{"title":"Markov chain-based model for IoT-driven maintenance planning with human error and spare part considerations","authors":"Vahideh Bafandegan Emroozi , Mahdi Doostparast","doi":"10.1016/j.ress.2025.111052","DOIUrl":null,"url":null,"abstract":"<div><div>This study introduces a Markov-based optimization framework for maintenance and spare parts inventory management, enhancing cost efficiency and operational reliability in cement production. By leveraging steady-state probabilities, the model integrates real-time equipment monitoring via the Industrial Internet of Things (IoT), reducing manual inspections and mitigating human errors. A comprehensive analysis demonstrates 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 the IoT adoption rate (<strong>γ = 0.72</strong>), human error probability (HEP) (<span><math><msup><mrow><mi>p</mi></mrow><mrow><mo>[</mo><mrow><mi>T</mi><mi>o</mi><mi>t</mi><mi>a</mi><mi>l</mi></mrow><mo>]</mo></mrow></msup></math></span> = 0.137), and total cost objective (<span><math><msup><mrow><mi>z</mi></mrow><mrow><mo>[</mo><mrow><mi>T</mi><mi>o</mi><mi>t</mi><mi>a</mi><mi>l</mi></mrow><mo>]</mo></mrow></msup></math></span>= 3151,385 currency <strong>units</strong>). The model dynamically adjusts inventory replenishment policies to minimize stockouts and reliance on costly emergency orders. Results indicate that the proposed framework significantly improves maintenance scheduling, optimizes resource allocation, and reduces operational downtime. Furthermore, the study underscores the model's adaptability and its potential for integration with predictive analytics, paving the way for intelligent, data-driven maintenance strategies. These findings provide a strong foundation for advancing industrial maintenance optimization and operational efficiency.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111052"},"PeriodicalIF":9.4000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832025002534","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
This study introduces a Markov-based optimization framework for maintenance and spare parts inventory management, enhancing cost efficiency and operational reliability in cement production. By leveraging steady-state probabilities, the model integrates real-time equipment monitoring via the Industrial Internet of Things (IoT), reducing manual inspections and mitigating human errors. A comprehensive analysis demonstrates 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 the IoT adoption rate (γ = 0.72), human error probability (HEP) ( = 0.137), and total cost objective (= 3151,385 currency units). The model dynamically adjusts inventory replenishment policies to minimize stockouts and reliance on costly emergency orders. Results indicate that the proposed framework significantly improves maintenance scheduling, optimizes resource allocation, and reduces operational downtime. Furthermore, the study underscores the model's adaptability and its potential for integration with predictive analytics, paving the way for intelligent, data-driven maintenance strategies. These findings provide a strong foundation for advancing industrial maintenance optimization and operational efficiency.
期刊介绍:
Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.