A Framework for the Selection of Material Handling Equipment Using Fuzzy FUCOM: A Case Study in the Automotive Industry
Purpose: The selection of material handling equipment is a strategic decision for companies, as it requires significant capital and affects operational efficiency. In the design of an operational industrial system, the decision to select the most appropriate equipment should consider multiple compet...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Sanayi ve Teknoloji Bakanlığı
2025-07-01
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| Series: | Verimlilik Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/4323739 |
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| Summary: | Purpose: The selection of material handling equipment is a strategic decision for companies, as it requires significant capital and affects operational efficiency. In the design of an operational industrial system, the decision to select the most appropriate equipment should consider multiple competitive criteria together. This research aims to provide a systematic method for prioritizing the criteria involved in the selection of MHE for assembly lines in the automotive sector.Methodology: This paper presents an application based on a real problem in a bus manufacturing plant. To gain insight into the experience and knowledge of the experts, the study was conducted from a phenomenological perspective and involved nine experts from different departments. The experts' evaluations were analysed via F-FUCOM.Findings: Research results show that the purchase cost, loading and unloading speed of equipment and adaptability of equipment to plants are the three most important factors in the selection of material handling equipment.Originality: In the automotive sector, expert opinion is rarely used in material handling equipment selection, and no case study on truck production exists in the literature, making this study an original contribution. In addition, the research is significant for its simultaneous evaluation of 14 criteria and the inclusion of insights from experts with diverse experiences. |
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| ISSN: | 1013-1388 |