Purpose This research aims to examine the impact of Industry 4.0 technologies on supply chain costs (manufacturing, administration, warehouse, distribution, capital and installation), specifically within the automotive industry, which is highly competitive and cost-sensitive. Design/methodology/approach To achieve this goal, the interval-valued Pythagorean fuzzy and Weighted Aggregated Sum Product Assessment (IVPF-WASPAS) method – an approach that integrates Pythagorean fuzzy sets with the WASPAS technique – was employed. The analyses were based on 14 interviews with seven experts. Findings The results reveal that Industry 4.0 technologies have the most significant impact on cost reduction in installation, followed by warehousing and distribution costs. These technologies significantly enhance inventory management, route optimization and predictive maintenance capabilities. Administration and manufacturing costs showed moderate improvement, reflecting increased process efficiency through the use of data-driven systems. However, the impact of Industry 4.0 technologies on reducing capital costs was found to be relatively limited, as these costs are primarily driven by the initial investments required for infrastructure and technological upgrades in digital transformation processes. Practical implications The findings provide strategic guidance for managers in the automotive sector on which Industry 4.0 technologies offer the highest cost benefits. Results show that digital tools are most effective in reducing after-sales, warehousing and distribution costs. This enables firms to allocate resources more effectively and prioritize high-impact technologies. It also supports data-informed decisions for enhancing operational efficiency under budget constraints. Originality/value This study uniquely integrates the dynamic capabilities framework and the resource-based view with a fuzzy multi-criteria decision-making approach to evaluate Industry 4.0 technologies in a cost-focused supply chain context. It applies the IVPF-WASPAS method, which remains underutilized in supply chain cost analysis.
Bağdat et al. (Thu,) studied this question.