Objective:

This study aims to conduct a foresight analysis of the automotive industry with a focus on structural and technological transformations driven by Industry 4.0. The automotive sector is undergoing rapid changes due to digitalization, automation, artificial intelligence, and increasing global competition. Given its strategic importance in national economic development, employment generation, and technological progress, understanding its future trajectories is essential. At the same time, the industry faces structural challenges such as economic sanctions, fragmented supply chains, limited access to advanced technologies, and institutional inefficiencies. Therefore, this research addresses two main questions: (1) What are the key drivers influencing the future of the automotive industry in the context of Industry 4.0? and (2) Based on these drivers, what plausible future scenarios can be developed for this industry?

Methodology:

This research adopts a mixed-methods (qualitative–quantitative) approach within a pragmatic paradigm. The statistical population included 10 experts and senior managers with experience in automotive technologies and digital transformation. Participants were selected through purposive sampling to ensure expert-level judgment.

In the qualitative phase, a systematic literature review of reputable academic sources was conducted to identify key factors influencing the future of the automotive industry. This process resulted in 32 initial drivers covering technological, organizational, economic, and policy-related dimensions.

In the quantitative phase, the Fuzzy Delphi method was used to screen these drivers. Experts evaluated each driver, and those with a defuzzified value above 0.7 were retained. The validity of the selected drivers was confirmed using Lawshe’s Content Validity Ratio (CVR).

Next, the final drivers were prioritized using the COPRAS multi-criteria decision-making technique. Three criteria were considered: intensity of importance, degree of certainty, and level of expert expertise. This approach enabled a structured ranking of drivers based on their relative influence on the future of the industry.

Finally, scenario development was conducted using the PANDA scenario planning framework. By combining the two most influential drivers, four distinct future scenarios for the automotive industry were constructed.

Findings:

The Fuzzy Delphi results showed that 10 out of 32 initial drivers exceeded the threshold and were selected as key determinants of the automotive industry’s future. These drivers include: information technology infrastructure, competitiveness of the automotive industry, supply chain integration, production intelligence, development of technology startups, innovation policies, internationalization of the supply chain, process automation, digitalization policies, and data-driven decision-making.

The COPRAS results indicated that “internationalization of the automotive supply chain” ranked first with a relative importance coefficient of 100%, making it the most influential driver. This reflects the importance of global integration, access to international markets, and participation in global value chains. The second most important driver was “data-driven decision-making in the automotive industry” with a coefficient of 92.46%, highlighting the critical role of analytics, artificial intelligence, and digital intelligence in modern industrial management.

Based on these two key drivers, four future scenarios were developed:

1. Smart Automotive Industry (Ideal Scenario): This scenario occurs when both international supply chain integration and data-driven decision-making are highly developed. It represents a technologically advanced, competitive, and globally integrated automotive sector.

2. Emerging Development Path (Transitional Scenario): In this scenario, international integration improves, but decision-making systems remain partially traditional. The industry shows progress but has not fully transformed digitally.

3. Resilient Automotive Industry (Adaptive Scenario): This scenario is characterized by limited global integration due to external constraints, but strong internal efforts toward digitalization and data-driven strategies. The industry focuses on self-reliance and internal innovation.

4. Lagging Automotive Industry (Crisis Scenario): This scenario reflects weak supply chain integration and traditional decision-making structures. It represents stagnation, reduced competitiveness, and technological backwardness.

Conclusion:

The study demonstrates that the future of the automotive industry will largely depend on the interaction between global supply chain integration and the adoption of data-driven decision-making systems. These two drivers play a central role in determining whether the industry moves toward a smart, competitive future or remains in a stagnant condition. Policymakers and industry leaders should prioritize digital transformation, international collaboration, and innovation-driven policies to ensure sustainable development in the automotive sector under Industry 4.0 conditions.