Authors: Sanjay Kumar; Bhim Singh; Mohammed Asim Qadri; Y.V. Satya Kumar; Abid Haleem
Addresses: Department of Mechanical Engineering, Galgotias College of Engineering and Technology, Greater Noida – 201306, India ' Department of Mechanical Engineering, School of Engineering and Technology, Galgotias University, Greater Noida – 201306, India ' Department of Mechanical Engineering, Galgotias College of Engineering and Technology, Greater Noida – 201306, India ' Department of Mechanical and Automobile Engineering, School of Engineering and Technology, Sharda University, Greater Noida, 201306, India ' Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi – 110025, India
Abstract: Adoption of lean practices is considered a vital strategic tool for firms to thrive in today's competitive times. Comparative evaluation of leanness of the companies has assumed crucial importance in the wake of increasing globalisation and phenomenal advancement in technology. Evaluation of policies and practices on an ongoing basis helps organisations identify the potential opportunities for improvement. Only limited efforts devoted to assessing the relative lean status of firms have been made so far and there is an express need to articulate a framework for measurement of lean adaptation. Here, a systematic fuzzy multi-criteria decision making (MCDM) evaluation model based on technique for order preference by similarity to ideal solution (TOPSIS) is proposed for relative lean ranking of firms. Fuzzy set theory concepts are used to deal with problems of vagueness, uncertainties, inexactness of data and the subjectivity associated with human judgment. An illustrative numerical example is included to elucidate the computational process. Sensitivity analysis is also carried out to demonstrate the robustness and efficacy of the adapted methodology.
Keywords: leanness; multicriteria decision making; fuzzy MCDM; triangular fuzzy numbers; TFN; fuzzy TOPSIS; sensitivity analysis; lean performance; firm ranking; fuzzy set theory.
International Journal of Productivity and Quality Management, 2013 Vol.11 No.4, pp.371 - 392
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 29 Mar 2013 *