations. It is also useful to determine
the change management quotient by the associated survey instrument (see Appendix A).
Step 16: Create Baseline Organization Attributes Inventory. During this step, the
objective is to perform an assessment of the existing system. Some of the key areas should focus
on:
• Baseline Process Management analysis
• Baseline IT System analysis
• Quality Management analysis
• Past experience within the field of Operation Excellence
• Lean Enterprise System/Project Management (LES/PM)
• Project initiation repository
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 102
In Session Launch Sequence (18–24 months)
During this phase, there are few additional reinforcements beyond the creation of the
initial infrastructure, which are essential. This phase is about change management and
adaptation, and moving up the learning curve. This phase requires support, frequent calibration
and validation to gauge the progress of the phase. While a successful launch can be a significant
milestone for the organization, an “all–hands–on–deck” approach needs to be in place. There
should be regular pulse checks through data gathering and validation to ensure proper resources
are deployed to meet the requirements.
Step 1: Perform the impact of training program, project launch, and team morale.
Ensure that an improvement project is off to good start and the team is utilizing the pertinent
tools. Help the team remove any and all obstacles and causes of friction within the project,
process and the department.
Step 2: Revalidate project dashboard against a baseline assessment of organization’s
key performance indices. It is important to have a clear inventory of organization’s in–session
state before re–initiating any further HEES implementation steps through the following:
• Create a robust assessment on training plan
• Provide leadership support and weekly debrief meeting
Step 3: Validate Knowledge Attributes. Calibrate the progress of the training program
and measure the technical competencies from each group ascertaining the level of Lean Six
Sigma training, subsequently taking the following steps:
• Stakeholder Analysis
• Measure the output from launch success
• Measure, analyze and display the VOC findings from the stakeholder group
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 103
Step 4: Create Motivation Attributes Solution.

• Confirm Reward and Recognition effectiveness
• Confirm Intangible Recognition program and its impact
• Teamwork and autonomy validation
• Stakeholder assessment
Step 5: Create Organization Attributes Solution.

Baseline Assessment – Take II
• Validate repeatability and re–gauge measurement system
System configuration
• Implement Lean Enterprise System (LES)/Project Management
• Make sure the LES portal is functional without any technical glitch
Project initiation repository
• Check list for P1S2 (Appendix I)
Post–launch sequence (Past 24 Months to 120 Months Plus)
The majority of the Lean initiatives have failed due to a lack of cementing the momentum
achieved at the time of the launch. The outlined recommendations, SOP and system
reinforcement are designed to reduce the probability of failure and help imprint HEES into the
enterprise DNA.
Step 1: Validate performance against the baseline Feasibility /Organizational
Readiness assessment.
Step 2: Perform a robust assessment of baseline assessment of organization’s key
performance indices. It is important to have a clear gap analysis to ensure that gap mitigation
strategies work as plan in the following categories.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 104
• Knowledge Attributes

• Technical Competencies
• Lean Skill Set – Recertification
• White Belt – Recertification
• Yellow Belt – Recertification
• Green Belt – Recertification
Stakeholder Analysis – Take–III
Motivation Attributes Solution
§ Reward and Recognition
§ Report card and impact analysis
Intangible Recognition
§ Report card and impact analysis
Teamwork and autonomy
§ Report card and impact analysis
Stakeholder assessment
§ Report card and impact analysis
Organization Attributes Solution
Baseline Assessment
System Validation
§ Report card and impact analysis
§ Lean Enterprise System/Project Management
Project initiation repository
§ Check list for P1S3 (Appendix I)
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 105
Summary for Phase 1
The HEES rollout phase 1 recommendation one consists of three distinct phases, Phase
1(P1), Phase 2 (P2) and Phase 3 (P3). The Phase 1 Sequence 1 initiative clearly establishes the
important part of being able to launch HEES. Lean implementation can help create a culture that
embraces the concept of continuous improvement and system optimization. It is recommended
that outside expertise and thought leadership should be engaged to help build the knowledge
competencies, and motivational and organizational capacitates. The checklist for Phase 1
Sequence 1 (P1S1), Phase 1 Sequence 2 (P1S2) and Phase 1 Sequence 3 (P1S3) is designed to
help the organization make sure that key milestones are not missed. As the research has
indicated, the successful conceptualization, launch, execution and sustainment of the first phase
of Lean rollout can create a smooth journey into the implementation of the second and third
phases (P2 & P3) of HEES.. Monthly stakeholder engagement meetings, debriefing, and creating
celebratory events for quick wins can help with the HEES transformation within the system as
well as the culture.
Phase 2 – Sequence 1 (P2S1): Launch of HEES Six Sigma Initiative
Pre–Launch Sequence
During the first phase, the infrastructure for Lean knowledge skill–sets, the LES system,
and processes for Lean initiative have been established. Now the P2S1 can be implemented to
incorporate the advanced concept of Six Sigma. This phase requires an advanced knowledge of
quality management principles, which includes the ability to analyze large set of data and the
ability to quantify the root causes. Also, P2S1, P2S2 and P2S3 are designed to help reduce the
variability of key outputs of HEES. This phase ensure that university is able to reduce waste,
align the processes and system to deliver key outputs, and reduce variability.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 106
Many of Lean–certified employees who show a great deal of engagement, receptiveness,
and leadership can be identified and provided training to equipp them with the Six Sigma tools to
solidify LSS their knowledgebase. As illustrated in Gantt chart in Figure 31, this phase is
concurrently planned with Phase P1S2. Depending on the success and agility of the organization,
P2S1 can be adjusted to reflect the pre–launch of P2.

Figure 18. HEES Gantt Chart.
Pre–Launch Sequence (0–12 Months)
In order to create high–level Six–Sigma capabilities in the organization, a new baseline
assessment of the organization’s key performance indicators (KPI) beyond the initial Lean phase
should be executed. During this phase, one must identify the next level of KPIs that are focused
on reducing or eliminating process/service variation altogether. This survey instrument measures
the VOC and key CTx in understanding the perceived service–level gaps for both internal and
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 107
external customers. It is also a good idea to reevaluate and assess the Lean knowledge base and
benchmark it against the readiness for Six–Sigma skill–set needs. It is important to have a clear
inventory of an organization’s existing state of operating systems before initiating P2S1 of HEES
implementation plan. In order to accomplish this, a survey instrument should be designed to take
a quick inventory of the baseline assets (see Appendix A).
Getting Started. The DMAIC Project (Define, Measure, Analyze, Improve, and Control)
can prove to be challenging to take on as a process improvement project when one needs to
analyze and solve problems, lead and teach effectively, organize and manage projects well, and
present project findings that convince key stakeholders. Thus, the selection and formation of the
project team is critical to the success of the project.
Table 15
DMAIC Project

DMADV (Define, Measure, Analyze, Design, and Verify). DMADV is an approach for
developing new initiatives. It is utilized when a process does not already exist or when the
current process is so faulty that it needs to be redesigned. The five phases of a DMADV project
are Define, Measure, Analyze, Design, and Verify. DMADV is similar to DMAIC but is used
Phase Action
1. Define Describe the process, opportunity, and goal
2. Measure Collect data, quantify existing process & define
metrics
3. Analyze Review inputs & interface capability (X’s), clarify
assumptions, identify potential solutions
4. Improve Test theories, pilot solutions and rollout solutions
project wide
5. Control Establish monitoring to maintain the gain
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 108
when there is a need to build or rebuild an entire process or product from scratch (a.k.a.
Greenfield design) or when the current process is outmoded or cannot be improved enough to
meet current or future needs.
Table 16
DMADV Project
Phase Action
1. Define Describe the opportunity, vision, phases & gather VOC (Y’s)
2. Measure Quantify existing process & define metrics
3. Analyze Review inputs & interface capability (X’s), clarify assumptions
4. Design Create & refine design pilot & expand implementation
5. Verify Establish control plans

Step 1: Create Baseline Knowledge Attributes. Create technical competencies of
advance project management, business statistics, data analytics and change management.
The following Lean and Six–Sigma skill sets are mandatory for the creation of the aforesaid
technical competencies:
• Green Belt for HEES Certified Associates
• Black Belt HEES Group Leader
• Master Black Belt HEES SPOC
Reevaluate Stakeholder Analysis to calibrate the engagement score and associated
change. This would provide an indication if re–engagement and message modification are
necessary.
Along with previously identified stakeholders, engage newly trained and certified Lean
associates, SPOC and SPOC to gain their respective perspective for the next level of
advancement.
Step 2: Create New Baseline (Post–Phase 1) Motivation Attributes. Expand the
Reward and Recognition mechanism in the following ways:
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 109
• Determine the impact and influence of the current reward system
• Expanding the recognition and employee participation rate, with an understanding of
phase 2 requires a deeper level of cognitive and meta–cognitive assets
• Enhance the degree of teamwork and autonomy within the organization
Step 3: Create New Baseline (Post–Phase 1) Organization Attributes. Conduct a
system assessment on the following parameters:
• Did the system implementation work as planned and has the organization adopted the use
of new systems such as LES and HEES Phase 1?
• Baseline Process Management Analysis
• Baseline IT System Analysis
• Quality Management Analysis
• Check past experience within the field of Operation Excellence
• Lean Enterprise System/Project Management
• Project initiation repository
A checklist for P2S1 (Appendix I)
Step 4: Create Training Program for Six–Sigma. Create a new training program
focusing on advanced knowledge skill set focusing on how to identify, initiate and successfully
manage DMAIC projects.
In Session Launce Sequence (18–24 months)
During this phase, there are few additional reinforcements beyond initial phase 1
infrastructure that must be done. The change management and adaptation/learning curve become
mandatory as this phase requires a much higher level of monitoring of resource movement. This
phase also requires support, frequent calibration and validation to gauge its progress. While the
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 110
successful launch can be a significant milestone for the organization, an approach of “all–hands–
on–deck” needs to be in place and proper resources should be allocated. There should be a
regular pulse check on measurement and data validation to ensure proper resources are deployed
to meet the requirements.
Step 1: Perform Baseline Assessment. Create a baseline assessment of organization
key performance indexes. It is important to have a clear inventory of organization’s current state
before initiating the HEES implementation plan.
Step 2: Create Knowledge Attribute Inventory. Technical Competencies–Lean and Six
Sigma advanced skill sets:
• Green Belt
• Black Belt
• Master Black Belt
Stakeholder Analysis:
• Measure the output from launch success
• Measure, analyze and display the findings from the VOC from stakeholder group
Step 3: Create Motivation Attributes Solution.

• Confirm Reward and Recognition effectiveness
• Confirm Intangible Recognition program and its impact
• Teamwork and autonomy validation
• Stakeholder assessment
Step 4: Create Organization Attributes Solution.
Baseline Assessment
• Validate repeatability and re–gauge measurement system
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 111
• Make sure LES portal is functional without any technical glitch
Project initiation repository
• Check list for P1S2 (Appendix I)
Post–Launch Sequence (Past 24 Months to 120 Months Plus)
During the sustainment phase, as cited in previous chapters, the majority of the Lean
initiatives have failed due to lack of cementing the momentum and forming organizational
habits. The outlined recommendations, SOP and system reinforcement are designed to reduce the
failure probability and help build the HEES into enterprise DNA.
System Validation
• Report card and impact analysis
• Lean Enterprise System/Project Management
• Project initiation repository
Check list for P2S3 (Appendix I)
Summary for Phase 2
The checklist for P2S1, P2S2, and P3S3 is designed to provide step–by–step guidelines to
the organization to make sure that critical milestones are not missed. As the research indicated,
the successful conceptualization, launch, execution, and sustainment off Lean rollout P1 and P2
can create a smooth journey into other recommendations to help the rollout of phase 3 of HEES.
Monthly stakeholder engagement meeting, debriefing with the staff, and creating celebratory
events for quick wins can help the HEES transformation with the system as well as the culture.
Although MU–Lean was unable to integrate phase 2, the outlined approach should help
integrate phase 2. The process of HEES phase–3 integration can greatly benefit from the
successful launch of phase 2. Six Sigma.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 112
Phase 3 (P3S1 and S2): Launch of Advance Data Analytics, AI ML, and RPA within HEES
It is recommendation that during phase 3, the organization starts out with mapping out
the data flows through the organization. Create various flow charts such as value stream mapping
(VSM) and cross–functional swim lane, and asses the systems capabilities. Based on the
literature review, benchmarking, interview and survey, it is recommended that the pre–launch
sequence for phase 3 should be included at a minimum of 9–12 months.
Pre–Launch Sequence (0–12 months)
Step 1: Define the Project Charter for the Advance Phase 3 Initiative.

Use the template discussed in phase 1.

