This study presents a Markov-based optimization framework for maintenance and spare parts inventory management, designed to enhance cost efficiency and operational reliability in a cement production facility. By leveraging steady-state probabilities, the model integrates real-time equipment monitoring through the Industrial Internet of Things (IoT), significantly reducing the need for manual inspections and mitigating associated human errors. The analysis indicates that level-2 preventive maintenance (PM) achieves the highest steady-state probability, effectively balancing cost minimization and system reliability over a 36-period planning horizon. Key optimization variables include IoT adoption rate (γ = 0.72), human error probability (HEP) (p^([Total]) = 0.137), and total cost objective (z^([Total])= 3,151,385 currency units). The model dynamically adjusts inventory replenishment policies to minimize stockouts and reduce dependence on costly emergency orders. Results demonstrate that the proposed framework enhances maintenance scheduling, optimizes resource allocation, and mitigates downtime. Furthermore, the study highlights the model’s adaptability and potential for integration with predictive analytics, paving the way for intelligent, data-driven maintenance strategies. These findings provide a robust foundation for advancing operational efficiency and maintenance optimization in industrial settings.

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