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This paper proposes a costing framework with the objective of providing reliable, relevant, and timely information about the actual costs and the cost efficiency of planned maintenance jobs. The proposed framework includes direct materials, direct labor, and support services costs. The traceability criterion is used for assigning direct materials and direct labor costs to planned maintenance jobs. However, support services costs are allocated to planned maintenance jobs using the concepts and techniques of activity‐based‐costing (ABC). The proposed framework provides a complete audit trail of planned maintenance costs. It also provides a functionality that could be added to enterprise resource planning (ERP) systems.

Maintenance control plays a key role in achieving the stated objective of effectiveness and efficiency of the maintenance system. In a recent paper, Gits proposed a reference framework that guides in the design and structuring of maintenance control. The framework is conceptual in nature and its use in practice is limited. Poses Gits’ framework as a set of mathematical programming models. Extends some of Gits’ procedure for maintenance control, then outlines the required expansion in the maintenance management information system (MMIS) in order to provide the needed data to execute the models. The models provide operational plans and schedules ready for implementation.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

This paper aims to optimize the maintenance scheduling of emergency rescue wagons in railway companies using a genetic algorithm (GA). It offers an integrated model for simultaneously solving maintenance planning, preventive maintenance, prognostic information and resource planning from which optimal levels of the system performance in terms of cost and repair time could be determined.

This study initially evaluates the previous types of research in presenting the optimal model of the rescue train wagon for maintenance and repair planning and lists the identified criteria based on experts' opinions using fuzzy analytic hierarchy process (FAHP) and stepwise weight assessment ratio analysis (SWARA) techniques. Then, the final weight of the desired criteria is calculated. Later, the final decision matrix is evaluated by the experts. The final normal decision matrix is formed to select the optimal maintenance and repair planning plan based on the GA. Finally, two strategies including joint optimization strategy of preventive maintenance planning are compared with the independent preventive maintenance planning strategy.

According to the primary results, three primary parameters, including technology, human damage caused by negligence and the average failure rate, should be considered for launching the GA model. Based on the results of the second part; by comparing the preventive maintenance planning strategy independently, the joint optimization strategy reduces the total cost of production up to 8.25%. Comparison results show that the total cost of joint optimization production is less than independent preventive maintenance and repair planning. Moreover, the value of total process time in joint optimization strategy was reduced by 1.2% compared to independent preventive maintenance (PM) planning (from 137.90 to 136.20 h).

The novelty of this paper lies on the application of the GAs to develop an optimized PM scheduling to localize the maintenance planning for maximizing productivity, avoid train accidents, reduce costs and increase efficiency and capacity.

Maintenance is a key lever for reducing costs in manufacturing companies. Researchers have focused in the past on the importance of integrating maintenance and production planning to obtain total cost savings and have proposed a number of models. Nonetheless, all such contributions take a static perspective and do not consider the dynamics of markets, e.g. when some already committed preventive maintenance (PM) interventions are to be rescheduled. Therefore, the aim of this paper is to suggest a new decision‐making process to manage the rescheduling of PM interventions and to show – through the proposal of an ad hoc planning model – the economical savings from expressly considering rescheduling costs in re‐planning PM interventions.

In this paper the authors used six case studies to infer both the current process of production and maintenance integrated planning and the decision‐making process followed for rescheduling PM interventions. From the current practices the authors derived a new managerial framework for managing rescheduling events and they have built two mathematical models to compare the current and the proposed frameworks by using real data.

Considering rescheduling costs in the joint production and maintenance planning process leads to significant economical savings under all the scenarios tested. In particular, the use of the proposed model allows significant reductions in maintenance costs while keeping constant production costs. This makes the implementation of the authors' proposal in real companies easier.

This research originates from the wish of a real company to better investigate the issue of rescheduling interventions. To the best of the authors' knowledge this is the first study to face the problem of rescheduling PM interventions and to present a mathematical model to support managers in such a decision‐making process.

The purpose of this paper is to provide comprehensive information on preventive maintenance (PM) planning and methods used in the industry in order to achieve an effective maintenance system.

The literature review is organized in a way that provides the general overview of the researches done in the PM. This paper discusses the literatures that had been reviewed on four main topics, which are the holistic view of maintenance policies, PM planning, PM planning concept and PM planning-based in developing optimal planning in executing PM actions.

PM policy is one of the original proactive techniques that has been used since the start of researches on maintenance system. Review of the methods presented in this paper shows that most researches analyse effectiveness using artificial intelligence, simulation, mathematical formulation, matrix formation, critical analysis and multi-criteria method. While in practice, PM activities were either planned based on cost, time or failure. Research trends on planning and methods for PM show that the variation of approaches used over the year from early 1990s until today.