Step 2: Feasibility/Organizational Readiness Assessment. Use the template discussed
in phase 1.
Step 3: Create an Baseline Assessment of the Organization’s KPIs.
Step 4: Create a Survey Instrument. Create a survey instrument to gauge the advance
knowledge of emerging technologies, the organization’s capabilities and its willingness to
embrace Phase 3.
It is important to have a clear inventory of the organization’s current state of operating
system before initiating the HEES implementation plan. In order to accomplish this, be sure to
engage the Chief Information Officer and the relevant cabinet from the beginning. The survey
instrument should be designed to take a quick inventory of the baseline assets (See Appendix A)
Step 5: Create Baseline Knowledge Attributes.
Technical competencies:
• AI/ML/RPA
• Advanced Data Analytics (Predictive and Prescriptive)
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 113
• System Architecture and advance knowledge of HEES CONOPS as shown in Figure 33
(See Appendix M).
Conduct stakeholder analyses of:
• The President’s office and his/her cabinet
• The Provost and his administrative cabinet
• The Dean and his cabinet at each of the school
• The faculty cabinet
• The student council
• Various Administrative heads across the universities
• Others as deemed necessary based on the university structure
• The Chief Information Officer and his/her cabinet
Step 6: Create Baseline Motivation Attributes.
• Reward and Recognition
• Intangible Recognition and employee participation rate
• Teamwork and autonomy within organization
Step 7: Create Baseline Organization Attributes.
System assessment:
• Baseline Process Management Analysis
• Baseline IT System Analysis
• Quality Management Analysis
• Past experience within the field of System Excellence
• Lean Enterprise System/Project Management
• Project initiation repository
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 114
Check list for P3S1 (Appendix I)
In Session Sequence (18–24 months)
Step 1: Create HEES System Rollout Preparedness.
1. Data Synthesis
2. Data Integration/Normalization
3. Identification of Objective function/Optimization
In order to create the phase 3 of the HEES infrastructure, many other advanced data analytics
and AI, ML implementation models were compared. The recommended approach below is a
result of the successful rollout and integration of phase 3 with phase 1 and phase 2 of HEES.
Step 2: Create Schematic Black–Box. There are three main phases within this black
box:
• Data Preprocessing
• Feature Engineering
• Model Building
After these 3 phases, the delivery channel can be either a dashboard, chat bot or web app.
The data from multiple sources with multiple representations need to be stored in a database.
Since we are planning on using Google Cloud Platform (GCP), it can store the application on
Google cloud. However, HEI should verify about the sensitivity of data, since its public cloud.
Step 3: Identify Data Preprocessing for General ML or Deep Learning Algorithms.
• Filling in missing values
• Smoothening noise by removing errors or outliers
• Resolving inconsistencies
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 115
• Resolving conflicts when merging data from multiple sources or data with different
representations
• Normalizing/aggregating/generalizing data if required
• Categorical values need to be converted to numerical ones
• Feature values need to be scaled so that they can be compared without any bias. If the values
are not present on the same scale, then the distance measures like Euclidean distance can get
affected, eventually affecting the model. Even otherwise, the model would converge faster when
all the features are scaled.
Step 4: Conduct Data Preprocessing for Language Understanding/Processing.
• Remove unnecessary characters like numbers, white space, and punctuation
• Handle capitalization – cannot change everything to lower because words like “US” would
lose meaning when changed to “us”
• Tokenization – splitting the text into tokens/words
• Stemming and Lemmatization (which is an alternative approach to Stemming by removing
inflections by using POS tags knowledge). E.g. Stemming of “leaves” might give “leave”
whereas lemmatization of “leaves” would give “leaf.”
• Annotation like POS tagging
• Building dataset – splitting the data into Train, Dev and Test sets. Cross–validation
techniques need to be used to avoid over–fitting.
Step 5: Conceptualize Engineering/system requirement.
• For the ML models, for each use case, pick/decide features and train model on those features.
• In the case of Deep Learning models, the neural networks will automatically pick features
and learn stuff. Furthermore, one needs to decide whether it will be a classification or a
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 116
regression problem. Based on that, the final layer will either be SoftMax or a single neuron,
giving out the regression value.
• For language processing, one can try statistical models. The Neural language models have
proven to be giving better performance. In another approach, the words must be converted to
word embedding either from pre–trained embedding or building custom ones based on the
dataset.
• The word embedding can be basic ones like word–counts, TF–IDF vectors or advanced ones
like Word2Vec, GloVe.
Step 6: Create a Model framework.
• Build traditional models like Naïve Bayes, etc. and check the performance.
• Try out the GCP’s pre–existing models and check the performance.
• Try deep learning model side by side.
• Build multiple candidate models and use evaluation metrics to choose one finally which
can totally be an ensemble approach as well.
• For language understanding, try out the statistical models as well as the neural language
models like RNNs, LSTMs, etc.
Identify datasets in detail (See Appendix N for Student, Faculty and Alumni datasets)
• Check for publicly available datasets, which can be readily used. Likewise for data on
student attrition rate, graduation rate, etc. which are general statistical measures that can
be used.
• For review, either web scraping from job portals like Glassdoor, Indeed, etc. or already
built dataset can be used to acquire information about various job positions, general
descriptions, pay scale, and companies.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 117
• Data sources – USC systems like Blackboard, Oasis, Workday, all sub–portals of myUSC
portal. LinkedIn, Twitter and Facebook posts or feed of a student.
Step 7: Platform Specifications. There are numerous Cloud AI solutions (See Appendix
O) such as AutoML, TPU’s, and Speech to Text and ML engine that can be used to build a
Google Cloud Platform.
Recommended Reference API Example
The first step involves uploading PDF and TIFF documents into the system, which are
stored on cloud storage, which triggers background function transcribing the documents stored in
cloud storage using Vision API. The next step involves storing the transcribed documents on
cloud storage. The further step involves accessing data quickly using the search capabilities. See
API example in Appendix L. A detailed system architecture and mock wireframe document is
provided in Appendix N.
Project Plan
A layout of the project is discussed in this section. The project will be divided into sprints
where each sprint provides certain features. The first sprint includes the database design,
platform bring–up, software architecture, foundation classes and programs that form the crux of
the whole system. In other words, the framework should be developed first in order for other
layers to utilize the ore services of the product. Everything proposed is to be built on Google
cloud platform since it had many built–in services that can be easily integrated into the project.
The second sprint will involve the development of a dashboard, which serves as the user
interface for students, and teachers of the university. Using this, the students can benefit from the
core services with a web app that consolidates all the features of the platform and also interact
with a Chabot for instant advisement.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 118
Further steps will be taken to make this a mobile–first approach, which brings mobile
apps to students on iOS and Android operating systems. The mobile app would be a miniature
version of the web app. This app would bring power and machine learning and HEES to the palm
of the users.
Phases
Phase 1:
● Percentage Graduation Rates
● Percentage Placements
● Return on Investment
Phase 2:
● Dashboards
● Alumni and Endowment score
● Staff Engagement Score
Phase 3:
● Ranking
● Research Productivity
● Industry engagement
Summary for Phase 3
The literature review and research findings have shown that very few organizations have
been successful in launching phase 1. Implementation Phase 2 and phase 3 are even rare within
HEI. However, both phase 2 and phase 3 can provide significantly advanced capabilities to
efficiently and effectively manage HEI’s delivery system for students, faculty, staff, and other
external stakeholders. The implementation of phase 3 can unleash the power of emerging
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 119
technologies such as AI, ML, and advanced data analytics. It may appear that data privacy and
other concerns can be overwhelming for the organization, but the successful integration of phase
1 and phase 2 of the HEES can help transfer the momentum into the phase 3.
HEES/ AI/ML/Advance Data Analytics Model Implementation Plan.
§ Run a full–scale prototype model with limited datasets to validate the objective functions.
§ Provide conceptual model algorithm and data layering architecture with easy API
integration to maximize subscribed objective functions.
Implications for Future Research
Phase 1 and 2 are relatively well defined to execute within the current framework. The
phase 3 of the HEES can contribute to continued research in integrating, designing and fully
implementing outlined capabilities. Following areas of focus can help foster further
development and research:
§ Create the awareness, research agenda, and the legislative engagement of the need for
HEES in HEI.
§ Entirely build out Google Cloud AI/Ml and RPA modules. Create Predictive/Prescriptive
data model and relevance within HEI.
§ Create a relevant survey/interview instruments with the capability and cultural
implication within HEI.
§ Promote policy implication at the local, state and national level on data–driven model
roles within HEI.
§ Promote the culture of data security and privacy protocol within HEI.
§ Create an assessment and evaluation framework for the ethical dilemma and misuse of
information.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 120
§ Building full–scale HEES and expansion into other levels within education including
potential global rollout.
Conclusion
In a progressively competitive global economy, massive disruptive forces have changed
the way we source, consume, live and learn. Obtaining a college degree has become increasingly
crucial while resetting one’s skill set is becoming more frequent in one's life. This change
demands innovation within HEI. America’s HEIs have been ranked among the world’s top
universities for over 100 years. However, cost inflation, student debt, and a lack of access will
not be able to guarantee the success for the future generation as student debt is continuing to
climb to record highs.
HEES’s three phase approach can help drive inefficiencies, redundancies, and the lack of
system–wide collaboration. It will help HEIs save hundreds of millions of dollars thus greatly
benefitting universities, students and society at large. HEES will also help solve some of the
peripheral issues such as the lack of sustainability and a reasonable Return on Investment (ROI)
for students and family. HEIs can use the DIY step–by–step guidelines of HEES to enhance
student experience with the use of AI/ML and advance data analytics capabilities. Leadership
within the HEIs can embrace HEES roadmap with some of the key measures as defined below:
§ Understand the inflection point and contributing factors for rising cost, learning gaps and
student debt within the university.
§ Identify critical elements of HEI eco–system needed for a successful launch of HEES.
§ Identify knowledge, motivation and organizational assets necessary to innovate university
and find ways to become a more data–driven, Lean and six–sigma organization.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 121
§ Establish best practices for baseline assessment of organizational culture and context,
stakeholder knowledge and motivation necessary to foster the philosophy of HEES.
§ Gain a more in–depth perspective on culture, leadership and change management, which
is agile, effective and efficient in meeting the needs of the future generation.
§ Embrace emerging technologies such as Artificial Intelligence (AI), Machine Learning
(ML), and Advance Data Analytics to help deliver efficient and effective learning
outcomes for students.
HEES multi–phase successful implementation will provide savings by removing waste,
redundancies and improve student’s ROI. It will also help improve the learning outcomes by
proactively using data–driven capabilities and provide mitigating strategies by curating resources
for students proactively. This process will improve the matriculation yield rate. Lastly, the use of
advanced data analytics capabilities with predictive and prescriptive capabilities will help
manage better in–class learning experience as well as post–graduation job placement. HEES
ability to use data–driven decisions and being able to proactively mitigate the learning obstacles
as well as curate the customized advisory/tutoring resources to students while lowering the cost
of education will reshape the future of HEI.
My industrial and system engineering background helped my journey in system
optimization during the formative years of my career. I was fortunate to have opportunities to
work on projects that allowed system–centric thinking and optimization. It further enabled me to
connect end–to–end processes, people, data/systems and linking system inputs to deliver
customer–centric outcomes. It gave me the good opportunity to drive transformation for over
500–plus projects in industries such as retail, manufacturing, defense, pharmaceutical, genome
research, agriculture, finance, hospitality service, and healthcare industries. My journey into
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 122
higher education along with critical issues of student debt, made me think why HEIs have been
reluctant to adopt models like HEES that helped other industries immensely. The recent issues
that the US–based HEIs are dealing with, particularly the negative press they have received
around the issues of student and family debt, access, equity, learning outcomes and most
importantly, ROI, have created a sense of urgency.
Through this research journey, I have gained a profound appreciation for the HEIs’
ability to do impeccable research and create repositories of knowledge that can help lead
breakthrough innovations for other industries. However, it is clear that the HEIs do not use
innovative management models such as HEES that can help them deliver education while
reducing cost, improved learning outcomes, and providing student–centric, innovative, 21st–
century delivery models. The full implementation of HEES will help universities reduce tuition
fee for at least about ten years based on the projected numbers from Miami University of Ohio.
Indeed, a successful HEES can help HEIs enhance their substantiality and reputation. The
investments and the commitment for five years of full integration of HEES will lead to massive
savings, an improved ROI, and improved learning outcome, all of which can suitably address the
concerns prevalent in the society about the prohibitive costs of higher education. An HEI’s
enhanced data–driven capabilities will help students get a better quality of education at
affordable rates with improved post–graduation opportunities that will enhance their ROI and, in
the long run, help sustainably create human capital for the 21st–century economy.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 123
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APPENDIX A
Survey Items
1. Your Role:
Lean Partner () Lean Manager () Lean Senior administrator () None ()
2. How Long have been at the Miami University? Use continuous scale rather than these
ordinal levels. It provide more information and will be more useful for your inferential
analysis.
a. 0–2 years
b. 3–5 years
c. 6 –10 years
d. 11 plus years
3. How many years of prior TQM, Lean, and Six Sigma experience did you have before
MU?
a. None
b. 0–2 years
c. 3–5 years
d. 6 –10 years
e. 11 plus years
4. I believe MU–Lean has been beneficial to cost–containment initiative for the university.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 142
5. As a certified MU–Lean participant, I have all the necessary tools to work on my process
improvement initiatives.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
6. As a certified MU–Lean participant, I have been trained to execute projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
7. It is important for MU–Lean to continue to deliver the cost savings for the next 10 years
as a part of the cost–containment initiative.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
8. I am recognized for my efforts for working on MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
9. I am rewarded for my efforts for working on MU–Lean projects.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 143
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
10. I am recognized for my efforts for leading MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
11. I am rewarded for my efforts for leading MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
12. I am recognized for my efforts for managing MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
13. I am rewarded for my efforts for managing MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 144
d. Strongly Disagree
14. I have the full support from my manager to work on MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
15. I feel confident in my ability to initiate the MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
16. I feel confident in my ability to execute the MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
17. I feel confident in my ability to sustain the MU–Lean projects.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree

18. There is a receptiveness for new ideas.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 145
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
19. There is a receptiveness for change management.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree

20. There is a need for Higher Education Institutes to create cost–containment initiative(MU–
Lean).
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
21. Students see the value in MU–Lean initiatives.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
22. MU–Lean has been able to collaborate with faculties.
a. Strongly Agree
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 146
b. Agree
c. Disagree
d. Strongly Disagree
23. MU–Lean has been able to collaborate with various department.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree
24. MU–Lean has been able to collaborate with administrator.
a. Strongly Agree
b. Agree
c. Disagree
d. Strongly Disagree

25. What are the most relevant parts of the MU–Lean that you think are important. (Select
one answer for each attribute on a Likert scale 1—5; 1 being not important and 5 being
very important)
Attributes 1
Not
Important
2
Maybe
Important
3
Important
4
Some
what
Important
5
Very
Important
TQM Principles
Lean Principles
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 147
Six Sigma Principles
Team building
Internal comraderies
Project management
Communication
Clearly defined Project
Charter

Management
Support/Collaboration

Training and coaching
Recognition and reward
Drive cost–containment
efforts

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 148
APPENDIX B
INTERVIEW PROTOCOL
Objective: This interview is part of doctoral dissertation study focusing on blended
quality management systems (MU–Lean) and its impact at MU for various stakeholders. The
goal is to understand how the system conceptualized, commenced and sustained from 2012 till
now.
Background Information: I would like to ask for your permission to record our
conversation so that I can transcribe the details and capture the true essence of our conversation.
a) How long have you been at the MU?
b) What is your current role and how long have you been in it?
c) How long have you been in the current role?
d) How long have you been affiliated with TQM, Lean, and Six Sigma practice?
1. Can you tell me about the circumstances that led MU to conceptualize the MU–Lean
initiative?
Potential Follow–up Questions:
a) How did the recession of 2008 influence on the launch of MU–Lean?
b) What influence from the drastic cuts in funding from the state had in the launch of
the MU–Lean?
c) How did the MU–Lean idea come to shape?

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 149
2. What past experiences in any quality management organizations help support your
role during pre–launch phase of MU–Lean?
Potential Follow–up Questions:
a) Follow–up Questions: If yes, where did you practice prior?
b) Can you discuss the details on your Lean certification credential?
c) How would you compare your experience from previous Lean transformation to here
at MU?
d) What were the main challenges during the initial meetings on MU–Lean launch?
3. What kind of resources was available for you to during the pre–launch phase of the
MU–Lean initiative, if anything?
Potential Follow–up Questions:
a) Can you share the details on how the financial resources were provided to build the
MU–Lean organization?
b) How did the outside consultants and the organization help set up the framework?
c) What tools, alignment was sought out for the kick–off?
d) What system alignments was sought out for the kick–off?
e) What stakeholders’ alignments was sought out for the kick–off?

