Lean Six Sigma and Big Data: Continuing to Innovate and Optimize Business Processes

  • David John Fogarty University of Phoenix

Abstract

Lean Six Sigma is widely used in many areas such as government, industry, healthcare and education. BIG DATA which is being collected in ever-increasing amounts due to digital commerce is viewed by futurists as holding many promises to solve some of our greatest problems in society and create new commercial ventures.  This paper explores how Lean Six Sigma can be applied to accelerate the process of extracting key insights from BIG DATA and also how BIG DATA can bring new light and innovation to projects requiring the use of Lean Six Sigma.   A case study approach using examples from both manufacturing and the service sector will be used in this research.

Author Biography

David John Fogarty, University of Phoenix
David Fogarty has a Ph.D. in Applied Statistics from Leeds
Beckett University located in the United Kingdom and currently works at Cigna, a Fortune 100 company, the fourth largest US health insurance company and the largest global health insurer as their Head of Global Customer
Value Management and Analytics

David currently has over 10 US patents or patents pending on business analytics algorithms and is also a certified Six Sigma Master Black Belt in Quality which is the highest qualification within the Six Sigma Quality methodology.

In addition to his work as a practitioner David has over 10 years of teaching experience and have held various adjunct academic appointments at both the graduate and undergraduate level in statistics, international management and quantitative analysis at various universities across the globe.

In term of scholarship David has over 50 published research papers in peer reviewed academic journals and has recently published a book on advanced analytics for business

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Published
2015-09-07
How to Cite
FOGARTY, David John. Lean Six Sigma and Big Data: Continuing to Innovate and Optimize Business Processes. Journal of Management and Innovation, [S.l.], v. 1, n. 2, p. 2-20, sep. 2015. ISSN 2378-5713. Available at: <http://jmi.mercy.edu/index.php/JMI/article/view/8>. Date accessed: 21 may 2018. doi: https://doi.org/10.18059/jmi.v1i2.8.
Section
Articles