Evaluating Sustainable Lean Six Sigma enablers using fuzzy DEMATEL: A case of an Indian manufacturing organization

Abstract

The survival of the manufacturing organizations has been challenged by global competition, dynamic customer requirements, and limited natural resources. Manufacturing organizations need to adopt innovative strategies to overcome such challenges. Lean Six Sigma is one of the key business strategies which has been employed by manufacturing organizations to improve their operational performance. The integration of Sustainability with Lean Six Sigma has created interest among the researchers and practitioners to improve sustainable performance. For any manufacturing organization, it is critical to identify the enablers for the successful Sustainable Lean Six Sigma implementation. The purpose of the present study is to identify and evaluate the enablers of Sustainable Lean Six Sigma. The enablers of Sustainable Lean Six Sigma in Indian manufacturing organizations have not been explored in past studies, which motivates the present case study. The enablers for Sustainable Lean Six Sigma implementation were identified through the combined approach of literature review and experts’ opinions. Fuzzy decision-making trial and evaluation laboratory technique was applied to evaluate the enablers for Sustainable Lean Six Sigma implementation in an electric component manufacturing organization. The relationships among the prominent cause enablers and effect enablers are established through the causal diagram. The top management commitment and involvement was found as the most significant enabler, followed by organizational readiness to implement Sustainable Lean Six Sigma, environmental management system. The present study is one of the initial studies to evaluate integrated (Sustainability and Lean Six Sigma) enablers for Sustainable Lean Six Sigma implementation. The results of the present study will assist the researchers and practitioners in implementing Sustainable Lean Six Sigma in manufacturing organizations by understanding the mutual relationships among the enablers.