4. Where did you gain the essential knowledge of Quality Management system for the
pre–launch phase of MU–Lean initiative?
Potential Follow–up Questions:
a) How do you feel TQM, Six Sigma principles blends into MU–Lean?
b) How do you feel Lean principles integrates into MU–Lean?
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 150
c) How do you feel Six–sigma principles integrates into MU–Lean?
5. Can you share thoughts regarding of if you were to restart, what would you do more
and less of?
Potential Follow–up Questions:
a) What were the significant issues in regards to Lean understanding and tools?
b) What were the significant issues in regards to change management?
c) What were the significant challenges with being supported by the upper
management?
6. How does MU–Lean certification work?
Potential Follow–up Questions:
a) What are the different levels?
b) How often do teach the class?
c) How do you provide exam, proctoring and provide certification?
7. During the sustainment phase, what support did you receive regarding the resources,
change management, various stakeholder group to collaborate with the MU–Lean initiative?
Potential Follow–up Questions:
a) How did you continue to engage the Lean teams on MU–Lean?
b) What incentives or recognition is in place for the successful teams?
c) What is the primary challenge to keep everyone motivated?
8. Why do think it is essential for the Mu to continue their efforts on the MU–Lean
initiative and is it sustainable?
Potential Follow–up Questions:
a) Has the cost–containment been the driving force for the MU–Lean?
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 151
b) Do you believe MU–Lean can help improve student learnings and efficacy? If so,
have you done any projects on this?
c) Why do you think HEI’s are reluctant to embrace quality management principles?
9. How do Lean administrators support continues enhancement for Lean administrators
(LA)?
Potential Follow–up Questions:
a) What are the qualification criteria to join the ranks of LA?
b) How is the annual goals and objectives are set?
c) What are the reward systems for LA?
d) What ate the incentive and recognition program for LA?

10) What steps are taken to ensure the Lean partners, Lean leaders, and Lean
administrators feel more confident and comfortable with the MU–Lean initiative? Potential
Follow–up Questions:
a) How often the toll–gate reviews are conducted and who is part of the review?
b) What dashboard and key performance indicators(KPIs) are targeted for discussion?
What steps are initiated to ensure the sense of belonging and ownership is instilled to
make MU–Lean as a part of the self.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 152
APPENDIX C
Definitions

Black Belt
Name used for a team leader trained in the DMAIC process and facilitation skills; responsible for
guiding an improvement project to completion.
Continuous Improvement
Proactive efforts to increase quality, process performance, customer satisfaction, etc. through
data–focused problem solving and process analysis as an ongoing activity.
Dashboards
Compilation of process performance data (usually presented in a graphical format); used to keep
current on trends, problems and opportunities.
Defect
Any instance or occurrence where the product or service fails to meet the customer requirements.
Defective
Any unit with 1 or more defects (see Defects).
Define
First phase in DMAIC to define the problem, the process, and the customer requirements;
because the DMAIC cycle is iterative, the process problem, flow and requirements should be
verified and updated through the other steps in DMAIC to more clearly define these components
(see Charter, Customer Requirements, Process Map, VOC).

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 153
Green Belt
Name used for a team member who is trained in the DMAIC process and will be responsible for
assisting in DMAIC projects.
Hand–off
Any time in a process where one person actually hands the item moving through the process to
another person; potential opportunity to add defects, time and cost to a process.
Hypothesis Statement
A complete description of suspected cause(s) of a process problem.
Process Improvement
Improvement approach focused on incremental changes/solutions to a process to eliminate or
reduce defects, costs or cycle time; process improvement leaves basic design and assumptions of
a process intact (see Process Redesign).
Quality Assurance (QA)
Discipline (or department) of maintaining product or service conformance to customer
specifications; primary tools are inspection and SPC.
Sponsor (a.k.a Champion)
Person who represents team issues to senior management and gives final approval on
recommendations of the team and supports those efforts with the Quality Council; facilitates
getting resources for the team as needed; helps Black Belt and team overcome obstacles; acts as
a mentor for the Black Belt.
Stakeholder
Any person who is the recipient of direct or indirect benefit by the project, affected by its
outcome or can influence its success.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 154
Standard Deviation
A measure of the average distance that the values deviate from the mean, or the arithmetic
average; calculated for continuous data only.
Subgroup
The number of consecutive units extracted for measurement at each sampling event; a subgroup
can be just one. Subgroup data may be required depending on the size of the population to be
sampled, and for specific SPC charts (see also Sampling, Sampling Event, Sampling Frequency).
Supplier
Any person or organization that feeds inputs (products, services or information) into the process;
in a service organization, many times the customer is also the supplier.
Value Added Activities (VA)
Steps/tasks in a process that meet all three criteria to define value as perceived by the external
customer: I) the customer cares, 2) the thing moving through the process changes, and 3) the step
is done right the first time.
Variation
Change or fluctuation of a specific characteristic in a process that determines how stable or
predictable the process may be; variation is effected by factors such as environment, people,
machinery/equipment, methods/procedures, measurements and materials; the object of any
process improvement activities should be to reduce or eliminate variation (see also Common
Cause, Special Cause).

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 155
APPENDIX D
INFORMED CONSENT/INFORMATION SHEET

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 156
APPENDIX E
WHY’S MODEL

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 157
APPENDIX F
LITERATURE REVIEW SUMMARY

Article/Author Subject Summary with Citations
Anderson, N. 2015. Efficiency

Major rise international students have increased competition
at top America’s top universities. Federal data indicated a
growth in students in freshman classes by 5% between 2004–
2014. However the total of foreign students grew by 46%,
raising the applications to schools by 88%

While international growth creates a cosmopolitan campus
culture, industry leaders fear that the high international
enrollments are crowding out qualified local students.

Some argue that more foreign students help balance budgets
in public universities, which helps during funding cuts year
around. Similarly, in private universities where international
students are required to pay the tuition in full, helps the
colleges increase school revenues.

Leading schools, deny that international recruitments is all
about money. Prominent colleges believe they simply wish to
live up to the global university standard and accept talented
candidates from different countries.
Cilluffo, A. 2017 Cost The United States has over $.13 trillion in student loans. More
than four in ten adults under the age of 30 have student loans.
The outstanding share of young adults with a bachelor’s
degree or more has a debt of nearly 55%. The debt is
significantly lower in adults aged between 30 –44. This age
category has a debt rate of 4%. Older adults have more time to
repay their loans. Roughly one–fifth of young adults who
have loans undertake a second job to cope with financial
struggles. Less than 30% of college graduates with loans can
afford a comfortable lifestyle.
Raikes,
M.H.,Berling, L.V
& Davis, M.J.
2012.
Cost The average cost of higher education in public universities
increased by 30% between 1995 – 2005, while private sector
rose to 21% (United States Department of Education, 2007).
The performance rates and financial aid numbers of students
are not adequately disclosed. As a result, it is questionable as
to whether the investment in these institutions have a positive
impact in the long run. With higher costs, comes increased
borrowing. Students at present are seeking more financial
assistance than prior generations. Loans account for 55% of
all aid, making it the primary source of financial aid (Heller,
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 158
2008). Therefore, institutional aid such as grants and
discounts are now more important and in fact necessary.
Studies conducted have proven that the most prominent
contributor to timely graduation was the amount of
institutional aid provided. Universities and colleges offering
the most aid had four year graduation rates than rival
institutions.
Ruiz,G.N &
Radford, J. 2017
Efficiency Post Great Recession, the number of international students
enrolling at public universities increased by 107% by 2016.
The number of foreign students enrolled in bachelor’s degrees
gre by 151%. Foreign students have spent nearly $2.5 billion
from 2008 to 2016 at public universities. On the contrary, the
number of international students in private schools grew by
98% in 2016. Amongst these students, the majority were
pursuing graduate degrees. In fact, nearly 50% of the total
enrolled in colleges, were pursing either a master’s or
doctorate degree. More than half of this population coming
from countries such as China, India and South Korea. A total
of 10 states including California, New York and Texas
accounted for 63% of foreign students in 2016. As a part of
the global leader of democratic country, the purpose of this
increase is not to discourage foreign students or limit them
from U.S HEI’s but rather making sure that HEI’s do not use
this increase in demand as a measure to avoid having the
conversation about the cost containment and addressing the
issues of fears by U.S students; student debt, cost information,
access, equity
Wilbert, M.J &
Haddad, M. 2014
Cost Key players in the HEI, including universities and colleges are
named in the blame game for the student debt crisis. These
players are criticized for raising tuition fees at unreasonable
rates. As a result, the world has turned a blind eye towards the
reductions in state funds for higher education (Kelderman,
2013)..

With higher educations promising the American Dream,
parents encourage their children to attend college through
borrowing loans and seeking financial aid. With parents
having no intention of helping the youth pay off the loans, the
rate for completing a degree is extended to over 5 years. The
overall rate to complete a program and graduate in six years in
54.1% (National Student Clearinghouse, 2013).

From 2011–2012, the federal government lend nearly $100
billion to students, this rate is double the amount is was a
decade ago (Carey, 2013). The lack of financial management
skills plays a critical role in the delayed completion rates
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 159
(Cummins,M.,Jenkins,S., Haskel, J., 2009). Parents and high
schools need to ensure the youth is financially literate, they
need to be the source of financial reason for these children (As
de Baca, 2012).
Kwong, C.J.,
Dhalla, A. I.,
Streiner, L.D.,
Baddour, E.R.,
Waddell, E.A &
Johnson L.I. 2002.
Cost The rise in tuition costs is not solely in the U.S. This increase
has taken place in Canadian Institutions as well. Since 1997
the cost of medical schools has doubled in Ontario. Over time
studies have indicated that within the last few decades the
student debt in Ontario has significantly increased.
Baum, S.2016.
Student Debt,
Rhetoric and
Realities of Higher
Education
Financing.
Palgrave Pivot
Cost Due to lack of financial knowledge, young adults are
borrowing more than they should. Borrowing without
guidance is harming many former students. The problem is
not borrowing big loans but taking loans to invest in programs
that would not pay off in the long run.

Under current regulations, unpaid loans are forgiven through
“income–driven repayment programs” that are taxable.
Therefore borrowers whose incomes have not met the
repayment rates in the long run will face high income tax
bills. There are numerous ways to improve the loan servicing
issues. The system needs to ensure that it prevents giving out
loans to people who are very unlikely to repay them in the
long run. In addition, the system needs to support borrowers
with easy insurance strategies that prevents them from getting
tied to a hostile system.
Increase in Student
Demand
Efficiency Increase in international enrollments has created stiff
competition amongst ivy league schools. At Yale University,
typically acceptance rate is 6% of 30,000 applicants. The
international share of the freshman cohort has grown to over
10%. This increase is due to the university’s goal to attract a
diverse student body.

Local applicants in the U.S are unaware of the rise in
international enrollments and what impacts it could have on
them.
Wright, A.D.,
Ramdin, G &
Vasquez–Colina,
D.M. 2013. The
Effects of Four
Decades of
Recession on
Higher Education
Enrollments in the
United States.
Efficiency The U.S has face six economic recessions from 1970 to 2009.
Study conducted by these authors found a strong link between
“gender and enrollment, and ethnicity and enrollments to
recessions and unemployment and undergraduate enrollment.”

Unemployment was a vital factor impacting one’s ability to
afford higher education (Betts & McFarland, 1995).

An ethnicity analysis indicated that enrollment amongst
Caucasians (43%) and African–Americans (74%) was higher
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 160
Universal Journal
of Educational
Research
during the 11 years of recession, in comparison to the 29 years
of non–recession (Caucasians, 32% and African–Americans
63%). Therefore recession did not cause the increased
enrollment.

The study further indicated that recession had no impact on
either African–American males and females or Caucasian
male or females enrolling in higher education.
Zhang, L.,
Worthington, C.A
& Hu, M. 2016.
Cost economics in
the provision of
higher education
for international
students:
Australian
evidence. Griffith
University
Cost The number of students pursuing higher education globally
has doubled from 2 million in 2000 to 4.2 million in 2012
(OECD, 2014).

In Australia, education services are one of the country’s
largest exports since 2013. In 2014, the country earned
A$15,7 billion, which accounted to nearly 30%of all service
exports (ABS 2015a;DFAT 2015). However, the low financial
incentives have made universities compete with each other for
more international students (Australian Government 2015a).
Therefore the the international higher education suffers from
quality issues such as poor performance, degree mill and more
negative interactions between locals and foreign students.
Popescu,. H.G &
Ciurlau, C.F. 2017.
The Skyrocketing
Costs of U.S
Higher Education
and the Student
Debt Crisis
Cost The private market for loans used to be vital in higher
education, but this trend has diminished over the past decade
due to the 2008 financial crisis (Nica et al., 2017). The market
has not moved to an enlargement of a government funded loan
scheme (Ionescu and Simpson, 2016).
Fitch, C.,
Hamilton, S.,
Bassett,P & Davey,
R. 2011.
Relationship
between personal
debt and mental
health: a systematic
review. Mental
Health Review
Journal
American
Dream
A study conducted by Drentea in 2000, analyzed 1,037 U. S
participants with different age groups, credit card debt and
anxiety. The results revealed that participants over 40 years
were less anxious about their credit card debts. However,
adults under age 30 had a greater likelihood of having debt
stress. Credit card debt was not the only factor; non–consumer
debts also played a significant role in rising stress levels.
Curto, V., Mitchell,
S.O & Lusardi, A.
2009. Financial
Literacy Among
the Young:
Evidence and
Implications for
American
Dream
Study conducted to examine financial literacy among young
adults in 1997, indicated that less than one–third of the youth
had basic knowledge of “interest rates, inflation and risk
diversification.” The results further showed that financial
literacy is strongly linked to socio–demographic
characteristics and family financial backgrounds. College
graduated males whose parents had “stock and retirement
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 161
Consumer Policy.
Center for
Research on
Pensions and
Welfare Policies
savings” had more knowledge about risk diversification than
females with less than a high school education with parents
who were not wealthy.
Gast, J.M &
Jackson–George,
C. 2015.
Addressing
Information Gaps:
Disparities in
Financial
Awareness and
Preparedness on
the Road to
College. Journal of
Student Financial
Aid.
American
Dream
The information gap among low income, Black and Latino
students is making these individuals less prepared for making
a stable living after graduation.
Houle, N.J. 2014.
Disparities in Debt:
Parent’s
Socioeconomic
Resources and
Young Adult
Student Loan Debt.
American
Sociological
Association.
Cost College costs and inflation has skyrocketed in the U.S
(College Board, 2010b). Parents and students are in charge of
paying these high costs. The rise in costs are due offset by
grant aid, that has failed to keep up with the increasing costs
(College Board, 2006). As a result, families are left to pay the
differences, creating more need for borrowing other loans.
The student loan debt crisis is known to be a double–edged
sword for current young adults (Dwyer, McCloud and
Hodson, 2012).One side, is where borrowed money can be
used to fill the gap family resources and rising college costs.
On the other side, debt has high risks, limiting opportunities
for college graduates.

