Modeling and Analysis of Lean Manufacturing Strategies Using ISM-Fuzzy MICMAC Approach

Authors

  • Mohit Tyagi Department of Industrial and Production Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India
  • Dilbagh Panchal 1Department of Industrial and Production Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India
  • Deepak Kumar Department of Mechanical Engineering, Delhi Technological University, Delhi, India
  • R. S. Walia Department of Production and Industrial Engineering, PEC University, Chandigarh, India

DOI:

https://doi.org/10.31181/oresta2040123t

Keywords:

Lean Manufacturing System (LMS); Lean Strategies; Factor Analysis; SPSS 21; ISM Methodology; Fuzzy MICMAC

Abstract

The current research work deals with an identification of different lean strategies and extraction to relevant strategies after discussion with experts and gives the answer of a question “how lean manufacturing strategies can help the organization to enhance the efficiency of the organization with great effectiveness?”  In this research work, thirty-six lean strategies have been identified and out of which thirteen lean strategies were filtered in respect of highly importance value by factor analysis using software SPSS 21. Further, to identify and analyze the inter-relationship among filtered strategies, an Interpretive Structural Modeling (ISM) with Fuzzy Matriced’ Impacts Croise´s Multiplication Applique´e a UN Classement (MICMAC) approach has been used. Fuzzy MICMAC help to understand the dependence and driver’s power of the lean strategies. The mutual importance of extracted strategies has been discussed through developing the ISM model and the individual assessment of each strategy with each of the other strategies has been derived using the Fuzzy MICMAC approach.

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Published

2021-02-20

How to Cite

Mohit Tyagi, Dilbagh Panchal, Deepak Kumar, & R. S. Walia. (2021). Modeling and Analysis of Lean Manufacturing Strategies Using ISM-Fuzzy MICMAC Approach. Operational Research in Engineering Sciences: Theory and Applications, 4(1), 38–66. https://doi.org/10.31181/oresta2040123t