Decision-making through fuzzy TOPSIS and COPRAS approaches for lean tools selection: A case study of automotive accessories manufacturing industry. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Bhuvanesh Kumar, M; Antony, Jiju; Cudney, Elizabeth; Furterer, Sandy L.; Garza-Reyes, Jose Arturo; Senthil, S. M
• Is Part Of: International journal of management science and engineering management. Volume 18:Issue 1(2023)
• Page Start: 26
• Page End: 35
• Publication Date: 2023-01-02
• Subjects: Value stream mapping -- lean manufacturing -- TOPSIS -- COPRAS -- systematic layout planning -- automobile industry; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2022.2064356
• Languages: English
• ISSNs: 1750-9661

Abstract

ABSTRACT: Similarity in prioritization of lean tools (LTs) by different frameworks on the same problem is a point of contention. The goal of the present research is to address LT selection problem through two commonly used multi-criteria decision-making approaches, namely the technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS). A framework involving value stream mapping and plant layout through TOPSIS and COPRAS approaches to find the best possible LTs for an automotive accessories manufacturing plant is developed and assessed in this research. The obtained similarity of rankings betweenTOPSIS and COPRAS is 71.42%, and the difference is 28.58%. Based on the assessment, systematic layout planning (SLP) is selected as the most suitable LT and its implementation is elaborated in detail. Significant reductions were obtained in lead time (16.44%), non-value added time (61.03%), transportation distances (40.42%), and waiting time (86%). Additionally, lean implementation resulted in reduced inventory, reduced internal traffic, improved productivity, and improved customer service. The LT selection framework presented in this research work addresses the computational complexity associated with the existing models and allows the segregation of the most preferable and the least preferable criteria which eliminate the criteria weight generation methods.