One theory known to reduce these risks is a reproduction of
advantage. Where parents use their financial and educational
resources to reduce their children’s college debts. The human
capital theory where parents with more financial capital can
easily invest in higher education for their children (Becker,
1981).
Kuah, T. C &
Wong, Y.K. 2011.
Efficency
assessment of
universities
through data
envelopment
analysis. Elsevier
Ltd.
Cost With high enrollment rates and limited funding in public
universities, it has become difficult to achieve high degrees of
efficiency in the institutions. Therefore various performance
tools are necessary to ensure success. One such method is
Data Envelopment Analysis (DEA). The DEA model has
become popular for measuring success in non–profit
institutions. The model consists of 16 inputs and outputs to
measure the performance of universities by looking at
teaching and research activities.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 162
Courtioux, P. 2012.
How income
contingent loans
could affect the
returns to higher
education: a micro
simulation of the
French Case.
Education
Economics.
Cost Income contingent loans (ICL) have been used in many
countries, including Australia and the U.K. ICL’s aim to
provide more financial resources to make up for the shortage
of credit for students from low income backgrounds. The ICL
scheme makes sure that tuition fees are paid through money
borrowed from state. However, the borrower has the power to
control how and when the debt is recollected. Once the
borrower earns above a certain income limit, they are required
to pay each a fixed part of the income to pay off the debt.
Pigini, C &
Staffolani, S. 2015.
Beyond
participation:do the
cost and quality of
higher education
shape the
enrollment
composition? The
case of Italy.
Springer
Science+Business
Media Dordrecht.
Cost Higher Education systems in countries like Italy are relatively
different to the U.S In Italy, participation and graduation rates
are lower and reforms are now restructuring the system. The
issue of low participation has sparked debates on whether
there is a needs for geographical redistributions, restructuring
tuition fees and grants and having more quality control tools.
Studies conduct through this paper, have shown that lowering
the tuition costs and increasing the geographical distribution
of higher education has increased enrollment rates. This new
strategy will further help the economically disadvantage to
pursue a higher education. However, not all countries can
afford these cost reductions. Countries with on going financial
crisis will have no option but to raise tuition fees to make up
for shortages, thus discouraging more enrollments in HEIs.
Hilton, L.J.,
Robinson J.T.,
Wiley.D &
Ackerman, D. J.
2014. Cost Savings
Achieved in Two
Semesters Through
the Adoption of
Open Educational
Resources. The
International
Review of Research
in Open and
Distant Learning
Cost The article examines the open education initiative known as
Kaleidoscope Open Course Initiative (KOCI). KOCI is a
learning project funded to eliminate high textbook costs,
improve the quality of courses and encourage collaborative
learning amongst students. Studies conducted indicate, that
open educational resources (electronic sources as opposed to
traditional tangible textbooks) have the ability to save money
for students and support them thought grants and loans. A full
time student would essentially spend over $900 on textbook
per year. With an open educational resource strategy, the cost
becomes a zero.
Wellman, V.J.
2010. Improving
Data to Tackle the
Higher Education
“Cost Disease.”
Society for College
and University
Planning.
Cost The financial crisis has brought chaos to the U.S HEIs.
Between 2008–2010 budget cuts, lay off and rising tuition
costs have create a cost disease in the system. This recession
not only affected public institutions, it further threatened
private universities like Harvard, Stanford and Yale. These
prestigious colleges lost many endowment revenues; nearly
30% was lost during the crisis.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 163
Cost disease is where cost increases and unavoidable as labor
costs can be reduced without harming the quality, therefore as
labor costs increase the overall costs rise (William Baumol
and William Bowen, 1966). To tackle cost disease, there
needs to be more access to public data (revenues and spending
rates), creating more transparent metrics, spending more
money on research to understand the correlation between
spending and performance and the relationship between
spending and degree productivity.
Taylor, J.B &
Morphew, C.C.
2015. Trends in
Cost–sharing in the
US and Potential
International
Implications.
Higher Education
Policy
Cost The principle of cost sharing would help bring investments
from various contributors and increase the total resources
available to HEIs (McMahon, 2009). The idea behind this
principle is to attract more financial support from
governments, private sector donations and parents and
students (Johnston, 2004). Cost sharing has been in practice
since the establishment of Harvard in 1636. The system in
place, used to provide HEI with high government support and
thus public institutions charged low tuition fees. However, in
subsequent decades the government support has decreased,
forcing public institutions to raise the costs (Wellman, 2008).
Oreopoulos, P &
Petronijevic, U.
2013. Making
College Worth It:
A Review of the
Returns to Higher
Education. Future
of Children.
Return On
Investment
The rise in earnings linked with college completion varies
over time. Primarily individuals with post– baccalaureate
degrees receive increased earnings. Earnings also vary
depending on different college majors. Since 1980s with more
technology driven changes to the American labor market, the
demand for tech jobs are increasing.
The Economists,
2012. Higher
Education – Not
what it used to be.
Return On
Investment
From 2001–2010 the cost of education at universities rose
from 23% to 38%, and the student debt doubled within the
past 15 years. Around two–thirds of graduates take out loans.
Archibald, B.R &
Feldman, H.D.
2008. Explaining
Increases in Higher
Education Costs.
The Journal of
Higher Education.
Cost In June 2005, Secretary of Education Margaret Spellings
started a National Commission on the Future of Higher
Education. The commission was established to look into the
costs and accountability in HEIs. However, the is yet a lack of
consensus and understanding about the forces behind the two
factors.

In 1996, David Breneman stated that there are two competing
theories that can explain the rise of costs in higher education.
The first theory is based on the cost difficulties experienced
by personal service industries (referring back to cost disease).
Howard Bowen’s “revenue theory of cost” views the source of
costs increases in the increments in revenue streams made
available to HEIs. These institutions “spend everything they
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 164
can raise, so revenue is the only constraint on cost”
(Archibald, B.R & Feldman, H.D. 2008, p. 269).
Ranalli, G.M &
Gnaldi, M. 2015.
Measuring
University
Performance by
Means of
Composite
Indicators: A
Robustness
Analysis of the
Composite
Measure Used for
the Benchmark of
Italian Universities.
University of
Perugia
Total Quality
Management
Composite indicators are a useful tool for analyzing
performance information, including university rankings. This
communication tool is used in various public sectors. In Italy,
they are used to allocate a quota of public funding to the
colleges. However, the value of composite indicators are still
under debate.
Altinay, F., Dagli,
G., Altinay.A &
Basari, G. 2016.
Assessment of the
Quality
Management
Models in Higher
Education. Journal
of Education and
Learning.
Total Quality
Management
The most vital factor in quality in HEIs is the academic staff.
The staff’s ability to do a good job is impacted by the physical
infrastructure. Furthermore, the type of services provided for
the community needs to be analyzed. In addition, the global
“accreditations earned by the institutions in an unrecognized
university” are known to be an important step towards quality.

There are numerous negative factors that affect the quality of
HEIs, such as the heavy workload the academic staff takes on.
This workload decreases the effectiveness of the staff and thus
the quality of teaching.
Svensson, C.,
Essa–Ba, M.,
Bakhsh, M &
Albilwi,S. 2018. A
Lean Six Sigma
Program in Higher
Education.
International
Journal of Quality
& Reliability
Management.
Total Quality
Management
There are numerous HEIs implementing LSS, such as “St.
Andrews University, Cardiff University, Central Connecticut
State University, MIT and more” (Anthony, 2014). However,
there is a still a low number of HEIs deploying LSS, this is
due to the view that LSS is only successful in the
manufacturing sector.

There is increasing awareness about the benefits of LSS in
HEIs, including the rise in student satisfaction, changing
culture, reducing hidden costs and increasing efficiency.
Marksberry, P.
2011. The Toyota
Way– a
quantitative
approach.
International
Total Quality
Management
Toyota believes that “respect is the most critical item” needed
to run a successful business. Every internal stakeholder must
show respect to his or her job, this respect has led Toyota to
be the forerunner in Lean manufacturing for years. Thus the
company’s culture and leadership has enabled a successful
deployment of Lean into their process.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 165
Journal of Lean Six
Sigma.
Anthony, J
&Psomas, E. 2017.
Total Quality
Management
Elements and
Results in Higher
Education
Instituitons.
Quality Assurance
in Education.
Total Quality
Management
A TQM framework for HEIs should include the following
elements and measure the performance of these elements to
ensure quality:

• Leadership and top management commitment
• Strategic Quality Planning
• Teaching Staff and employee involvement
• Supplier management
• Student focus
• Process management (education, research and
administration)
• Continuous improvement
• Quality data
• Knowledge and education
Shams, R.S.M.
2017.
Transnational
education and total
quality
management: a
stakeholder–
centred model.
Journal of
Management
Development.
Total Quality
Management
In 2008, a study showed that the demand for higher education
is expected to increase from 99 million (current) to 414
million by 2030. Therefore in the academic sector, quality
assurance needs to be a key concern. For transnational
education (TNE), there is a different learning environment and
there is a lack of accountability in these programs compared
to onshore ones. Therefore is a growing need for a TQM
framework for TNE’s.

Prasad, R.V.S.
2017. Total Quality
Management in
Higher Education.
Surragh
Publishers.
Total Quality
Management
Research suggests that each HEI must have a customized
model for the application of TQM. However, the every model
should include the following step by step characteristics:

• Step 1– Awareness and commitment of top authorities
about TQM
• Step 2–Setting up the groups (controllers, facilitators,
Quality Improvement Teams) formally
• Step 3– Identify team leaders and facilitators
• Step 4–Train the identified members
• Step 5–Strengthening Quality Improvement Teams
• Step 6–Train Quality Improvement Teams
• Step 7–Sample the Implementation
• Step 8–Review of sample and TQM Implementation
• Step 9– Enforce full fledged TQM Implementation
throughout the Educational Institution.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 166
Kassem–Al, H.A
&In’airat, H.M.
2014. Total Quality
Management in
Higher Education:
A Review.
Macrothink
Institute.
Total Quality
Management
There numerous advantages of implementing TQM into HEIs,
including the following:

• TQM will provide guidance to HEIs to initiate an
upgraded service to its key stakeholders such as
students and faculty
• Continuous improvement will satisfy the
accountability essential to educational reform
• TQM will empower students and teachers to embrace
change and take on new challenges
Anderson, E. 1995.
High Tech v high
touch: a case study
of TQM
implementation in
higher education.
Managing Service
Quality: An
International
Journal.
Total Quality
Management
Student Service Offices in HEIs can improve service quality
by:

• Encouraging interdepartmental support and co–
operation
• Communicating constantly with students, faculty and
staff
• Providing employees the support and authority to act
effectively.
Chockalingam, R.,
Gurumurthy,A &
Narayanamurthy,G.
2017. Applying
Lean Thinking in
an Education
Institute–an action
research.
International
Journal of
Porductivity and
Performance
Management.
Total Quality
Management
Lean thinking has been used in numerous manufacturing and
service sectors, however there is few research indicating any
framework for using Lean thinking in education institutes.
There a various elements for the framework of Lean Thinking
implementation in an educational institute, including:

• Understand the holistic picture of the whole
organization, the functional aspects, and processes.
• Identify the different types of wastes
• Locate the Lean solutions to eradicate the waste

Tambi., A.B.,
Malek, A & Kanji,
K.G. 1999. Total
Quality
Management in
UK Higher
Education
Institutions. Total
Quality
Management.
Total Quality
Management
There are numerous casual reasons for implementing quality
management into HEIs, such as:
• To remain competitive
• Increase stakeholder satisfaction
• Image building
• Raise market share
• Promote teamwork
• Increase student performance
• To better use resources and resolve current problems
• Managing change
• Using feedback to improve current issues
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 167
McDermott, M.C
& Prajogo, D.
2016. The
relationship
between total
quality
management
practices and
organizational
culture.
International
Journal of
Operations and
Production
Management.
Total Quality
Management
Different types of organizational cultures determine different
TQM practices. The hierarchical organizational culture seems
to have a significant relationship with TQM.
Misra, B.R. &
Ravinder, H. 2016.
The Treatment of
Six Sigma In
Introductory
Operations
Management
Textbooks:
Clearing Up the
Confusion. The
Clute Institute.
Lean Six
Sigma
Six Sigma programs aim to achieive high output levels, for
instance, there is around 3.4 defective units out of every
million units produced (Hoerl, 1998). However, the most vital
aspect of Six Sigma programs is the organizational approach
to identify and solve problems. This approach is “relies on
trained problem–solvers using defined processes” to improve
quality.
Alrifai, B.N. 2008.
Optimizing a Lean
Logistics System
and the
Identification of its
Breakdown Points.
University of
Southern
California.
Lean Six
Sigma
Taiichi Ohno developed the Toyota Lean Manufacturing
System to enhance the quality of the company’s products. The
objective was to increase profits and productivity by
eliminating costs. Toyota achieved this goal by:

Eliminating all forms of Muda (Waste)
Designing out Muri (overburden)
Designing out Mura (inconsistency)

The Lean Manufacturing System was based on the following
principles:

• Just in time manufacturing
• Jidoka (machines stop production as soon as they
identify a defect
• Kaizen (continuous improvement)
• Challenging the status quo
• Teamwork
• Respect
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 168
Holmes, C.M.,
Kumar, A &
Jenicke, L.O. 2008.
A framework for
applying six sigma
improvement
methodology in an
academic
environment. The
TQM Journal.
Lean Six
Sigma
There are numerous challenges of applying six sigma to HEIs,
such as:

Students could be viewed as a product and the customer,
therefore a completed product would mean a graduate. The
definition would not fit well with the process

Measurements of quality are more difficult in educational
environments.