Research about PM is known to be extensively conducted and majority of companies applied the policy in their production line. However, most analysis and method suggested in published literatures were done based on mathematical computation rather than focussing on solution to real problems in the industry. This normally would lead to the problems in understanding by the practitioner. Therefore, this paper presented researches on PM planning and suggested on the methods that are practical, simple and effective for application in the real industry.

The originality of this paper comes from its detail analysis of PM planning in term of its research focus and also direction for application. Extensive reviews on the methods adopted in relation to PM planning based on the planning-based such as cost-based, time-based and failure-based were also provided.

The purpose of this paper is to present previous studies and reports which have shown that most school buildings in the USA suffer from inadequate physical conditions. As American school buildings become “older”, it is essential for every school district to have an effective facilities maintenance management plan of its facilities. Therefore, the main purpose of this study is to identify and analyze how facilities maintenance is planned, managed and carried out by large public schools in the State of Texas, USA.

Guidelines taken from the literature drove the development of a survey questionnaire, which was sent to 320 school facilities managers from four major metropolitan areas in Texas. Elementary, middle and high schools with enrollments of 750 students or more were selected from the Texas Education Agency's web site. The responses were received in a secure database established at an online web site, where participants' identities were kept confidential.

The overall findings of this research indicate a poor quality of facilities maintenance management practices exists in large public schools in Texas. Nevertheless, the study also found that schools following the guidelines set by the US Department of Education tend to have comparatively more detailed and contemporary information about their facility's condition. As a result, these schools are able to predict the projected needs of the school, including its maintenance needs.

The results of this study can be adapted and used by any public school that would like to provide high‐quality school facilities for healthy and supportive teaching and learning environments.

The purpose of this paper is to determine how a small country's military force and a small country's non‐governmental organization (NGO) plan for and set up equipment maintenance, spare parts inventories in connection with man‐made humanitarian disasters. Additionally, it seeks to determine how the physical context, organizational structure and governance affect the planning and set‐up.

A research model that combines organizational theory with spare parts inventory theory is developed. Case study research methodology is used and observations and findings are discussed within the research model in order to answer predefined hypotheses.

Regarding planning procedures, as well as how the maintenance concept and spare parts inventory are set up, the research concludes that the organizational structure and governance of the organization contributing to the humanitarian operation in question are more important than physical context of the operation itself. Further it is concluded that the maturity level, when it comes to inventory control issues, is different for the two cases in question. None of the cases, however, utilize modern optimization methods and tools.

Qualitative data from the two case studies give the possibility for in depth analysis of the case study findings. Lack of quantitative data means that it has not been possible to statistically reject or accept the hypotheses. More research is needed to present a template and/or processes based on the findings in the research.

By applying the research model developed in this study, organizations that contribute to humanitarian disasters could more easily assess their own possibility for effective maintenance and spare parts inventory planning and set up.

The study of the planning and set up of maintenance and spare parts inventories for both military and NGO players in connection with a man‐made humanitarian disaster is new. Further, the development of spare parts inventory theory into organizational theory is relatively new. Limited research is available within this field. The paper should be of interest to both practitioners and researchers within the field of maintenance and spare parts inventories in general, and in connection with humanitarian disasters in particular.

The purpose of this paper is to examine how certain limitations of the current approaches to planning and scheduling of aircraft heavy maintenance can be addressed using a single integrated framework supported by unified data structures.

The “unitary structuring technique”, originally developed within the context of manufacturing planning and control, is further enhanced for aircraft heavy maintenance applications, taking into account the uncertainty associated with condition‐based maintenance. The proposed framework delivers the advanced functionalities required for simultaneous and dynamic forward planning of maintenance operations, as well as finite loading of resources, towards optimising the overall maintenance performance.

Execution of maintenance operations under uncertainty involves materials changes, rectification and re‐assembly. It is shown that re‐scheduling of materials (spare‐parts), resources and operations can be taken care of by simultaneous and dynamic forward planning of materials and operations with finite loading of resources, using the integrated framework.

As part of adopting the proposed framework in practice, it needs to be guided by an overall methodology appropriate for application‐specific contexts.

The potential direct benefits of adopting the proposed framework include on‐time project completion, reduced inventory levels of spare‐parts and reduced overtime costs.

Existing approaches to aircraft maintenance planning and scheduling are limited in their capacity to deal with contingencies arising out of inspections carried out during the execution phase of large maintenance projects. The proposed integrated approach is, capable of handling uncertainty associated with condition‐based maintenance, due to the added functionalities referred to above.

The purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.

An embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.

The studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.

The results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.

First and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the state of the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners.