Analyzing Lean Six Sigma enablers: a hybrid ISM-fuzzy MICMAC approach

Abstract

PurposeThe purpose of this paper is to identify Lean Six Sigma enablers (LSSEs) and analyse the interaction among the enablers via a hierarchical model developed by employing interpretive structural modelling (ISM) and determine the driving and dependence power of enablers through fuzzy MICMAC (Matriced’ Impacts Croise´s Multiplication Applique´ea´un Classement) analysis.Design/methodology/approachAn expert group of industry professionals and academicians is consulted at the initial stage as an input for ISM methodology to explore the paired relationship among LSSEs for each parameter of Lean Six Sigma (LSS) implementation. The outcome of ISM is further utilized by fuzzy MICMAC analysis to discover the enablers that are strong drivers and highly dependent. Fuzzy set is included in MICMAC analysis in order to obtain more precise output and effective model.FindingsIn total, 20 key enablers are identified through a literature review and expert opinion that emerged as the most significant factors towards LSS implementation. The identified enablers are portrayed into a structural form representing as input and output variables. Later, the driving and the dependence power of each enabler is presented in cluster form.Research limitations/implicationsThe paired relationships among LSSEs are obtained through the interpretation made by the experts. The judgments of experts are subjective and may be biased; as difference in expert opinion may influence the final outcome. Conducting a large-scale survey may provide a better catch for interactions of LSSEs.Practical implicationsThis study provides strong practical implications for researchers as well as industry practitioners. The industry professionals must deliberately focus on the identified LSSEs more conservatively during LSS implementation and the top management should plan strategically to avoid any implementation failure.Originality/valueThe present study identifies 20 crucial enablers of integrated LSS and presents them in a hierarchical form which will be beneficial for researchers and practitioners. The interactions among the enablers shown in cluster form will help in better execution of LSS.