The reward systems applied in businesses cannot be used in
educational settings

In an academic setting, influences are often beyond control,
for instance a student success may depend on personal
motivation, family pressures etc.
Kumar, M., Cullen,
D., Krishan, N &
Anthony, J. 2017.
Lean Six Sima for
higher education
institutions:
Challenges,
barriers, success
factors, and
tools/techniques.
International
Journal of
Productivity and
Performance
Management.
Lean Six
Sigma
Barriers in the use of Lean Six Sigma in Higher Education:

Tools and techniques used in manufacturing is different to
education
There is a lack of process thinking and ownership that needs
to be addressed first
Lack of visionary leadership (empowering employees etc)
Lack of a open communication between stakeholders
Less knowledge about the different types of customers
Few resources available, such as time and budget.
Comm, C.L &
Mathaisel, F.X.D.
2017b. A case
study in applying
Lean sustainability
concepts to
universities.
International
Journal of
Sustainability in
Higher Education.
Lean Six
Sigma
For Lean initiatives to be successful, they need to have the
following components:

• The change environment needs to be proactive rather
than reactive (Womack and Jones, 1996).
• Change must come from “a bottom up” approach,
empowering employees.
• The culture must be open, respectful and honest,
where employees feel they have the freedom to voice
their opinions.
• Providing proper training to establish quality service

Francis, E.D. 2014.
Lean and the
learning
Total Quality
Management
Francis provided 5 recommendations when pursuing Lean
implementation that organizations or higher education
institutions must consider:
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 169
organization in
higher education.
Canadian Journal
of Educational
Administration and
Policy.

1) A strong executive leadership, who understands the
complexity of Lean and how to apply them to their objectives.
2) Lean implementation would need training of staff and
development of programs to establish quality
3) Knowledge management
4) Information technology systems should be geared towards
ensuring information sharing and collaboration of results.
5) Use Lean consultants to build a strategic plan to execute the
implementation of Lean practices.
Emiliani, M.L.
2005. Using
Kaizen to improve
graduate business
school degree
programs. Quality
Assurance in
Education.
Total Quality
Management
The study conducted in business school degree programs
indicated that the kaizen process resulted in “rapid
improvement without creating undesirable trade–offs” that
may negatively affect stakeholders. Negative impacts
including, the academic and student freedom or value. Since
student’s idea of value changes over time, Kaizen must be
repeated in regular intervals by utilizing relevant and updated
data.
Douglas, A.,
Anthony, J &
Douglas, J. 2017.
Waste
Identification and
Elimination in
HEIs: the role of
Lean Thinking.
International
Journal of Quality
& Reliability
Management.
Lean Six
Sigma
Lean movement has 8 categories of waste (Duffy and Wong,
2013). :

• Excess motion
• Excess transportation
• Underutilized people
• Inventory
• Defects
• Over production
• Waiting
• Over processing
Keenan, P., Noone,
T., Curtis, E &
Redmond, R. 2008.
Quality in higher
education. The
contribution of
Edward Deming’s
principles.
International
Journal of
Educational
Management.
Total Quality
Management
Deming’s recommends that organizations must undergo
fundamental change in order to survive in the current
marketplace. Thus, quality is the most vital component.
Lakshminarayanan,
R. 2014. Necessity
of Six Sigma–As a
Measurement
Lean Six
Sigma
The most common measurements for quality in Six Sigma
results are as follows:

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 170
Metric in
Measuring Quality
of Higher
Education.
Research Gate.
1) Compare the university’s performance with similar rival
programs.
2) Set up performance goals to achieve higher targets based
on the “current level of performance” to measure continuous
performance.
3) Set up bench mark goals to reach higher standards
4) Eliminate ambiguity to bring more clarity to stakeholders.
Fisher, R., Francis,
M., Anthony, J &
Thomas, J.A.2015.
A comparative
study of Lean
implementation in
higher and further
education
institutions in the
UK. International
Journal of Quality
and Reliability
Management.
Lean Six
Sigma
Research conducted indicate that HEIs in the UK were more
“likely to develop holistic strategies to implement Lean” due
to the institutions having improved their time and support
resources. However Further Education Institutions seems to
have more experience and knowledge of creating and
executing programs for improvement.
Anthony, J. 2014.
Readiness factors
for the Lean Six
Sigma journey in
the higher
education sector.
International
Journal of
Productivity and
Performance
Management.
Lean Six
Sigma
If an organization is prepared for using LSS, they should show
more of these attributes:

• Employees have a “can do attitude” and are motivated
intrinsically to reach new goals.
• Willingness to take risks when necessary
• Having a healthy and positive environment for change
• Leaders have the ability to provide adequate resources
and credit employees for achievement
• Management makes decisions based on facts and data
but not on “gut–feeling.”
• The goals established for LSS are measureable,
relevant and aligned with the organization’s goals.
• Have culture for collecting the necessary data to drive
performance.
Mehrotra, D. 2008.
Implementing Six
Sigma in Education
Towards TQM in
Academics.
SChand
Publications.
Lean Six
Sigma
There a considerable amount of effort that goes into
translating TQM to education:

• TQM identifies students as both customers and
employees of the education system. Administrators
need to “involve students in their training to question
the learning process
• TQM encourages teachers to view education through
the students’ eyes.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 171

• Not using standardized tests to measure a student’s
progress, TQM encourages teachers to assess the
performance regularly.
Rusinko, A.C.
2005. Using quality
management as a
bridge in educating
for sustainability in
a business school.
International
Journal of
Sustainability in
Higher Education.
Total Quality
Management
Quality management can be used to bridge the gap between
“management theory and environmental sustainability.” It can
be used as a framework for teaching sustainability in
management classes.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 172
APPENDIX G
SURVEY QUESTIONS AND ITS RELATIONSHIP STAGE
Question KM
O
Attri
butes
Pre
–
Lau
nch
Sta
ge
Cur
rent
Sess
ion
Stag
e
Po
st
St
ag
e
1. Your Role:
Lean Partner ()
Lean Manager ()
Lean Senior administrator ()
None ()
O X X X
2) How Long have been at the Miami University? O X X X
3) How many years of prior TQM, Lean, and Six Sigma
experience did you have before MU?
K X
4) How many years of prior TQM, Lean, and Six Sigma
experience did you have before MU?
K X
5) I believe MU–Lean has been beneficial to cost–containment
initiative for the university.
O X X X
6) As a certified MU–Lean participant, I have all the necessary
tools to work on my process improvement initiatives.
M X X X
7) As a certified MU–Lean participant, I have been trained to
execute projects.
K, M X X X
8) It is important for MU–Lean to continue to deliver the cost
savings for the next 10 years as a part of the cost–containment
initiative.
O X
9) I am recognized for my efforts for working on MU–Lean
projects.
O X X X
10) I am rewarded for my efforts for working on MU–Lean
projects.
M, O X X
11) I am recognized for my efforts for leading MU–Lean projects. O X
12) I am rewarded for my efforts for leading MU–Lean projects. M X
13) I am recognized for my efforts for managing MU–Lean
projects.
O X X
14) I am rewarded for my efforts for managing MU–Lean projects. O X
15) I have the full support from my manager to work on MU–Lean
projects.
O X
16) I feel confident in my ability to initiate the MU–Lean projects. K, M X
17) I feel confident in my ability to execute the MU–Lean
projects.
K, M X X X
18) I feel confident in my ability to sustain the MU–Lean projects. K, M X X
19) There is a receptiveness for new ideas.

O X X X
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 173
20) There is a receptiveness for change management. M, O X X X
21) There is a need for Higher Education Institutes to create cost–
containment initiative(MU–Lean).
M X X X
22) Students see the value in MU–Lean initiatives. M X
23) MU–Lean has been able to collaborate with faculties. O X X X
24) MU–Lean has been able to collaborate with various
department.
25) MU–Lean has been able to collaborate with administrator.

Interview Questions and Its Relationship Stage
Question KMO
Attributes
Pre–
Launch
Stage
Current
Session
Stage
Post Stage
1) How long
have you
been at the
MU?
K, O X X
2) What is
your current
role and how
long have you
been in it?
O X X
3) How long
have you
been in the
current role?
K X
4) How long
have you
been
affiliated with
TQM, Lean,
and Six
Sigma
practice?
K X
5) Can you
tell me about
the
circumstances
that led MU
to
conceptualize
the MU–Lean
initiative?
K, M, O X
X
X

X
X

X
X

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 174
6) How did
the recession
of 2008
influence on
the launch of
MU–Lean?
O X
7) What
influence
from the
drastic cuts in
funding from
the state had
in the launch
of the MU–
Lean?
M, O X
8) How did
the MU–Lean
idea come to
shape?
O X
9) What past
experiences
in any quality
management
organizations
help support
your role
during pre–
launch phase
of MU–Lean?
K, M X X X
10) What
kind of
resources was
available for
you to during
the pre–
launch phase
of the MU–
Lean
initiative, if
anything?
O X
11) Where
did you gain
the essential
knowledge of
Quality
Management
K, M X
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 175
system for the
pre–launch
phase of MU–
Lean
initiative?
12) Can you
share
thoughts
regarding of
if you were to
restart, what
would you do
more and less
of?
K, M, O X X X
13) How does
MU–Lean
certification
work?
K, O X
14) During
the
sustainment
phase, what
support did
you receive
regarding the
resources,
change
management,
various
stakeholder
group to
collaborate
with the MU–
Lean
initiative?
K, M X
15) Why do
think it is
essential for
the Mu to
continue their
efforts on the
MU–Lean
initiative and
is it
sustainable?
K, M X
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 176
16) How do
Lean
administrators
support
continues
enhancement
for Lean
administrators
(LA)?
O X X X
17) What
steps are
taken to
ensure the
Lean partners,
Lean leaders,
and Lean
administrators
feel more
confident and
comfortable
with the MU–
Lean
initiative?
M, O X X X
18) What
steps are
initiated to
ensure the
sense of
belonging and
ownership is
instilled to
make MU–
Lean as a part
of the self.
M, O X X X

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 177
APPENDIX H
ABBREVIATIONS

COPQ

Cost of Poor Quality
CT&VA Cycle Time and Value Analysis
CTQ Critical to Quality
CVA Customer Value Analysis
DMAIC Define, Measure, Analyze, Improve, Control
DMADV Define, Measure, Analyze, Design, Verify
DISC Define, Investigate, Streamline, Control
DPMO Defects per Million Opportunities
DPU Defects per Unit
FMEA Failure Modes and Effects Analysis
LSL Lower Specification Limit
NVA Non–Value–Added
QFD Quality Function Deployment
ROI Return on Investment
SIPOC Suppliers, Inputs, Process, Outputs, Customer
SME Subject Matter Expert
SPC Statistical Process Control
SSD Six Sigma Design
SSI Six Sigma Improvement
SSPM Six Sigma Process Management
USL Upper Specification Limit
VA Value–Added
VE Value–Enabling
VOC Voice of the Customer

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 178
APPENDIX I
CHECKLIST

Phase 1: Lean Initiative

Sequence 1

¨ Perform feasibility /Organizational Readiness assessment
¨ Create Baseline Knowledge Attributes Inventory
¨ Execute Stakeholder Analysis
¨ Create Baseline Motivation Attributes Inventory
¨ Create Baseline Organization Attributes Inventory

Sequence 2

¨ Perform the impact of training program, project launch, and team morale
¨ Revalidate project dashboard against a baseline assessment of organization key
performance indexes.

Sequence 3

¨ Validate performance against the baseline Feasibility /Organizational Readiness
assessment:
¨ Perform a robust assessment of baseline assessment of organization key performance
indexes.
¨ Revalidate Knowledge Attributes
¨ Revalidate Motivation Attributes Solution
¨ Revalidate Organization Attributes Solution

Checklist

Phase 2: Six Sigma Initiative

Sequence 1

¨ Baseline Knowledge Attributes
¨ New Baseline (Post phase 1) Motivation Attributes
¨ New Baseline (Post phase 1) Organization Attributes
¨ Training Program for Six–Sigma
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 179
¨ Create a baseline assessment of organization key performance indexes. It is important to
have a clear inventory of organization’s current state before initiating the HEES
implementation plan.
Sequence 2
¨ Create new knowledge attributes
¨ Create new motivation attributes solution
¨ Create new organization attributes solution

¨ Knowledge Attributes
¨ Motivation Attributes Solution
Sequence 3

¨ Lean Enterprise System (LES) Validation

Phase 3: Advance Data Analytics, AI ML, and RPA Initiative

¨ Create a baseline data assessment of organization systems and processes.

¨ Provide survey instrument that measures the knowledge in Advance Data Analytics, AI
ML, and RPA

¨ Baseline Knowledge Attributes
¨ Baseline Motivation Attributes
¨ Baseline Organization Attributes

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 180
APPENDIX J
PROJECT CHARTER WORKSHEET
Project Title: Reducing number of missing documents in student admission portal
Project Leaders: Head of Admissions Team Members:

Lean Manager
Lean Partner
Lean Senior Administrator
Lean Certified Associate
Student recommendation
body
Business Case:

Currently over 18% of student applications are missing some
documentation, preventing timely processing between the
receipt of the documents/verifications and linking to the right
account, the process
appears to fail.

In order for this goal to come to fruition, the project team should do
the following:

a. Develop a delivery model that allows for an excellent
student experience that can be replicated in other specialties
and settings.
b. Create an excellent place for student application process and
timely admissions decisions.
c. Future state recommendation should not include large
capital investment.
Problem/Opportunity Statement:

Current student application system that consists of web portal and
manual mail service requires tremendous manual steps, delays and
errors causing request for second and third set of documents.
Goal Statement: The goal
is to create a system that
delivers an excellent
student experience
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 181
Project Scope, Constraints, & Assumptions:

This project will address the following:
1. Review of current processes and design new processes to
improve efficiency.

Assumptions:
- We have the appropriate amount of time to implement the
blue print
- We have the personnel resources to implement the blue
print
- We have the financial resources within the means to
implement the blue print
- We have the IT infrastructure to implement the blue print
- We have the tools to monitor progress and success after the
blue print has been implemented
- We have the data to effectively evaluate the implementation
of the blue print
Constraints:
- Department is open to considering new options with
ongoing work load
- Extraordinary expenses/budgetary requirements
- Competing organizational priorities

Stakeholders:

President office and
his/her cabinet
Provost and his
administrative cabinet
Dean’s cabinet at each of
the school
Faculty cabinet
Student council
Administrative heads
across the Universities
Others as deem necessary
based on the University
structure. Look for
budgeted $ spent and
determine the need.
Development/Axillary
services
Board of
Trustee/Governance
committee

Project Scope, Constraints, & Assumptions:

This project will address the following:
2. Review of current processes and design new processes to
improve efficiency.

