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

Authors

  • David John Fogarty University of Phoenix

DOI:

https://doi.org/10.18059/jmi.v1i2.8

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

References

Auschitzky, E., Hammer, M., Rajagopaul, A. (2014), How Big Data Can Improve Manufacturing: Manufacturers Taking Advantage of Advanced Analytics Can Reduce Process Flaws, Saving Time and Money, McKinsey Insights, July.

Barjaktarovic, L., Jecmenica, D. (2014) Six Sigma Concept, Acta Technica Corvininesis ΓÇô Bulletin of Engineering. vol. 7 no. 3, pp. 103-108.

Catalina, S. (2012) Six Sigma as a Strategic Tool for Companies, Young Economists Journal. vol. 7 no. 19, pp. 94-102.

Dumitrescu, C., D. Tent, I., O., D., Dumitrescu, E., Ivonne, C. (2010) Lean Six Sigma Principles, Annal of DAAAM & Proceedings, pp. 433ΓÇö435.

Dumbill, E. (2012) What is Big Data? An Introduction to the Big Data Landscape. [Blog post, January 2011.] O'Reilly Strata . http://strata.oreilly.com/2012/01/whatis- big-data.html.

Eckes, G. (2000). The Six Sigma Revolution: How GE and Others Turned Process into Profits. New York: Wiley.

Edward, G., Amos, M. (2011) Using Social Networks to Improve Operations, Harvard Business Review, December-January.

Fogarty, D., J. (2015) Lean Six Sigma and Advanced Analytics: Integrating Complementary Activities, Global Journal of Advanced Research, vol. 2, no. 2, pp. 472-480.

Franks, B. (2012) Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics. New York: Wiley.

Gobble, M., A. (2013) Big Data: The Next Big Thing in Innovation. Research and Technology Management, vol. 56, no. 1, pp. 64-66.

Hamm, S. (2012) Big Data: How Infinity Sniffs Out Insurance Fraud, IBM- Building a Smarter Planet Blog, Retrieved from: http://asmarterplanet.com/blog/2012/03/big-data-how-infinity-sniffs-out-insurance-fraud.html

Harvard Business Review (2012) Spotlight on Big Data. Harvard Business Review 90(10) (October).

IBM Consulting (2015) Retrieved from: http://www-35.ibm.com/services/us/gbs/strategy/social-business-consulting-and-solutions/

Jacobs, F. R. & Chase, R. (2011). Operations and Supply Chain Management. (13th ed.) Boston, MA: McGraw-Hill Irwin.

Lean Production.com (2015) Online Source for Lean-Based Information and Tools. In: http://www.leanproduction.com/.

Manenti, P. (2014) the Digital Factory: Game Changing Technologies That Will Transform Manufacturing Industry, SCM World, November.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Hung Byers, A. (2011), Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute.

Ravichandran, J. (2012) A Review of Preliminary Test-Based Statistical Methods for the Benefit of Six Sigma Quality Practitioners, Statistical Papers. vol. 53, no. 3, pp. 531-647.

Vijaya, S., M. (2013) Synergies of Lean Six Sigma. IUP Journal of Operations Management, vol. 12, no. 1, pp. 21-31.

World Economic Forum. (2012) Big Data, Big Impact: New Possibilities for International Development . Geneva, Switzerland: World Economic Forum. http://www.weforum. org/reports/big-data-big-impact-new-possibilities-international-development.

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Published

2015-09-07

Issue

Vol. 1 No. 2 (2015): Fall 2015

Section

Articles

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