Purpose The purpose of this study is to develop an optimal production control strategy for manufacturing systems producing perishable goods under stochastic demand and machine unreliability. It aims to determine the best production rates and inventory levels that minimize total operational costs – including production, holding, shortage and disposal – while ensuring customer demand satisfaction. Additionally, the study investigates the impact of shelf-life variability and system uncertainties on operational performance, providing practical guidelines for cost-effective and resilient production planning in industries where perishability and equipment disruptions are critical concerns. Design/methodology/approach A stochastic production control model is developed for perishable goods in unreliable manufacturing systems under random demand, machine failures and limited shelf life. The model integrates production, inventory, shortage and disposal costs into a unified framework. A numerical optimization procedure is employed to determine the optimal production rates and inventory levels that minimize total operational costs while maintaining a desired service level. Sensitivity analyses are conducted to evaluate the impact of key parameters, such as shelf-life variability, demand fluctuations and shortage costs, on system performance and cost-efficiency. Findings The study demonstrates that the proposed stochastic production control model effectively minimizes total operational costs while maintaining a desired service level. Numerical results show that dynamically adjusting production based on machine status and inventory aging reduces shortages and product waste. Sensitivity analyses reveal that shelf-life duration, demand variability and shortage costs significantly influence optimal production and inventory policies. The model provides actionable insights for managing perishable goods in unreliable manufacturing systems, highlighting the importance of integrating demand uncertainty, machine failures and product perishability into production planning for cost-effective and resilient operations. Originality/value This study presents a novel stochastic production control framework that simultaneously addresses perishability, machine unreliability and stochastic demand – factors often treated separately in existing literature. By integrating production, inventory, shortage and disposal considerations into a unified model, it provides a practical tool for cost-effective and resilient production planning. The numerical optimization approach enables parameterized control policies adaptable to real-world variability. The findings offer both theoretical insights and actionable strategies for industries such as food, pharmaceuticals and biotechnology, where perishable products and system uncertainties critically impact operational efficiency and service levels.

Purpose In underground mining, geostatic pressure results in drift surface deformation. To ensure the stability and safety of the mining environment, a so-called ground support systems are commonly used. Today, there are various types of ground support systems, such as rockbolts, steel sets, mesh and shotcrete, cable bolts, ground anchors, etc. Ground support systems based on rockbolts are used to provide the tensioning support to the compressive strength of the rocks. Surface deformation occurring in the drift brings changes in the rockbolt protruding from the surface. Monitoring and predicting the health of the ground support would assist the decision process for rehabilitation of the ground support in the drift to maintain safe working conditions. Design/methodology/approach In this article, a methodology has been proposed aimed at the quantification of deformation of underground mining drifts through point cloud processing (PCD) and other data sources to support health monitoring, development of decision support tools and integration with the mine Trigger, Action, Response Plans (TARPs). Findings Quantification and comparison of rockbolt protruding length is indicative of the surface deformation, which can be visualised to access regions requiring rehabilitation. Research limitations/implications The results of the developed algorithms are dependent on the quality of the data governed by human process during data collection. Recommendations have been made to the industries to improve the practices. Practical implications The results were used to augment the PCD to create interactive visualisations on virtual reality (VR) and augmented reality (AR) systems for off-site or on-site inspections. Social implications The presented work improves the safety of the working environment in deep underground mines by providing quantifiable results and useable visualisations. Originality/value The proposed methodology processes point cloud data, collected in underground mining drifts, in a campaign-based manner. PCD processing focuses on (1) comparison of PCD quality, (2) computation of deformation while compensating for imperfect registration and (3) comparison of rockbolt regions using Wasserstein distance.

Purpose This paper aims to provide a systematic review of how agent-based modeling (ABM) has been applied in maintenance contexts across various industries. By consolidating findings from a range of peer-reviewed studies, this review identifies current research trends and pinpoints underexplored opportunities for future work. Design/methodology/approach A systematic literature review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Relevant papers were gathered from Web of Science and supplementary sources such as Google Scholar, applying predefined search queries and exclusion criteria. A final set of 69 papers was analyzed in detail, focusing on industry applications, methodological combinations, software usage and stated future research directions. Findings The review reveals that ABM and multi-agent systems (MAS) approaches are widely used, in the context of maintenance, in energy, infrastructure, aviation and manufacturing, often integrating simulation, optimization and machine learning techniques. Less attention has been given to healthcare, oil and gas and non-aviation military maintenance, highlighting areas with significant potential for further study. Researchers emphasize the need for real-world validation, larger-scale modeling and the incorporation of human and environmental factors. Additionally, variations in software choice reflect differing priorities, from visualization and simplicity (e.g. NetLogo) to robust coordination in multi-agent environments (e.g. JADE) and comprehensive, hybrid simulation capabilities (e.g. AnyLogic). Originality/value This review uniquely synthesizes current research on ABM in maintenance, providing a comprehensive overview of its applications, benefits and future research directions.

