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

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 A...

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Bibliographic Details
Published inProduction planning & control Vol. 34; no. 9; pp. 767 - 790
Main Authors Belhadi, Amine, Kamble, Sachin S., Gunasekaran, Angappa, Zkik, Karim, M., Dileep Kumar, Touriki, Fatima Ezahra
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 04.07.2023
Taylor & Francis LLC
Subjects
Big Data
Big Data Analytics
case study
chemical sector
Data analysis
Environmental management
green performance
Inspection
Lean Six Sigma
New technology
Predictive maintenance
Quality control
Six Sigma
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Summary: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.
ISSN:0953-7287
1366-5871
DOI:10.1080/09537287.2021.1964868