High upfront costs and weak supply‑chain integration are the chief bottlenecks to Industry 4.0 adoption in Thailand's auto sector; expert-driven prioritization shows financing and supply‑chain coordination should come before capability-building to unlock the biggest gains.

Main Finding

High initial investment and weak supply-chain integration are the top, causally dominant barriers slowing Industry 4.0 adoption in the Thai automotive industry; addressing these first will most effectively accelerate digital readiness. Key enabling practices are customization, flexible production, human–machine collaboration, and cybersecurity. The study also contributes a novel integrated fuzzy MCDM framework (Fuzzy BWM → PROMETHEE II → DEMATEL) for prioritization and causal analysis of adoption barriers.

Key Points

• Objective: Prioritize barriers and uncover causal relationships that impede I4.0 adoption in Thailand’s automotive sector to inform firm strategy and policy.
• Most influential supportive practices (from Fuzzy BWM):
  • Customization
  • Flexible production
  • Human–machine collaboration
  • Cybersecurity
• Most critical barriers (from PROMETHEE II + DEMATEL):
  • High initial investment (largest causal driver)
  • Supply chain integration (major causal driver)
    These two drive other dependent barriers; resolving them yields largest downstream effects.
• Methodological contribution: Integrated fuzzy multi-criteria decision-making pipeline combining BWM for criteria weighting, PROMETHEE II for ranking alternatives/barriers, and DEMATEL for mapping causal influence—applied to I4.0 adoption barriers.
• Practical recommendation: Prioritize financing/support mechanisms and supply-chain coordination initiatives before or alongside capability-building, to maximize transformation performance.

Data & Methods

• Data sources: literature survey and structured expert knowledge elicitation (experts in Thai automotive/I4.0 domain). (Paper does not report firm-level panel data; approach is expert-driven.)
• Overall approach: three-stage fuzzy MCDM model:
• Fuzzy Best–Worst Method (BWM) to derive weights for practices and barrier criteria under uncertainty.
• Fuzzy PROMETHEE II for systematic ranking/prioritization of barriers using the weighted criteria.
• Fuzzy DEMATEL to reveal causal (driver–dependent) relationships among the critical barriers.
• Rationale: fuzzy logic handles expert uncertainty/imprecision; BWM reduces pairwise-comparison burden; PROMETHEE II supplies preference ranking; DEMATEL identifies directional influence and causal structure.
• Limitations to note: context-specific (Thai automotive), relies on expert judgments (subject to bias), sample/response details not given here — limits statistical generalizability.

Implications for AI Economics

• Investment bottlenecks shape the pace and distribution of AI/Industry 4.0 adoption: High upfront capital requirements are primary constraints — suggesting market failures (finance/credit frictions) that policy can target with subsidies, concessional loans, leasing models, or co-investment to accelerate adoption and improve social returns.
• Supply-chain integration matters for diffusion: AI-driven manufacturing gains depend on interoperable digital supply chains. Policies and standards facilitating data sharing, interoperability, and contract/coordination mechanisms can unlock network effects and increase ROI on firm-level AI investments.
• Prioritization of complementary capabilities: Firms and policymakers should sequence interventions — address financing and supply-chain coordination first to create the enabling environment, then invest in workforce training, cybersecurity, and production flexibility to capture productivity gains from AI and automation.
• Labor and skill implications: Emphasis on human–machine collaboration implies demand for reskilling and hybrid skillsets; targeted training programs can improve adoption outcomes and mitigate displacement risks.
• Targeting and cost-effectiveness: Using an evidence-based prioritization (as in this study) can help allocate scarce public funds more efficiently—e.g., focused financing for high-impact bottlenecks rather than uniform subsidies.
• Methodological relevance for AI economics research: The integrated fuzzy BWM–PROMETHEE II–DEMATEL pipeline is a useful toolkit for:
  • Prioritizing barriers/opportunities where hard data are scarce and expert judgment is required.
  • Mapping causal influence among institutional, financial, technical, and human-capital factors affecting AI diffusion.
  • Designing policy experiments that sequence interventions according to causal leverage.
• Generalizability caution: Results should be adapted to local industry and country contexts; replication with firm-level quantitative data or cross-country comparisons would strengthen external validity.