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Article type: Research Article
Authors: Omerali, Mete; * | Kaya, Tolga
Affiliations: Department of Management Engineering, IstanbulTechnical University, Macka, Istanbul, Turkey
Correspondence: [*] Corresponding author. Mete Omerali. ORCID: 0000-0001-7275-394X, E-mail: omerali18@itu.edu.tr.
Abstract: Digitalization is the key trend of the Industry 4.0 revolution. Industrial companies are transforming the way they design and maintain their products and solutions. The user requirements become more demanding. Competition among the manufacturing companies is at its limits and transforms the products to be more complex. Yet, other challenges such as faster time to market, higher quality requirements and legislation force enterprises to provide new ways of design, manufacture and service their end products. Product Lifecycle Management (PLM) is a key solution to track the entire lifespan of the product from idea to design, design to manufacture and manufacture to service. Besides the complexity of products and production, the selection of the right PLM solution which will become the backbone of enterprises is an open problem. In this paper, a thorough literature review is conducted to analyze the most important features for selecting the right PLM solution for manufacturing firms. Moreover, to overcome the challenge of decision makers’ (DM) subjective judgments, a novel interval value spherical fuzzy COPRAS (IVSF-COPRAS) multi-criteria decision making (MCDM) method is introduced. The paper aims to help enterprises rapidly identify the best alternative vendor/solution to be selected based on the need of the organization. In order to show the applicability, DM inputs are collected from a leading defense company where the PLM selection process is ongoing. The industrial case study is provided to demonstrate the success of the proposed selection framework.
Keywords: Digitalization, industry 4.0, product lifecycle management (PLM), multi-criteria decision making (MCDM), IVSF-COPRAS
DOI: 10.3233/JIFS-219201
Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 425-438, 2022
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