Big data in lean six sigma: a review and further research directions

Shivam Gupta, Sachin Modgil, Angappa Gunasekaran

Research output: Contribution to journalReview articlepeer-review

161 Scopus citations


Manufacturing and service organisations improve their processes on a continuous basis to have better operational performance. They use lean six sigma (LSS) projects for process improvement. Therefore, this study aims to investigate the existing literature in LSS and the application of big data analytics (BDA) to have more confident and predictable decisions in each phase of LSS. Fifty-two articles have been identified after a careful and vigilant screening of closely related themes. Future research directions in the big data and LSS have been highlighted on the basis of organisational theories. Review presents an investigation framework consisting of BDA techniques applicable to each phase of LSS in all the dimensions such as volume, variety, velocity and veracity of big data. Review highlights the concerns of big data in LSS such as system design and integration, system performance, security and reliability of data, sustaining the control and conducting the experiments, distributed material and information flow. The review unveils the application of 8 modern organisational theories to big data in LSS with 21 key aspects of related theories and 19 distinct research gaps as opportunities for future research.

Original languageEnglish (US)
Pages (from-to)947-969
Number of pages23
JournalInternational Journal of Production Research
Issue number3
StatePublished - Feb 1 2020

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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