A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company

Amine Belhadi, Sachin S. Kamble, Angappa Gunasekaran, Karim Zkik, Dileep Kumar M, Fatima Ezahra Touriki

Research output: Contribution to journalArticlepeer-review

Abstract

The advent of new technologies alongside the generation of the vast amount of data in the manufacturing processes makes Green Lean Six Sigma (GLSS) approaches very challenging. This paper presents a novel framework termed ‘BDA-GLSS’ that guides companies to effectively integrate Big Data Analytics (BDA) in GLSS to improve their environmental performance. The BDA-GLSS framework is validated using an industrial case study of a leading chemical company. The results suggest measurable benefits of the proposed framework in enhancing technological readiness, problem identification, and analysis with predictive capability. The BDA-GLSS guides the implementation of BDA techniques within the GLSS framework offering real-time quality control, event-based inspection, and predictive maintenance. The BDA-GLSS enhances the environmental capability, process performance and provides a new perspective for researchers and practitioners to support GLSS projects in achieving higher green performance.

Original languageEnglish (US)
JournalProduction Planning and Control
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

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

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