Amine Belhadi, Sachin S. Kamble, A. Gunasekaran, Karim Zkik, D. M., Fatima Ezahra Touriki
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A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company
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.
期刊介绍：
Production Planning & Control is an international journal that focuses on research papers concerning operations management across industries. It emphasizes research originating from industrial needs that can provide guidance to managers and future researchers. Papers accepted by "Production Planning & Control" should address emerging industrial needs, clearly outlining the nature of the industrial problem. Any suitable research methods may be employed, and each paper should justify the method used. Case studies illustrating international significance are encouraged. Authors are encouraged to relate their work to existing knowledge in the field, particularly regarding its implications for management practice and future research agendas.
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