PurposeThe purpose of the current work is to present an intuitionistic fuzzy (IF) concept-based structured framework for selecting the optimal maintenance policy in a sugar mill.Design/methodology/approachThe proposed framework utilizes IF concept based multi-criteria decision making (MCDM) approaches, specifically IF-analytic hierarchy process (IF-AHP) and IF-Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS). IF theory based MCDM approaches utilises the hesitation present in the maintenance experts to encounter the uncertainties/vagueness to much higher degree of accuracy in decision-making. The suggested framework is used to evaluate and select the optimal maintenance policy based on six different criteria namely safety factor, cost factor, maintenance factor, reliability, risk and added values.FindingsThe IF-AHP approach has been employed to calculate the weights of the criteria and sub-criteria, while the IF-TOPSIS approach was utilized to rank the maintenance strategies. Based on the results, with a relative coefficient value of 0.7204, corrective maintenance (CM) is determined to be the best maintenance policy. For certifying the consistency of the recommended structured framework, sensitivity analysis (SA) has been also conducted.Research limitations/implicationsThe ranking results obtained from the analysis are provided to the maintenance management of the considered sugar mill for its further implementation and validation. The findings of this work are also applicable to all other sugar mill industries which are installed globally.Practical implicationsThe analysis results has been supplied to the maintenance manager of considered sugar mill industry. The implementation policy of the results will be shared with the higher management and hence once implemented the results could be tested and verified.Originality/valueThe developed framework so implemented to the considered sugar mill industry is original in nature. Also, consideration of hesitation effect in the collected raw data under the developed framework provide more authenticated decision results which proves to be useful in achieving higher availability and profitability of the industry.

PurposeThe main objective of the present investigation is to develop a novel efficient stochastic model for availability optimization of reverse osmosis machine system (ROMS) for water purification under the concepts of exponentially distributed decision variables and various redundancy strategies at the component level.Design/methodology/approachROMS is a complex framework configured in a series structure using six subsystems. Initially, a state transition diagram is developed and Chapman–Kolmogorov differential-difference equations are derived using Markov birth death process. The steady-state availability of the ROMS is derived for a particular case. The impact of variation in failure and repair rates measured on availability. Furthermore, an effort is made to predict the optimal availability of the ROMS system using the metaheuristic algorithms, namely, dragonfly algorithm (DA), grasshopper optimization algorithm (GOA) and whale optimization algorithm (WOA).FindingsIt is observed that the ROMS system predicts optimal availability of 0.999926 after five iterations with a population size of 300 by the WOA. The findings of this study are significant for reliability engineers as well as for maintenance engineers to ensure the availability of ROMS for water purification.Originality/valueIn the present investigation, a novel stochastic model is developed for ROMS, and metaheuristics algorithms are applied to predict the optimal availability.

PurposeThe purpose of this paper was to understand the difficulties related to asset management observed by experts in Brazilian organizations in light of the requirements outlined in the ISO 55001:2014 standard.Design/methodology/approachA survey was performed with asset management experts. The collected data were analyzed using frequency analysis, hierarchical cluster analysis and fuzzy technique for order preference by similarity to deal solution (TOPSIS).FindingsBased on data analysis, the most critical difficulties observed were related to managing and controlling the impact of changes in the company that affect asset management objectives; to the committing to and supporting the asset management system by the top management of the organization; to manage the processes for dealing with risks and opportunities for the asset management system and plans, and correcting failures in asset performance; and to plan and conduct actions in an integrated manner to identify and minimize adverse impacts associated with the asset management system, and afterwards verifying their effectiveness.Originality/valueThe findings of this study have important theoretical and practical contributions, since they indicate the most critical points observed in asset management in Brazil, which can be used as a source for future research and by professionals to prioritize difficulties in future planning and develop action plans to overcome them. The step-by-step methodological approach presented in this study provides professionals and researchers with a replicable method of identifying potential asset management difficulties in a given specific reality.

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

PurposeWith production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.Design/methodology/approachFifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.FindingsThe main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.Originality/valueThis paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.Design/methodology/approachTo obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.FindingsThe major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/valueThe proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

PurposeThe aim of this paper states that total productive maintenance (TPM) is an improvement tool which employs the effective utilization of employees in order to enhance the reliability of the equipment in consideration.Design/methodology/approachThis paper identifies and evaluates the factors accountable for the adoption of TPM methodology in manufacturing organizations. Twenty-four factors affecting the TPM implementation are explored and categorized into five significant categories. Afterwards, these identified TPM factors have been evaluated by using a most popular Multi-criteria decision-making (MCDM) approach namely fuzzy pivot pairwise relative criteria importance assessment (F-PIPRECIA).FindingsIn this paper, through application of F-PIPRECIA, “Behavioural factor” is ranked first while “Financial factor” the last. Considering the sub-factors, “Top management support and commitment” is ranked first while “Effective use of performance indices” is ranked the last. A further sensitivity analysis indicates the factors that need higher level of attention.Practical implicationsThe result of current research work may be exploited by the top administration of manufacturing enterprises for assessing their TPM adoption status and to recognize the frail links of TPM application and improve accordingly. Moreover, significant factors of TPM can be identified and deploy them successfully in their implementation procedure.Originality/valueThe conclusion obtained from this research enables the management to clearly understand the significance of each considered factor on the adoption of TPM in the organization and hence, provides effective utilization of resources.