Assumptions:
- We have the appropriate amount of time to implement the
blue print
- We have the personnel resources to implement the blue
print
- We have the financial resources within the means to
implement the blue print
- We have the IT infrastructure to implement the blue print
- We have the tools to monitor progress and success after the
blue print has been implemented
- We have the data to effectively evaluate the implementation
of the blue print
Constraints:
- Department is open to considering new options with
ongoing work load
- Extraordinary expenses/budgetary requirements
Stakeholders:

President office and
his/her cabinet
Provost and his
administrative cabinet
Dean’s cabinet at each of
the school
Faculty cabinet
Student council
Administrative heads
across the Universities
Others as deem necessary
based on the University
structure. Look for
budgeted $ spent and
determine the need.
Development/Axillary
services
Board of
Trustee/Governance
committee
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 182

- Competing organizational priorities

PRELIMINARY
PLAN
Target Date Actual Date
Start Date 01/01/2020
Define 02/01/2020
Measure 03/01/2020
Analyze 05/01/2020
Improve/Design 08/01/2020
Control/Validate 11/30/2020
Completion Date 12/31/2020
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 183
APPENDIX K
STAKEHOLDER ANALYSIS WORKSHEET

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 184
APPENDIX L
API Example

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 185
APPENDIX M
TABLES AND FIGURES
Table 17
Frequency Analysis for receptiveness for new ideas by survey participants

Percent
Strong Agree 13.2
Agree 43.8
Disagree 10.6
Strongly Disagree 2.5
Total 70.1
*N Valid – 303
Table 18
Paired t test between receptiveness for new ideas and receptiveness for change management

N Mean SD
There is receptiveness
for new ideas

302 2.04 .695
There is receptiveness
for change
management
302 2.18 .704

Table 19
Frequency Analysis for receptiveness for new change management by survey participants

Percent
Strong Agree 9.0
Agree 42.8
Disagree 15.0
Strongly Disagree 3.2
Total 70.1
*N Valid – 303

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 186
Table 20
Frequency Analysis for the need for Higher Education Institutes to create cost–containment
initiatives (MU–Lean) by survey participants

Percent
Strong Agree 25.0

Agree 41.0

Disagree 2.5

Strongly Disagree 1.6

Total 70.1
*N Valid – 303
Table 21
ANOVA Analysis for receptiveness to new ideas by participating stakeholders

N Mean SD F Value
Lean Partner

106 1.95 .695 1.859*
Lean Manager 27 2.22 .698

Lean Senior
Administrator

27

1.89

.577

Lean Certified
Associate

143

2.09

.711

*Note: P = 1.36

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 187
Table 22
ANOVA Analysis for receptiveness for change management by participating stakeholders

N Mean SD
Lean Partner

106 2.21 .727
Lean Manager 27 2.30 .724

Lean Senior
Administrator

27

1.89

.577

Lean Certified
Associate

143

2.19

.702

Table 23
ANOVA Analysis for need for Higher Education Institutes to create cost–containment initiatives
(MU–Lean) by the role of the participant

N Mean SD F Value
Lean Partner

106 1.67 .628 2.117*
Lean Manager 27 1.56 .506

Lean Senior
Administrator

27

1.63

.688

Lean Certified
Associate

143

1.82

.657

* Note: P= .098

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 188
Table 24
Frequency Analysis for Faculty seeing the value in MU–Lean by participating stakeholders

Percent
Strong Agree 3.2

Agree 36.1

Disagree 24.1

Strongly Disagree 6.5

*N Valid – 303
Table 25
Frequency Analysis for Top leadership seeing the value in MU–Lean by participating
stakeholders

Percent
Strong Agree 21.1

Agree 41.7

Disagree 6.5

Strongly Disagree .7

*N Valid – 303

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 189
Table 26
Frequency Analysis for participating stakeholder’s believing MU–Lean has been beneficial to
cost–containment initiative for the university

Percent
Strong Agree 24.5

Agree 50.2

Disagree 6.5

Strongly Disagree 3.5

*N Valid – 303
Table 27
ANOVA Analysis for MU–Lean being beneficial to cost–containment initiative for the university
by the role of the participant

N Mean SD F Value
Lean Partner

119 1.83 .763 2.177*
Lean Manager 28 1.71 .659

Lean Senior
Administrator

29

1.66

.614

Lean Certified
Associate

190

1.95

.703

* Note: P = .090

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 190
Table 28
ANOVA Analysis for confidence in ability to initiate the MU–Lean projects by the role of the
participant

N Mean SD F Value
Lean Partner

107 1.95 .605 9.247*
Lean Manager 27 1.74 .526

Lean Senior
Administrator

27

1.59

.636

Lean Certified
Associate

145

2.19

.710

* Note: P = .000
Table 29
ANOVA Analysis for confidence in ability to execute the MU–Lean projects by role of the
participant

N Mean SD F Value
Lean Partner

107 2.02 .549 10.860*
Lean Manager 27 1.85 .534

Lean Senior
Administrator

27

1.59

.636

Lean Certified
Associate

145

2.26

.685

* Note: P= .000

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 191
Table 30
ANOVA Analysis for confidence ability to sustain the MU–Lean projects by role of the
participant

N Mean SD F Value
Lean Partner

107 1.99 .541 8.374*
Lean Manager 27 1.96 .587

Lean Senior
Administrator

27

1.63

.565

Lean Certified
Associate

145

2.20

.619

* Note: P=.000
Table 31
ANOVA Analysis for confidence in ability to initiate the MU–Lean projects by the number of
years of Total Quality Management experience prior to Miami University

N Mean SD F Value
None

181 2.07 .691 .960*
0–2 years 49 2.00 .677

3–5 years

27

1.85

.662

6–10 years

22

1.91

.610

11+ years

27

1.93

.675

* Note: P=.430

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 192
Table 32
ANOVA Analysis for confidence in ability to sustain the MU–Lean projects by number of years
of Total Quality Management experience prior to Miami University

N Mean SD F Value
None

181 2.11 .623 1.584*
0–2 years 49 2.02 .520

3–5 years

27

1.93

.550

6–10 years

22

1.82

.501

11+ years

27

2.07

.730

* Note: P=.179
Table 33
ANOVA Analysis for confidence in ability to execute the MU–Lean projects by number of years
of Total Quality Management experience prior to Miami University

N Mean SD
None

181 2.15 .654
0–2 years 49 2.02 .629

3–5 years

27

1.89

.577

6–10 years

22

2.00

.690
11+ years

27

1.96

.706

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 193
Table 34
ANOVA Analysis for confidence in ability to sustain the MU–Lean projects by the number of
years of Lean experience prior to Miami University

N Mean SD
None

246 2.10 .594
0–2 years 25 2.00 .408

3–5 years

15

1.73

.704

6–10 years

11

1.55

.522
11+ years

9

2.11

.928

Table 35
ANOVA Analysis for confidence in ability to execute the MU–Lean projects by the number of
years of Lean experience prior to Miami University

N Mean SD F Value
None

246 2.12 .621 4.121*
0–2 years 25 2.16 .688

3–5 years

15

1.73

.799

6–10 years

11

1.45

.522

11+ years

9

2.00

.866

* Note: P=.003

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 194
Table 36
ANOVA Analysis for confidence in ability to initiate the MU–Lean projects by the number of
years of Six Sigma experience prior to Miami University

N Mean SD
None

265 2.05 .672
0–2 years 20 1.90 .641

3–5 years

10

2.00

.816

6–10 years

7

1.43

.535
11+ years

4

1.25

.500

Table 37
ANOVA Analysis for confidence in ability to sustain the MU–Lean projects by number of years
of Six Sigma experience prior to Miami University

N Mean SD F Value
None

265 2.09 .592 2.785*
0–2 years 20 1.90 .553

3–5 years

10

1.90

.568

6–10 years

7

1.57

.787

11+ years

4

1.50

1.000

* Note: P=.027

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 195
Table 38
ANOVA Analysis for confidence in ability to execute the MU–Lean projects by the number of
years of Six Sigma experience prior to Miami University

N Mean SD
None

265 2.13 .633
0–2 years 20 1.80 .523

3–5 years

10

1.90

.876

6–10 years

7

1.57

.787
11+ years

4

1.50

1.000

Table 39
ANOVA Analysis for certified MU–Lean participant who have all the necessary tools to work on
my process improvement initiatives by the role of the participant

N Mean SD F Value
Lean Partner

119 1.91 .611 6.891*
Lean Manager 28 1.93 .378

Lean Senior
Administrator

29

1.69

.660

Lean Certified
Associate

190

2.14

.631

* Note: P= .000

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 196
Table 40
Frequency Analysis for certified MU–Lean participant who have trained to execute projects

Percent
Strong Agree 14.1

Agree 48.1

Disagree 13.7

Strongly Disagree 2.1

Total 78.0
*N Valid – 337
Table 41
Frequency Analysis for certified MU–Lean participant who have all the necessary tools to work
on process improvement initiatives

Percent
Strong Agree 14.1

Agree 57.2

Disagree 11.6

Strongly Disagree 1.9

Total 84.7
*N Valid – 366

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 197
Table 42
Frequency Analysis for participants recognized for efforts for working on MU–Lean projects

Percent
Strong Agree 10.6

Agree 40.0

Disagree 21.8

Strongly Disagree 5.6

Total 78.0
*N Valid – 337
Table 43
Frequency Analysis for participants recognized for efforts for leading on MU–Lean projects

Percent
Strong Agree 7.6

Agree 39.8

Disagree 22.0

Strongly Disagree 5.1

Total 74.5
*N Valid – 322

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 198
Table 44
Frequency Analysis for participants rewarded for efforts for leading on MU–Lean projects

Percent
Strong Agree 6.5

Agree 35.6

Disagree 26.2

Strongly Disagree 6.3

Total 74.5
*N Valid – 322
Table 45
Frequency Analysis for participants recognized for efforts for managing on MU–Lean projects

Percent
Strong Agree 6.7

Agree 39.4

Disagree 22.9

Strongly Disagree 5.6

Total 74.5
*N Valid – 322

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 199
Table 46
Frequency Analysis for participants rewarded for efforts for managing on MU–Lean projects

Percent
Strong Agree 5.6

Agree 37.0

Disagree 25.9

Strongly Disagree 6.0

Total 74.5
*N Valid – 322

Table 47
Frequency Analysis for participants who have the full support from manager to work on MU–
Lean projects

Percent
Strong Agree 21.3

Agree 41.9

Disagree 6.3

Strongly Disagree 1.4

Total 70.8
*N Valid – 306

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 200
Table 48
ANOVA Analysis for participating stakeholders who have the full support from manager to work
on MU–Lean projects

N Mean SD F Value
Lean Partner

107 1.74 .619 1.455*
Lean Manager 27 1.85 .770

Lean Senior
Administrator

27

1.74

.712

Lean Certified
Associate

145

1.90

.660

* Note: P= .227
Table 49
ANOVA Analysis for confidence in ability to execute the MU–Lean projects by number of years
of Total Quality Management experience prior to Miami University

N Mean SD F Value
None

181 2.15 .654 1.496*
0–2 years 49 2.02 .629

3–5 years

27

1.89

.577

6–10 years

22

2.00

.690

11+ years

27

1.96

.706

* Note: P= .203

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 201
Table 50
ANOVA Analysis for confidence in ability to initiate the MU–Lean projects by number of years
of Lean experience prior to Miami University

N Mean SD F Value
None

265 2.05 .672 2.995*
0–2 years 20 1.90 .641

3–5 years

10

2.00

.816

6–10 years

7

1.43

.535

11+ years

4

1.25

.500

* Note: P= .019
Table 51
ANOVA Analysis for confidence in ability to sustain the MU–Lean projects by the number of
years of Lean experience prior to Miami University

N Mean SD F Value
None

246 2.10 .594 3.549*
0–2 years 25 2.00 .408

3–5 years

15

1.73

.704

6–10 years

11

1.55

.522

11+ years

9

2.11

.928

* Note: P= .008

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 202
Table 52
ANOVA Analysis for confidence in ability to execute the MU–Lean projects by the number of
years of Lean experience prior to Miami University

N Mean SD F Value
None

246 2.12 .621 4.121*
0–2 years 25 2.16 .688

3–5 years

15

1.73

.799

6–10 years

11

1.45

.522

11+ years

9

2.00

.866

* Note: P= .003
Table 53
ANOVA Analysis for confidence in ability to initiate the MU–Lean projects by the number of
years of Six Sigma experience prior to Miami University

N Mean SD F Value
None

265 2.05 .672 2.995*
0–2 years 20 1.90 .641

3–5 years

10

2.00

.816

6–10 years

7

1.43

.535

11+ years

4

1.25

.500

* Note: P= .019

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 203
Table 54
Frequency Analysis for Students seeing improvement in learning outcomes through MU–Lean
initiative

Percent
Strong Agree 2.8

Agree 31.0

Disagree 28.7

Strongly Disagree 7.4

Total 69.9
*N Valid – 302
Table 55
Frequency Analysis for Faculty seeing improvement in learning outcomes through MU–Lean
initiative

Percent
Strong Agree 2.8

Agree 33.8

Disagree 26.4

Strongly Disagree 6.9

Total 69.9
*N Valid – 302

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 204
Table 56
Frequency Analysis for Top leadership seeing improvement in learning outcomes through MU–
Lean initiative

Percent
Strong Agree 12.7

Agree 41.2

Disagree 13.9

Strongly Disagree 1.6

Total 69.4
*N Valid – 300
Table 57
Frequency Analysis for MU–Lean has being able to collaborate with administrator

Percent
Strong Agree 12.0

Agree 50.5

Disagree 5.3

Strongly Disagree 1.6

Total 69.4
*N Valid – 300

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 205
Table 58
Frequency Analysis for participants rewarded for efforts for working on MU–Lean projects

Percent
Strong Agree 9.0

Agree 35.6

Disagree 25.9

Strongly Disagree 7.4

Total 78.0
*N Valid – 337
Table 59
Frequency Analysis for participants rewarded for leading on MU–Lean projects

Percent
Strong Agree 6.5

Agree 35.6

Disagree 26.2

Strongly Disagree 6.3

Total 74.5

*N Valid – 322

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 206
Table 60
One–Sample Analysis for the most relevant and important parts of MU–Lean

N Mean SD t value Sig. (2–
tailed)
TQM Principles

291 3.18 1.039 2.989 .003
Six Sigma Principles 291 3.24 1.121 3.607 .000

Internal Comraderies

291

3.63

1.080

9.880

.000

Communication

291

4.34

.984

23.166

.000
Management
Support/Collaboration
291 4.10 1.039

18.115 .000

Training and
Coaching

291

4.02

1.050

16.577

.000

Recognition and
Reward

291

3.79

1.099

12.269

.000

Drive Cost–
Containment Efforts

291

3.77

1.044

12.521

.000

Impact on Learning
Outcome

291

3.54

1.090

8.447

.000

Lean Principles

291

3.84

1.080

13.193

.000
Team Building 291 4.02 1.094 15.974 .000

Project Management

291

3.97

1.012

16.343

.000
Clearly Defined
Project Charter
291 4.07 1.069 17.107 .000

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 207
Table 61
One–Sample Analysis for the level of importance of emerging technologies for the future of
higher education excellence

