A Novel BWM-RADAR Approach for Multi-Attribute Selection of Equipment in the Automotive Industry

Nikola Komatina

doi:10.31181/smeor21202531

Authors

Nikola Komatina University of Kragujevac, Faculty of Engineering, Kragujevac, Serbia Author

DOI:

https://doi.org/10.31181/smeor21202531

Keywords:

MADM, BWM, Automotive industry, RADAR, Green Production

Abstract

This study examines the application of Multi-Attribute Decision Making (MADM) techniques for selecting an optimal technical solution in the automotive industry, specifically addressing the automatic adjustment and control device of a parking brake cable. The research begins by using the Best-Worst Method (BWM) to determine the weights of criteria, such as device speed, price, weight, and calibration period, based on expert input from maintenance professionals and operators at an automotive original equipment manufacturer in automotive industry. The main objective is to identify the solution that best balances operational efficiency with long-term stability and adaptability to production requirements. In the subsequent phase, both the RADAR method and its modified version, RADAR II, are applied to rank the alternatives. The original RADAR method, which employs ratio-based normalization, tends to favor alternatives that demonstrate stable performance across all criteria, whereas RADAR II, utilizing difference-based normalization, accentuates alternatives that excel in particular aspects. Comparative analysis reveals that although both methods produce generally consistent overall rankings, nuances in the normalization process can lead to differences in the relative prominence of certain solutions. Sensitivity analysis further confirms the robustness and reliability of these approaches, underscoring the importance of selecting a method that aligns with the specific decision-making context. Ultimately, the study demonstrates that a carefully tailored MADM approach, integrating both RADAR and RADAR II techniques, provides valuable insights and supports effective decision-making in complex industrial environments.

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References

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