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Big Data Analytics: Review of Application of Data Mining Techniques for (Lean) Six Sigma Methodology

EasyChair Preprint no. 11009

18 pagesDate: October 3, 2023


Lean Six Sigma has proven to be an invaluable performance measure among competing firms. The Data-driven methodology imposed by LSS defines metrics to measure, analyze, improve, and control processes. As our digital footprint increases by the day, there is more data, hence more opportunities for companies to gain a competitive advantage. Firms are beginning to look into ways of capturing more data in an effort to capitalize on its value. Estimations suggest that roughly 80% of all data (Big Data) goes unaccounted, alluding to a wealth of insights for leveraging the market. Advanced data analytics can enhance LSS to improve operational efficiency and facilitate innovation. Through research, we hope to provide a thorough analysis of LSS and BDA in support of combining methods. This paper investigates existing literature to rationalize the application of advanced analytics into the LSS methodology. Also discussed are existing case studies of BDA integration for process improvements.

Keyphrases: Advanced Data Analytics, Big Data, Lean, process improvements, Six Sigma

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {William Pontius and Mark McMurtrey},
  title = {Big Data Analytics: Review of Application of Data Mining Techniques for (Lean) Six Sigma Methodology},
  howpublished = {EasyChair Preprint no. 11009},

  year = {EasyChair, 2023}}
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