N Mean SD t value Sig. (2–
tailed)
Robotic Process
Automation

35 3.31 1.301 1.429 .162
Predictive and
Descriptive Data
Analytics
35 4.49 1.011 8.695 .000

Machine Learning

35

3.54

1.120

2.866

.007

Artificial Intelligence

35

3.37

1.374

1.599

.119

Table 62
ANOVA Analysis for Lean Principles that are most relevant part of MU–Lean by participating
stakeholders

N Mean SD F Value
Lean Partner

102 4.05 1.028 4.199*
Lean Manager 27 3.81 1.039

Lean Senior
Administrator

27

4.15

1.064

Lean Certified
Associate

136

3.61

1.090

* Note: P= .006

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 208
Table 63
ANOVA Analysis for Internal comraderies being a most relevant part of MU–Lean by the role of
the participant

N Mean SD F Value
Lean Partner

102 3.77 1.014 2.677*
Lean Manager 27 3.44 1.086

Lean Senior
Administrator

27

3.96

1.055

Lean Certified
Associate

136

3.48

1.109

* Note: P = .047
Table 64
ANOVA Analysis for Cost–containment efforts being a most relevant part of MU–Lean by the
role of the participant

N Mean SD F Value
Lean Partner

102 3.88 .968 1.920*
Lean Manager 27 3.59 .931

Lean Senior
Administrator

27

4.07

1.072

Lean Certified
Associate

136

3.66

1.104

* Note: P= .126

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 209
Table 65
ANOVA Analysis for Team building being a most relevant part of MU–Lean by the role of the
participant

N Mean SD F Value
Lean Partner

102 4.19 1.002 2.214*
Lean Manager 27 3.74 1.023

Lean Senior
Administrator

27

4.26

.984

Lean Certified
Associate

136

3.92

1.174

* Note: P= .087
Table 66
ANOVA Analysis for Project management being a most relevant part of MU–Lean by the role of
the participant

N Mean SD F Value
Lean Partner

102 4.16 .920 2.898*
Lean Manager 27 3.93 .958

Lean Senior
Administrator

27

4.19

1.039

Lean Certified
Associate

136

3.80

1.060

* Note: P= .035

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 210
Table 67
ANOVA Analysis for Clearly Defined Project Charter being a most relevant part of MU–Lean by
the role of the participant

N Mean SD F Value
Lean Partner

102 4.16 1.012 1.952*
Lean Manager 27 4.22 .974

Lean Senior
Administrator

27

4.37

1.006

Lean Certified
Associate

136

3.93

1.126

Note: P= .121

Table 68
ANOVA Analysis for Training and coaching being a most relevant part of MU–Lean by the role
of the participant

N Mean SD F Value
Lean Partner

102 4.22 .981 2.837*
Lean Manager 27 4.19 .879

Lean Senior
Administrator

27

4.07

1.107

Lean Certified
Associate

136

3.84

1.097

* Note: P= .038

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 211
Table 69
ANOVA Analysis for Impact on Learning Outcome being a most relevant part of MU–Lean by
the role of the participant

N Mean SD F Value
Lean Partner

102 3.68 1.036 2.027*
Lean Manager 27 3.11 1.121

Lean Senior
Administrator

27

3.63

1.149

Lean Certified
Associate

136

3.51

1.102

* Note: P= .110
Table 70
ANOVA Analysis for Six Sigma Principles being a most relevant part of MU–Lean by the role of
the participant

N Mean SD F Value
Lean Partner

102 3.51 1.132 3.346*
Lean Manager 27 3.07 .874

Lean Senior
Administrator

27

3.22

1.013

Lean Certified
Associate

136

3.07

1.143

* Note: P= .020

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 212
Table 71
ANOVA Analysis for Management Support/Collaboration being a most relevant part of MU–
Lean by the role of the participant.

N Mean SD F Value
Lean Partner

102 4.11 .932 1.477*
Lean Manager 27 4.30 .912

Lean Senior
Administrator

27

4.41

.971

Lean Certified
Associate

136

4.01

1.139

* Note: P= .221
Table 72
ANOVA Analysis for Recognition and Reward being a most relevant part of MU–Lean by the
role of the participant

N Mean SD F Value
Lean Partner

102 3.95 1.009 1.156*
Lean Manager 27 3.70 1.137

Lean Senior
Administrator

27

3.81

1.111

Lean Certified
Associate

136

3.69

1.152

* Note: P= .327

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 213
Table 73
ANOVA Analysis for Communication being a most relevant part of MU–Lean by the role of the
participant.

N Mean SD F Value
Lean Partner

102 4.46 .886 2.156*
Lean Manager 27 4.52 .849

Lean Senior
Administrator

27

4.48

.935

Lean Certified
Associate

136

4.18

1.069

* Note: P= .092
Table 74
ANOVA Analysis for level of importance of Machine Learning is for the future of driving higher
education excellence by participating stakeholders

N Mean SD F Value
Lean Partner

19 3.84 1.015 2.350*
Lean Manager 2 4.00 1.414

Lean Senior
Administrator

2

2.00

1.414

Lean Certified
Associate

12

3.25

1.055

* Note: P= .092

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 214
Table 75
ANOVA Analysis for level of importance of Predictive and Descriptive Data Analytics is for the
future of driving higher education excellence by participating stakeholders

N Mean SD F Value
Lean Partner

19 4.74 . 733 2.519*
Lean Manager 2 5.00 .000

Lean Senior
Administrator

2

3.00

2.828

Lean Certified
Associate

12

4.25

.965

* Note: P= .076
Table 76
ANOVA Analysis for level of importance of Artificial Intelligence is for the future of driving
higher education excellence by participating stakeholders

N Mean SD F Value
Lean Partner

19 3.42 1.502 .060*
Lean Manager 2 3.50 2.121

Lean Senior
Administrator

2

3.00

2.828

Lean Certified
Associate

12

3.33

.985

* Note: P= .980

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 215
Table 77
ANOVA Analysis for level of importance of Robotic Process Automation is for the future of
driving higher education excellence by participating stakeholders

N Mean SD F Value
Lean Partner

19 3.37 1.257 .067*
Lean Manager 2 3.50 2.121

Lean Senior
Administrator

2

3.00

2.828

Lean Certified
Associate

12

3.25

1.215

* Note: P= .977
Table 78
Frequency Analysis for MU–Lean has being able to collaborate with faculties

Percent
Strong Agree 5.1

Agree 42.4

Disagree 19.2

Strongly Disagree 2.8

Total 69.4
*N Valid – 300

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 216
Table 79
Frequency Analysis for MU–Lean has being able to collaborate with various departments

Percent
Strong Agree 13.0

Agree 50.0

Disagree 4.9

Strongly Disagree 1.6

Total 69.4
*N Valid – 300
Table 80
Frequency Analysis for how important it is for MU–Lean to continue to deliver the cost savings
for the next 10 years as a part of the cost–containment initiative

Frequency Percent
Strong Agree 101 23.4

Agree 198 45.8

Disagree 29 6.7

Strongly Disagree 9 2.1

Total 337 78.0
*N Valid – 337

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 217
Table 81
Frequency Analysis for level of emerging technology experience

Percent
Robotic Process
Automation
1.4

Predictive and
Descriptive Data
Analytics

6.3

Machine Learning

2.3

Artificial Intelligence

1.1
None 59.5
*N Valid – 6
Table 82
ANOVA Analysis for importance of Machine Learning for the future of driving higher education
excellence by the role of the participant

N Mean SD F Value
Lean Partner

19 3.84 1.015 2.350*
Lean Manager 2 4.00 1.414

Lean Senior
Administrator

2

2.00

1.414

Lean Certified
Associate

12

3.25

1.055

* Note: P= .092

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 218
Table 83
ANOVA Analysis for importance of Predictive and Descriptive Data Analytics for the future of
driving higher education excellence by the role of the participant

N Mean SD F Value
Lean Partner

19 4.74 . 733 2.519*
Lean Manager 2 5.00 .000

Lean Senior
Administrator

2

3.00

2.828

Lean Certified
Associate

12

4.25

.965

* Note: P= .076
Table 84
ANOVA Analysis for importance of Artificial Intelligence for the future of driving higher
education excellence by the role of the participant

N Mean SD F Value Sig.
Lean Partner

19 3.42 1.502 .060*
Lean Manager 2 3.50 2.121

Lean Senior
Administrator

2

3.00

2.828

Lean Certified
Associate

12

3.33

.985

Total 35 3.37 1.374 .980
* Note: P= .980

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 219
Table 85
ANOVA Analysis for importance of Robotic Process Automation for the future of driving higher
education excellence by the role of the participant

N Mean SD F Value
Lean Partner

19 3.37 1.257 .067*
Lean Manager 2 3.50 2.121

Lean Senior
Administrator

2

3.00

2.828

Lean Certified
Associate

12

3.25

1.215

* Note: P= .977

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 220
Table 86
Validated Knowledge Attributes through survey data

Knowledge
Attribute
Validated Analysis
Reference
Not Validated New
Influence
Analysis
Reference
In–depth understanding
TQM and HEES
principles

✓

One Way
Anova

Knowledge of how to
identify, initiate, and
drive Lean projects

✓

Correlations,
Independent
Sample t Test,
One Way
Anova

Skill to create a Lean
organization and an
essential Eco–system

✓

Paired Sample
t Test,
Correlations,
Frequencies

Skills to transfer
knowledge to all the
colleges,
Administrative
department and
auxiliary services

✓

Frequencies

Ability and skill to
deploy right
methodology and tools
to successfully deliver
the goals

✓

Frequencies,
Correlations

Ability to engage
various stakeholders:
faculties, staff, support
services, and senior
executives

✓

Frequencies

Teams are self–
directed and capable of
identifying, initiating
and leading
successfully to deliver
the results

✓

Robotic Process
Automation

✓
Frequency
One Sample
One Way Anova

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 221

Predictive and
Descriptive Data
Analytics

✓ Frequency
One Sample
One Way Anova

Machine Learning

✓ Frequency
One Sample
One Way Anova

✓ Frequency

Artificial Intelligence

One Sample
One Way Anova

Table 87
Validated Motivational Attributes through survey data

Motivational
Attribute
Validated Analysis
Reference
Not
Validated
Analysis
Reference
New
Influence
Analysis
Influence
Lean Administrators
see value in driving
Lean Six Sigma
(HEES)
transformation as an
organization wide
initiative to help
support a part of
cost containment
initiative

✓

One Way
Anova
Frequencies

Lean certified
members see value
in driving HEES
transformation to
make Miami
University the most
cost–effective
University

✓
One Sample
t Test,
One Way
Anova,
Frequencies
Paired
Sample t
Test,

Lean administrators
are confident in their
ability to identify,
initiate and execute
Lean projects
throughout the
university by
building Lean
teams, driving
consensus and

✓

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 222
certifying 750
university associates
in Lean
methodology

Initially find
"University Wide"
projects that will get
multiple
departments
involved in
program. Often it
seems groups are
working on smaller
projects that are
surely needed.
However, I think
getting the energy
behind 2 or 3 high
level projects across
all business units of
the University
builds momentum to
sustain more smaller
projects in the
future.

✓

Open End
Offer training
regardless of class
size to get as many
people trained as
possible. Use the
LEAN "belt" system
for recognition of
staff members. Have
an online system for
tracking projects
and training classes.

✓

Open End

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 223
Table 88
Validated Organizational Attributes through survey data

Organizational
Attribute
Validated Analysis Reference New
Influence
Analysis
Influence
The Miami University promotes,
supports and recognizes cost
containment efforts as a critical
component of their strategic
initiative. Since MU–Lean
initiative has been successful, it is
assumed that communication,
transparency , tools resources, and
the organizational foundation was
critical and essential part of MU–
Lean strategic plan.

✓

Frequencies
Correlations
Independent Sample t
Test,
One Way Anova
Open End

The MU–Lean leadership provides
support to Lean administrators in
gaining buy–in from faculty, staff
administration, and senior
leadership to help drive the cost
containment efforts

✓

Frequencies
Correlations,

Miami University provides the
administrations necessary
resources such as time, tools,
training, and support to facilitate
change

✓

Correlations
Frequencies

Miami University promotes open
communication channels between
associates, staff, and leadership to
gather the process improvement
and innovation ideas to help
support cost containment.

✓

Q31 Open
End
Open End

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 224
Table 89
Project Communication Plan

Stakeholder
Group
Objective
Result/ Action
Desired
Content of
Message
Channel By When
Students Define the
learning
outcome flow
path

Establish
baseline
resource needs
Create clear
understanding of
student needs
and baseline
capabilities
Questionnaire
Town Hall
TBD
Faculty Define pedagogy
and classroom
resources

Create impactful
curriculum and
learning
outcomes
Establish
proactive
engagement
strategy and buy
in
Meetings
Lunch and Learn
TBD
Alumni Establish
continuous
engagement plan

Communicate
achievements
through HEES
Provide active
venue for
industry pulse
check and
required changes
in delivery
systems
Network Events
Symposiums

TBD
Administration Create project
repository

Create training
master plan
Creating a
culture of waste
elimination,
efficiency and
effectiveness
Weekly/Monthly
Meetings
TBD
President’s
Council
Create executive
dashboard

Create monthly
debrief meetings
Promote the
culture and
reward system to
embrace HEES
President’s
Message
Monthly
Meetings
TBD

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 225
Table 90

RACI* Matrix

Project Task Lean Partner Lean Senior
Administrators
Faculty Council President
Council
Create Project
Charter for
HEES

A R C I
Create
Stakeholder
Analysis

R A C C
Identify Phase 1
Roll Out
Schedule

R R C A
Provide
necessary
resources to
HEES needs
A A C R
*(R) Responsible – (A) Accountable – (C) Consulted – (I) Informed

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 226

Figure 19. Survey Participants Perceptions Students seeing the value in MU–Lean.

Figure 20. Survey Participants Perceptions towards certified MU–Lean participant being trained
to execute projects.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 227

Figure 21. Survey Participants Perceptions towards having full support from manager to work
on MU–Lean projects.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 228

Figure 22. Picking Strategy Chart

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 229

Figure 23. Project Status Worksheet.

