Details

Autor(en) / Beteiligte

• Pansare, Rajesh
• Yadav, Gunjan
• Nagare, Madhukar R.

Titel

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

Ist Teil von

• International journal of advanced manufacturing technology, 2023, Vol.124 (3-4), p.863-885

Ort / Verlag

London: Springer London

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.