A TOPSIS-based approach for the best match between manufacturing technologies and product specifications. (30th November 2020)

• Record Type: Journal Article
• Authors: Bertolini, Massimo; Esposito, Giovanni; Romagnoli, Giovanni
• Is Part Of: Expert systems with applications. Volume 159(2020)
• Publication Date: 2020-11-30
• Subjects: Multi-criteria decision support-system -- DSS -- Multi-criteria decision-making -- TOPSIS -- Additive manufacturing -- Technology selection; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113610
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Product development needs an analysis on the manufacturing processes to be used. Technique for Order Preference by Similarity to Ideal Solutions fits to this issue. The proposed model allows to see how to match product and technology specifications. We applied our model to an industrial case to prove its applicability. The proposed model allowed to select the most suitable technology for a given part. Abstract: When manufacturing a product, companies must consider the specifications of its design and choose the manufacturing technology that matches them the best in terms of product quality, production time and costs. Since all these parameters can be represented by several different and conflicting indicators, the problem of technology selection can be defined as a multi-criteria decision-making (MCDM) problem. Although several mathematical models have been developed to solve similar problems, recent literature still presents a lack of specific applications of renowned decision-making techniques to the technology matching problem in the manufacturing sector. This study attempts to fill this gap by proposing a manufacturing-oriented model of the Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), one of the most solid and robust MCDM methods. The solution we present, which is designed for general manufacturing processes, has been applied to the specific case of a producer of food and beverage plants and equipment that is interested in reengineering one of its products. Due to the complexity of the food and beverage industry, the case study is useful for supporting the definition of the general model and validating its applicability. Further, the results of the specific application prove the effectiveness of our model.