Figure 24. VOC Persona Matrix
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 230

Figure 25.HEES CONOPS

Student Data

Inputs Black–Box (HEES)
Web Crawl
Job Posting
LMS
Web Info
Public Datasets
Social Media
ID Card Swipes
Outputs
Cost
Learning
Outcome
Experience
Job Suggestion
Future Info

Storage
Cloud
Storage
Cloud
Pub/Sub
Cloud
Dataflow

Processing

NLP
API/Auto
NL
BigQuery
ML
Engine/
Auto ML

Visualizati
on
Google
Cloud
BI
Solution/
Data
Cloud
Datalab
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 231
APPENDIX N
PROTOTYPE WIREFRAME
Student dataset
• Full Name
• Gender
• Age
• Nationality
• ID# – to uniquely identify each student’s record
• Previous educational experience – bachelor’s or Masters (Either from USC records or from
resume); Grades, accomplishments
• Work experience – Company worked, job title, salary, # of years
• Skill set
• Projects – keywords picked from projects and skill set that can be used for identifying
matching job descriptions
• Achievements – technical and non–technical
• Volunteering work, social activities
• Courses taken towards degree
• Courses that need to be mandatorily taken for degree completion
• Current grade and subject scores
• Sports involvement
• Research work/Publications – Previous or currently into; Conferences attended if any
• School fees related bill payment
• Opted for school insurance or not
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 232
• Grants/Financial aids if any
• Events/ workshops/fairs the student has registered for in Career gateway and USC career
resources portal
• Student’s mental and physical health related data – Highly sensitive, so difficult to get hand
on this
• Students card swipes – Libraries, Labs
• Current part–time / on–campus job
• Sessions with advisor – if possible a summary of their discussion
• Involvement in USC clubs – Meditation club, Technical clubs, Frat houses
• Tweets posted or liked. List of people/pages whom the student is following.
• LinkedIn data such as posts liked/shared, people/companies following, job search status
and job applications.
• Facebook posts liked/shared, events liked or attending, Meetups or Eventbrite tickets
bought and shared on FB.
• Github for CS students, other websites for students doing other majors.
• Scraping personal blogs if any
Faculty Dataset
• Full Name
• Educational background
• Work experience
• Areas of interest
• Courses being handled; Feedback and popularity of thee courses
• Labs/collaborations with students
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 233
• Publications
• Funds provided by USC
• Provides advisement sessions or not
Alumni Dataset
• Full name
• ID#
• Age
• Gender
• LinkedIn account posts/connections
• Company working for, Financial stability
• University’s alumni management team’s data

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 234
APPENDIX O
GOOGLE CLOUD PLATFORM
Below are some of the Cloud AI solutions that can be used in building this system
• Provides ML services, with pre–trained models and a service (Neural–net–based ML
service) to generate our own tailored models.
• Cloud AutoML – Customize ML models by leveraging transfer learning and neural
architecture search technology.
• Cloud TPUs – Hardware Support.
• Cloud ML engine – This makes it easy to build sophisticated, large–scale ML models. Its
integrated with other GCP products like Cloud Storage, Cloud Dataflow, Cloud Datalab.
• BigQuery ML – Models with SQL.
• Dialogflow Enterprise Edition – Build interfaces like Chatbots
• Cloud Vision API – (Image recognition & Classification) Classifies images into thousands
of categories, detects individual objects and faces within images, and finds and reads
printed words contained within images. Does this via pre–trained API models or by training
custom vision models with AutoML Vision Beta.
• Cloud Speech–to–Text – Provides APIs to convert audio to text.
• Cloud Text–to–Speech – Synthesize natural–sounding speech from APIs.
• Cloud Natural Language API – (Text parsing & Analysis) Offers pre–trained ML models
as REST APIs which identifies Entities, Sentiment, Syntax and categories. Also, AutoML
Natural Language Beta can be used for building custom text–classification models.
• Cloud Inference API – Run large scale correlations over typed time–series datasets. Detect
trends and anomalies with event time markers.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 235
Languages:
Machine Learning and GCP:
• Python, Spark, Tensor Flow, SQL, NoSQL
Web Application
• HTML, CSS, Javascript, Angular, React
Backend Services
• Java, NodeJS, SQL
Mobile Apps
• Swift, Java, Kotlin, C#(Xamarin)
Environments and Tools:
Machine Learning and GCP
• pyCharm, Jupyter Notebook, AWS, GCP, Azure
Web Application
• VS Code, Visual Studio, Atom, Google Chrome
Backend Services
• NodeJS, Laravel, Apache, NGINX, Docker, Kubernetes
Mobile Apps
• Xcode, Android Studio, App Store, Playstore

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 236
APPENDIX P
DEVELOPING THE BUSINESS CASE
Every project needs to have a clearly articulated business case that links the project
outcomes directly back to business outcomes or objectives. If it cannot articulate a business case
for a project, then there is no meaningful project and should not move forward. The purpose of
the business case is to justify the project. A good business case will include the following
elements:
• A broad statement of the area of concern or opportunity
• The impact/benefit the potential improvement will have on the university
• The impact/cost/risk of not improving
• Show the direct link to the business’ strategy, values, students, etc.
The Problem/Opportunity Statement.

To determine the appropriate problem or opportunity, review the business case and
identify any sub–issues or elements of the problem. Determine if there are related concerns or
problems as well as try to see if there are ways to break down or narrow the problem. The
problem/opportunity statement should be measurable. It should:
• Describe the issue, gap or opportunity
• Identify the severity of the problem or the size of the opportunity
• If available, give specifics.Do not at this point describe the cause, attach blame or
responsibility or attempt to prescribe the solution.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 237
It should describe the issue, gap, or opportunity as well as Identify the severity of the
pain, size of the opportunity. It should give specifics as available. However, it should not
describe the cause, attach blame or responsibility, and contain a prescribed solution. In most
cases, it should be able to answer the following questions:
• What is the opportunity or problem? – Be Specific.
• How to know the opportunity or if a problem exists? – What observations confirm the
need?
• How big is the problem/gap/opportunity? – Be sure it is measurable! Leave “measure”
placeholder(s)
• What is the impact of the problem/opportunity?
Creating the Project Goal.
Once problem/opportunity has been identified, write a goal statement to be included on project
charter. The goal statement identifies the critical outcome of the project and should be written as
SMART goals: Specific, Measurable, Attainable, Relevant, and Time–bound. At this point in
the project, any measuring, so the “From” and “To” part of the statement should not be finalized.
This portion of the goal statement will be finalized after the Measure phase when the full scope
of the problem is indeed known. The following attributes should be considered by reviewing the
business case and identifying the following points:
• Sub–issues or elements of the problem
• Related concerns or problems
• Ways to break down or narrow the problem
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 238
• Assess elements and appropriate action:
• Significant, cause–unknown problems with no apparent solutions: DMAIC (Phase 2)
• Immediate problems with obvious, low–risk solutions: (FastTrack)
• Issues too large, small or outside the scope: (Defer altogether)
Goal Statement.
To establish the goal statement,identifies the critical outcome of the process improvement project
and describes target/results and quantifiable benefits. Some of the likely target areas can be:
• Linked to project vision
• One primary "Y," sometimes with secondary goals
• Includes a time frame for implementation and results
• Should NOT pre–judge the solution or design
In Scope, Out of Scope.
This is a significant part of the project charter. The scope is the breadth of the problem to
be tackled and sets the boundaries of the project. Defining what is in scope and what is not is
important because it helps to prevent “goal creep” where the project becomes more substantial
than can be accomplished. Defining the scope clarifies what is relevant to the project and where
the Measure and Analyze phase will focus. The scope section of the Project Charter will clearly
state what is to be accomplished, analyzed, discussed (in– scope) and what is NOT to be
accomplished, analyzed, discussed (out of scope). The scope of the project may be further
refined during the Measure and Analyze phase as roadblocks and barriers, or other issues are
discovered.
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 239
In addition to previously defined key steps within project charter, other elements within
project charter should be considered including financial Benefits to the HEI. It should Identify
the benefits that will occur if the goal is achieved. There are four types of benefits:
• Cost Reduction – Savings to the bottom line
• Cost Avoidance – Reduces future costs or expenses
• Revenue Increase
• Other or No Financial Impact – Increase in customer satisfaction
Along with the financial benefits, identifies any known dependency that could affect the project.
Try to understand it any dependency that could present a risk to the project should also be
captured in a critical dependency section of the charter.
People and Other Resources.
The critical part of the project charter is to identify the resources in terms of team
members as well as others explicitly. Some of the key questions should be asked:
• What expertise do we need on the team?
• Are the stakeholders represented on the team?
• What resources besides people, are required? (e.g., laptops, vehicles, training materials,
etc.).

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 240
Constraints.
Identifying constraint ahead of the project launch help build the success and unwanted
surprises. The limitations on resources and capabilities that may influence the project and
solutions (e.g., space, money, people, talent, time).It also highlights the risk assumptions and
accountabilities using “Risks & Issues” log summarizes the key risks that could impact this
project. This step helpsrecord and track these risks in the “Risks and Issues Log” as the project
progresses. To summarize, following points should be considered:
• List the assumptions that have gone into creating the project brief.
• Assumptions are things that have assumed to be true (but may not have the proof yet).
• Identify who will ultimately accountable for delivering the impacts and benefits.
• This person should also have accountability over the process, the changes that are made
and that the benefits that are achieved.

DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 241
APPENDIX Q
ATTRIBUTE DESCRIPTION

Student information – School databases and datasets.

The critical dimension of academic record including previous education, current performance
such as Blackboard or similar LMS), courses completed and registered, timetable (to see how
occupied the student is or does student have much free time); Research work; Publications.
• Bill payments – Is the student paying bills on time or in installments or is there any due.
• Financial aid – Is the student receiving Grant/Financial aid or student loan.
• Any other school–specific career resource portal – Events, workshops and fairs, the student
has registered. Identify student participation in OCR interviews.
• Student medical appointments with the health center.
• Working/volunteering at some internship or does he/she visit libraries often.
• Workday
• Whether the student is working part–time or full–time on–campus, if so how many hours
he/she is working and how much is he/she is paid.
• Whether the student is interning in a company outside USC. If yes, how many hours and how
much pay.
• An advisement session with an advisor – How often does the student meet advisor,
report/outcomes of their meeting.
• Student clubs the student is part of – Like USC meditation club, Technical clubs, Fraternity
houses, Volunteering activities, etc.
• Current statistics for graduation rates, placements, ROI, etc.
• Social accounts like LinkedIn, Twitter, Facebook
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 242
• LinkedIn activity info, posts liked,
• People and companies following
• Jobs the student has looked at or applied for within the area of interest.
• Social media connections and relevance to his/her area of interest.
• Interaction with different people, recruiters, influential people in different industries.
• Posts liked, posted, shared.
• Pages or people followed.
• Groups/meetups and other professional organization association.
• Events liked or attending – Meetups or Eventbrite tickets purchased and shared on FB.
• Conferences he/she has attended/participated
• Twitter
• Tweets tweeted, liked, shared.
• Pages and people followed.
• Github – Open source contributions
• Personal blogs: Content posted in a personal blog can give indications about mental health,
stress if any.
• USC student email: Track emails student receives to get information about job applications,
offers, events participating/attending, other miscellaneous stuff.
Faculty information

• Faculty directory
• CV
• Specializations/areas of interest
• Courses being handled
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 243
• Collaborations with students in labs or projects
• Publications
• Research work
• Funding provided by USC and ROI.
Alumni

• Alumni information from USC’s database.
• More information about their financial position and stability from LinkedIn (Employment
info), Facebook (photos posted).
• Student card swipes for entry to Labs and Libraries – If the student has a Information from
Alumni management team, which calls up each prospective alumnus asking for donations –
which can help identify alumni who would make donations or stay connected with hiring events,
talks.
Industry trends/needs

• General info about most highly paid jobs
• Technological advancements or trends (Can conduct workshops for exposing the students to
these technologies; Invite alumni/companies excelling in these fields to give talks)
Donors: (to help identify prospective donors)

• University database
• Donations/Development dataset/courses offered by other top universities and its popularities
• Analyzing the popularity of courses offered by the university and its reviews.
The foundation of HEES prepares an individual with a set of tools and methodology to become a
capable “change agent” to significantly improve business processes. HEES helps reduce process
variation, eliminate waste and defects, accurately measure and analyze data for process
DATA ANALYTICS AND BLENDED QUALITY MANAGEMENT 244
improvement, identify and eliminate process variation sources, and implement process control to
sustain project improvement.
Post Launch Sequence (Past 12 Months to 24 Months Plus)
At this point of HEES implementation journey, organization should use the predictive
and prescriptive capabilities of HEES. It should create advanced dashboards and reporting
capabilities to enhance resource management, mitigation strategies, student advising and tutoring
needs as well as being able to quantify the learning outcomes in a dynamic way. System portal
with a built–in machine learning capability will enhance the ability for organizations to curate
students, faculty, staff need in a much more accurate, efficient and effective manner.

Abstract (if available)

Linked assets

University of Southern California Dissertations and Theses

Asset Metadata

Creator Vyas, Nikhil (Nick) V. (author)

Core Title Integrating data analytics and blended quality management to optimize higher education systems (HEES)

School Rossier School of Education

Degree Doctor of Education

Degree Program Global Executive

Publication Date 08/12/2019

Defense Date 04/18/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag American Dream,analyzing student debt,awareness of Lean principles,background of the blended quality management system in HEI,challenges of blended quality management in education field,Communication,concepts of Lean thinking,cost-containment and competing theories in education,DMADV (Define, Measure, Analyze, Design, and Verify),education return on investment (ROI),emerging technologies in education,empowerment and support from leadership in education,engagement of stakeholders in education,funding in higher education,HEES/I/ML/Advance Data Analytics Model implementation plan,higher education description of stakeholder groups,higher education organizational context and mission,higher education organizational performance goal and current performance,history of quality management,importance of examining promising practices of blended quality management,international enrollments,Lean and Six Sigma,lean six sigma,long-term impact of student debt,Miami University of Ohio Mu-Lean approach,need for higher education optimization system,OAI-PMH Harvest,principles of total quality management (TQM),receptiveness to change management in education,rising cost of higher education,TQM,training, recognition and reward systems in education,understanding the Higher Education Excellence System (HEES) principles

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Filback, Rob (committee chair), Chung, Ruth (committee member), Kumar, Ravi (committee member)

Creator Email Thevyas@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-212407

Unique identifier UC11663214

Identifier etd-VyasNikhil-7770.pdf (filename),usctheses-c89-212407 (legacy record id)

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Dmrecord 212407

Document Type Dissertation

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Rights Vyas, Nikhil (Nick) V.

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Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA