Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Production planning & control, 2023-07, Vol.34 (9), p.767-790

Ort / Verlag

London: Taylor & Francis

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EBSCOhost Business Source Ultimate

Beschreibungen/Notizen

• 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.