Determination of optimal robot investment decisions inautomotive manufacturing processes using multi-criteriadecision-making models

The global manufacturing industry is currently experiencing a profound structural transformation under the influence of the Industry 4.0 paradigm. Within this evolving ecosystem, the automotive sector has emerged as one of the principal driving forces, where increasingly stringent tolerance requirements, shortened product life cycles, and the pursuit of near-zero defect production have progressively exposed the limitations of conventional manufacturing practices. In particular, welding processes carried out in the Body-in-White (BIW) line, which play a critical role in ensuring vehicle safety and structural durability, have become among the most intensively automated production stages due to their demanding precision requirements. Nevertheless, the substantial capital expenditures associated with these systems, coupled with the risk of technological obsolescence, have transformed the selection of the most appropriate industrial robot into a strategically significant decision-making problem for manufacturing enterprises. In this study, a comprehensive decision-making framework was developed to identify the most suitable industrial robot for the modernization of a welding line within the automotive supplier industry. Within the proposed research methodology, the FUCOM method was employed to determine the relative importance of the evaluation criteria, while the MARCOS method was integrated to establish the priority ranking of the alternatives. During the construction of the model, the judgments of six experts possessing extensive sectoral experience were incorporated, and nine globally recognized robot brands were evaluated according to nine fundamental criteria encompassing technical performance, financial constraints, and digitalization compatibility dimensions. The findings of the study indicate that the "Investment Costs" criterion possessed the highest significance weight, followed respectively by the "Digitalization/Interoperability" and "Operating Speed" criteria. According to the ranking results obtained through the MARCOS algorithm, alternative A9 achieved the highest overall performance by establishing the most balanced compromise between technical competence and financial sustainability. Furthermore, the sensitivity analysis conducted through 90 distinct scenarios demonstrated that the proposed decision model produces highly stable and resilient outcomes against potential fluctuations in criterion weights.