Towards Automated Quality Control in Industrial Systems: Developing Markov Decision Process Model for Optimized Decision-Making

Abstract

In the context of rapidly evolving industrial environments, optimizing decision-making for quality control is crucial. This paper develops a Markov Decision Process (MDP) model aimed at enhancing automated quality control and reducing scrap in manufacturing systems, addressing challenges posed by complex and uncertain decision scenarios. The study focuses on improving the sub-key element of quality-accuracy within a Performance Measurement System (PMS) framework, specifically targeting scrap minimization and cost reduction. The research employs a mathematical model that integrates vector random processes, each representing critical factors such as machine condition, operator behaviour, tools, and materials. These factors are modeled as individual one-dimensional MDPs, which are combined to create a multi-dimensional MDP capable of monitoring and offering optimal policy for minimizing scrap rates and costs. The research methodology leverages advanced data analytics, statistical modeling, and real-time monitoring to accurately estimate transition probabilities and optimize policies. Different MDP models and methods are explored to enhance adaptability and iterative learning, allowing for optimal policy refinement over time. The proposed model is validated through its application to a real-world printing enterprise identified critical element, demonstrating a reduction in scrap and costs. This improvement underscores the model’s effectiveness in practical settings, offering structured, subsystem-specific interventions that enhance manufacturing quality control. The results hold both theoretical and practical significance. Theoretically, the study contributes to the body of knowledge on MDP modeling for industrial quality control, providing a scalable approach that addresses complex interdependencies and decision-making under uncertainty. Practically, the model offers a robust tool for optimizing manufacturing processes, supported by modern IT systems, integration of advanced technologies, predictive maintenance, and data-driven decision-making. This integrated approach enables manufacturers to proactively identify and mitigate quality issues, enhancing operational efficiency, reducing waste, and driving continuous improvement in industrial systems.