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A hybrid framework to prioritize the performance metrics of reconfigurable manufacturing system (RMS) using fuzzy AHP–TOPSIS method

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Abstract

The reconfigurable manufacturing system (RMS) meets the challenges of dynamic customer demands, technological advancements, and reducing lead time, among other things. It was necessary to have a framework that can assist in increasing RMS adoption as well as evaluating its performance. The present study seeks to develop a hybrid framework for prioritizing performance metrics of RMS that helps the designers of the manufacturing system in decision making. A total of 31 enablers for RMS were identified through a literature review, the weight of each enabler was computed by the fuzzy-AHP (analytic hierarchy process) method, and the fuzzy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to prioritize 22 performance metrics of RMS. According to the findings of the presented study, smart factory enablers have the highest weightage among all of the main criteria enablers, followed by strategy and policy enablers. The prioritization of performance metrics reveals that the top three most important performance metrics for RMS are lead time, reconfiguration time, and product flexibility. The feasibility and appropriateness of the framework was tested through a case analysis in the manufacturing organization. The framework developed has a high capacity to assist designers during the adoption of the RMS and will facilitate the identification of the relevant parameters. The authors believe that researchers and professionals will find this study as a ready reference for the stepwise adoption of RMS. The study presented here is likely the first to present a hybrid framework for RMS in which a set of enablers and performance metrics are presented together.

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Authors and Affiliations

  1. Production Engineering, Veermata Jijabai Technological Institute, Mumbai, 400019, India

    Rajesh Pansare & Madhukar R. Nagare

  2. Mechanical Engineering, Swarrnim Startup and Innovation University, Gandhinagar, 382420, India

    Gunjan Yadav

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  1. Rajesh Pansare
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Contributions

All authors contributed to the study conception and framework preparation. Data collection and analysis were performed by Rajesh Pansare and Gunjan Yadav, whereas Madhukar R. Nagare supervised the study. The first draft of the manuscript was written by Rajesh Pansare, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Gunjan Yadav.

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APPENDIX 1

APPENDIX 1

Table 13

Table 14

Table 15

Table 16

Table 17

Table 18

Table 19

Table 20

Table 21

Table 13 Members of an expert panel
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Table 14 Pairwise comparison matrix for strategy and policy enablers
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Table 15 Pairwise comparison matrix for managerial and HR enablers
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Table 16 Pairwise comparison matrix for organizational enablers
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Table 17 Pairwise comparison matrix for tangible and intangible enablers
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Table 18 Pairwise comparison matrix for technical enablers
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Table 19 Pairwise comparison matrix for smart factory enablers
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Table 20 Initial comparison matrix for fuzzy-TOPSIS
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Table 21 Weighted normalized matrix for fuzzy-TOPSIS
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Pansare, R., Yadav, G. & Nagare, M.R. A hybrid framework to prioritize the performance metrics of reconfigurable manufacturing system (RMS) using fuzzy AHP–TOPSIS method. Int J Adv Manuf Technol 124, 863–885 (2023). https://doi.org/10.1007/s00170-022-10440-8

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