Introduction and Purpose:The rapid development of digital technologies and Industry 4.0 has transformed supply chain management, logistics, distribution, and competition. Artificial intelligence (AI) has become a driver of digital transformation because it can process large volumes of data, identify patterns, generate predictions, and support managerial decision-making. These capabilities are particularly relevant to automotive parts distribution, where product variety, demand uncertainty, geographical dispersion, inventory requirements, and the need for rapid delivery create complex challenges. Although previous studies have examined AI applications in supply chains, logistics, forecasting, and optimization, less attention has been devoted to systematically identifying the broader consequences of AI implementation in automotive parts distribution networks. Therefore, this study aimed to identify, validate, prioritize, and conceptually model the consequences of implementing AI technologies in the market distribution network of the automotive parts industry. The study sought to move beyond a narrow technology-centered perspective and explain how AI can generate interconnected organizational, financial, logistical, and customer-related outcomes.
Methodology:The research employed an applied, exploratory mixed-method approach. In the qualitative phase, semi-structured interviews were conducted with experts possessing knowledge and experience in AI, digitalization, logistics, supply chain management, distribution networks, and the automotive parts industry. Experts were selected through snowball sampling, and interviews continued until theoretical saturation was achieved. Qualitative data were analyzed using thematic analysis, including extraction of initial codes, development of organizing themes, and construction of overarching themes. The quantitative phase involved ten experts. First, fuzzy Delphi was used to screen and validate the consequences identified through thematic analysis. Subsequently, the Fuzzy Best-Worst Method (FBWM) was applied to determine the relative importance and final weights of the validated consequences. Experts identified the best and worst consequences, performed fuzzy pairwise comparisons, and their judgments were aggregated. The resulting mathematical model was solved to obtain fuzzy weights, which were then defuzzified to determine final importance weights.
Finding:The thematic analysis identified four major categories: organizational consequences, financial consequences, logistics and distribution network consequences, and customer-related consequences. Organizational consequences included improved business processes, analytical capability, organizational efficiency, competitive capability, and reduced human and process errors. Financial consequences included improved long-term operating profit, reduced distribution-network costs, and reduced long-term company costs. Logistics and distribution consequences included greater supply chain transparency, improved forecasting capability, service customization, organizational decision quality, delivery quality, crisis management, distribution-network optimization, logistics-operation optimization, warehouse-process optimization, transformation of logistics and distribution models, logistics sustainability, security-threat identification, reduced human workload, and reduced dependence on traditional methods. Customer-related consequences included increased customer trust, enhanced security of customer interactions, improved customer experience, and improved customer satisfaction. Quantitative results showed that improved organizational decision quality was the most important consequence, with a final weight of 0.06465, followed by improved customer experience at 0.05264 and improved long-term operating profit at 0.05142. These results indicate that AI creates value beyond automation and cost reduction by improving decision quality, customer value, and long-term performance.
Discussion and Conclusion:The findings demonstrate that AI implementation in automotive parts distribution should not be regarded as an isolated technological intervention. Instead, it represents a strategic organizational capability generating interconnected consequences across decision-making, logistics, financial performance, and customer value. The high importance assigned to improved organizational decision quality indicates that a central contribution of AI is transforming data into actionable information and supporting more effective managerial decisions. Better decisions can subsequently contribute to inventory management, distribution planning, logistics optimization, crisis management, and resource allocation. From a managerial perspective, AI implementation should therefore be integrated into the broader digital transformation strategy rather than treated merely as an information-technology project or software acquisition. Organizations should first identify business problems, information requirements, and decisions requiring improvement and then select appropriate AI technologies. The results also highlight the importance of data integration, analytical capabilities, and organizational coordination. From a marketing perspective, AI can transform distribution into a value-creating capability by improving demand forecasting, product availability, service responsiveness, customization, and customer experience. From a financial perspective, the importance of long-term operating profit suggests evaluating AI investments through both immediate and long-term benefits. The conceptual model therefore portrays AI implementation as a multidimensional value-creation process in which improved organizational decision quality can contribute to better operational and logistics performance, enhanced customer outcomes, improved financial results, and competitive advantage. The framework provides a basis for managerial decision-making, AI investment prioritization, and digital transformation planning. However, because the study is context-specific, applying its findings to other industries requires caution. Future research c