TY - JOUR
T1 - A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance
T2 - a case study of chemical company
AU - Belhadi, Amine
AU - Kamble, Sachin S.
AU - Gunasekaran, Angappa
AU - Zkik, Karim
AU - Dileep Kumar, M.
AU - Touriki, Fatima Ezahra
N1 - Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.1080/09537287.2021.1964868
DO - 10.1080/09537287.2021.1964868
M3 - Article
AN - SCOPUS:85112711992
SN - 0953-7287
VL - 34
SP - 767
EP - 790
JO - Production Planning and Control
JF - Production Planning and Control
IS - 9
ER -