Digital Transformation and Administration Innovation In Press 2026 09 01 Designing a Causal–Stage Model of Artificial Intelligence–Based World-Class Total Productive Maintenance for the Iranian Automotive Industry Designing a Causal–Stage Model of Artificial Intelligence–Based World-Class Total Productive Maintenance for the Iranian Automotive Industry 1 16 EN Journal Article 2026 05 21 <p>In the era of Industry 5.0, the transformation of Total Productive Maintenance (TPM) in the automotive industry extends far beyond the adoption of conventional maintenance practices or the fragmented use of artificial intelligence applications. Such a transformation requires an integrated model that explains how AI-based maintenance capabilities should be identified, causally structured, sequenced, and translated into an executable architecture for achieving world-class performance. Although research on intelligent maintenance and predictive analytics has expanded considerably, much of the existing literature has focused on algorithmic accuracy, condition monitoring, or narrowly defined technological applications, while paying limited attention to how artificial intelligence capabilities can be structured and implemented to support World-Class Total Productive Maintenance. To address this gap, the present study designs a causal–stage model of Artificial Intelligence–Based World-Class Total Productive Maintenance (AI-WCTPM) for the Iranian automotive industry. The findings reveal a six-level architecture in which the real-time communication network and cyber–physical security constitute the foundational layer; edge processing and smart sensors form the perception and data-processing layer; intelligent knowledge management functions as the central knowledge platform; dynamic failure prediction and resilient supply chain operate as advanced analytical engines; preventive quality control and intelligent continuous improvement constitute the optimization and action layer; and world-class performance indicators represent the final outcome layer. The proposed AI-WCTPM model integrates causal influence, implementation sequencing, AI-tool alignment, and performance monitoring into a coherent data–knowledge–decision architecture. Expert validation confirms the structural coherence, content adequacy, and practical applicability of the model. This study advances intelligent maintenance research by shifting the focus from isolated algorithmic applications toward a stage-based architecture for World-Class TPM and provides automotive managers with a structured roadmap for prioritizing AI investments, reducing implementation risk, and aligning maintenance transformation with world-class performance objectives.</p> https://journaldtai.com/index.php/jdtai/article/download/341/311