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With 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.

Fifteen 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.

The 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.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

Through 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.

To 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.

The 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%.

The 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.

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

The study aims to identify and evaluate relevant criteria in the proposal and support of public administration policies for preventive maintenance comprised in a conservation approach to built heritage and aligned with local sustainable development of the historic center of the city of Sobral, in Brazil.

A novel multicriteria decision model adopting the Bayesian best-worst method is presented and its application and results are described. Though a systematic procedure, criteria were selected in order to protect the tangible and intangible values of cultural heritage, as well as its sustainable development. Then experts evaluate these criteria through an elicitation instrument.

The results show that for the decision problem over preventive maintenance, social contribution and historical record of built heritage are more important than its structural vulnerability, while architecture is less relevant. Due to the low restrictions, the subcriterion related to this property has the least influence. The weights can assist in the characterization of measures and policies for the protection of the built cultural heritage.

The use of a novel decision-making method in cultural heritage is an important initiative, given the frequent use of simple and inefficient methods. The identified and weighted criteria are important data to characterize the scenario and the topic. The results contribute to protection and development of the built heritage, encouraging the implementation of preventive conservation in the historic center, conferring to the public administration valuable information to support and propose initiatives.

This paper aims to identify the different system approach using Building Information Modelling (BIM) technology that is equipped with decision making processes. Maintenance planning and management are integral components of the construction sector, serving the broader purpose of post-construction activities and processes. However, as Precast Concrete (PC) construction projects increase in scale and complexity, the interconnections among these activities and processes become apparent, leading to planning and performance management challenges. These challenges specifically affect the monitoring of façade components for corrective and preventive maintenance actions.

The concept of maintenance planning for façades, along with the main features of information and communication technology tools and techniques using building information modeling technology, is grounded in the analysis of numerous literature reviews in PC building scenarios.

This research focuses on an integrated system designed to analyze information and support decision-making in maintenance planning for PC buildings. It is based on robust data collection regarding concrete façades' failures and causes. The system aims to provide appropriate planning decisions and minimize the risk of façade failures throughout the building's lifetime.

The study concludes that implementing a research framework to develop such a system can significantly enhance the effectiveness of maintenance planning for façade design, construction and maintenance operations.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.

The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.

The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.

This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.

The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.

The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.

The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).

Competencies are significant predictors of employee outcome. Nowadays, new technologies are changing maintenance processes and workflow. The role of employees and their competencies will therefore undergo decisive changes in the future. Therefore, a well-designed competency model for maintenance departments is important. The purpose of this paper is to develop a maintenance 4.0 competency model applicable to all industrial sectors by adapting it to the specificities of each sector.

The research methods consist of a comprehensive literature review on the main characteristics of the competency model and the individual competencies needed for the maintenance 4.0 employees. Interviews were conducted in order to validate and prioritize the required competencies for maintenance 4.0 employees identified in the literature.

The maintenance 4.0 competency model combines the required competencies in maintenance 4.0 and crosses the three hierarchical levels: managers, engineers and technicians. These competencies are organized in terms of four categories: technical, personal, social and methodological. In addition, a degree of importance for each competency is assigned as very important, moderately important and slightly important. As a result, this study identified the essential competencies for maintenance 4.0 stakeholders, where 12 competencies are considered very important for maintenance 4.0 technicians, 19 for engineers and 18 for managers.

This work has some limitations. First, although the articles related to competencies and their classification were selected very carefully, it is difficult to eliminate the probability of overlooking publications. Second, the limitation of the study is based on the difficulty of implementing the model in a case study, given that a minority of industrial companies have implemented maintenance 4.0 technologies in Morocco.

This work has practical implications for both individuals and institutions (companies and academies) to cope with new competency requirements in maintenance 4.0. Organizations can use the model in the recruitment process and for the identification of training needs. The results of the research will also contribute to identifying the scope of competencies of the maintenance 4.0 actors (engineer, manager and technician), which, in practice, contributes to the creation of requirements for the candidates applying for a job in the maintenance department. Additionally, educational institutions should make the necessary changes to their curricula to suitably prepare students for the required maintenance 4.0 competencies.

The social implications of the article result from the contribution to the development of maintenance competencies. Individuals can use this model for their own personal development. Furthermore, companies can use this model to define job profiles for vacancies in M4.0. Therefore, using the model for training program implementation has a positive effect on employee job satisfaction and employees ’morale.

This research develops a novel maintenance 4.0 competency model by categorizing the maintenance workforce into three hierarchical levels: managers, engineers and technicians. In addition, the competency requirement is prioritized to three degrees: very important, moderately important and slightly important. According to the previous studies conducted on maintenance 4.0 and employees' competencies, this study revealed that no research has developed a competency model for maintenance 4.0. Hence, this model is unique, generic and integrative since it presents the most relevant competencies for the three hierarchical levels. Moreover, this work combines the results of the literature review and the experts' returns. This model can be useful in the recruitment of new maintenance employees, the evaluation of their performance and the identification of training needs to cope with new changes in maintenance competencies.

The purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.