safety,risk and reliability
Reza Masoudi; Mohammad Reza Zahedy; Morteza piri
Abstract
This study aims to enhance the performance of maintenance teams by proposing a mathematical programming model for team formation, focusing on the maximization of knowledge sharing. In this model, key factors influencing knowledge sharing — including knowledge absorptive capacity, knowledge-sharing ...
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This study aims to enhance the performance of maintenance teams by proposing a mathematical programming model for team formation, focusing on the maximization of knowledge sharing. In this model, key factors influencing knowledge sharing — including knowledge absorptive capacity, knowledge-sharing capability, willingness to share knowledge, and motivation to acquire knowledge — along with the required expertise for maintenance activities, are identified and incorporated into the optimization process. The data used to develop the model were collected through performance evaluation questionnaires and expert interviews. The implementation of the proposed model in an organization with extensive physical assets demonstrates that optimizing team formation leads to significant improvements in maintenance key performance indicators. The findings of this study indicate that knowledge sharing not only improves individual and team skills but also plays a key role in improving the performance of maintenance teams. The proposed model can be used as an efficient decision-making tool for maintenance.IntroductionIn today's competitive environment, organizations are constantly exploring innovative approaches to enhance their productivity and performance. One of the key areas that plays a critical role in achieving this goal is industrial maintenance. Effective performance in this domain can have a direct impact on reducing costs, extending the lifespan of equipment, and improving organizational reliability (Okirie, 2024). Consequently, the use of innovative approaches for more efficient management of maintenance activities has gained significant importance.One of the main challenges in maintenance management is the formation of optimal work teams that can efficiently carry out their assigned tasks. Teams composed of knowledgeable and skilled individuals not only enhance the quality of task execution but can also foster problem-solving and innovation through collaborative knowledge sharing (Alharbi & Aloud, 2024). This is particularly crucial in asset-intensive industries.The process of team formation can vary depending on factors such as individual characteristics, task types, and organizational context. One of the key elements in effectively guiding team formation is the willingness and ability of members to share knowledge (Stavrou et al., 2023). Knowledge sharing among team members facilitates the transfer of experiences and critical information, which improves skills and helps prevent the repetition of past mistakes. This process transforms tacit knowledge into collective assets and supports informed decision-making. Institutionalizing a culture of knowledge sharing not only enhances collaboration and innovation but also increases organizational resilience to environmental changes, thereby laying the foundation for sustainability and competitive advantage (Hamill, 2025).However, many organizations face challenges in identifying and leveraging their organizational knowledge capacities (Zamiri & Esmaeili, 2024). This study presents a model for forming optimal maintenance work teams with the goal of maximizing knowledge sharing. While maintenance tasks are often performed based solely on job roles, with less emphasis on team formation according to the specific issue at hand, this research integrates knowledge management concepts with optimization principles to offer an operational and quantitative model for improving the performance of technical maintenance teams.In this regard, the factors affecting knowledge sharing and the required expertise for performing maintenance activities have been identified using data collected from performance evaluation questionnaires and expert interviews. The identification process utilizes the Motivation-Opportunity-Ability (MOA) framework as its theoretical foundation, enabling a deeper analysis of the factors influencing knowledge sharing within work teams. The questionnaires designed based on this model assess team members' motivation to share knowledge, their technical and communication capabilities, and the existing opportunities for knowledge sharing. This information helps managers identify team strengths and weaknesses and adopt a data-driven approach to decision-making.The proposed model in this study employs mathematical programming. The implementation of this model in an organization has shown that forming optimal work teams leads to significant improvements in key performance indicators, such as average repair time and equipment availability (Rahman et al., 2022). The findings of this research highlight that knowledge sharing can serve as a fundamental driver for enhancing the performance of maintenance teams. This study represents an important step in redefining the role of team formation in the efficiency of maintenance processes.MethodsThis study adopts a quantitative approach to optimize the formation of maintenance teams. In the first phase, a nonlinear integer programming model is developed to maximize knowledge sharing among team members while considering the assumptions and constraints relevant to equipment maintenance. To evaluate the model's performance, it is applied within an asset-intensive industry. Required data were collected via expert interviews, as well as performance evaluation questionnaires. Following data collection and parameter calculation, an industry-specific mathematical model was formulated. The model was solved using the branch-and-bound method implemented in MATLAB software. As a result, the most suitable maintenance teams for carrying out designated activities were proposed. Finally, the performance of the formed teams was assessed using key indicators such as mean time to repair (MTTR) and equipment availability. This evaluation was conducted over six-month intervals using a Computerized Maintenance Management System (CMMS).Discussion and resultsTo illustrate the proposed model, a case study was conducted for forming maintenance teams for floating equipment. The selected equipment and the corresponding maintenance activities were recommended by industry experts. Based on these recommendations, key indicators — including technical expertise of maintenance personnel and Motivation-Opportunity-Ability parameters — were evaluated using a 360-degree assessment method. This was carried out through questionnaires employing a five-point Likert scale and distributed among individuals (self-assessment), managers (top-down assessment), and peers. Additionally, the required expertise levels for each maintenance task, as well as the opportunities for knowledge sharing and absorption, were identified through expert surveys. The collected data on individuals’ expertise were then normalized based on the maximum level of expertise needed for the maintenance tasks.Finally, to assess the impact of the implemented policies on equipment performance, two key indicators were analyzed: Mean Time Between Failures (MTBF), representing equipment reliability, and Mean Time to Repair (MTTR), reflecting the skill level of maintenance personnel. These indicators were monitored over a six-month period prior to implementation and three subsequent six-month intervals post-implementation. Simultaneous improvement in both indicators suggests that maintenance activities were conducted with greater speed and higher quality.ConclusionThe findings of this study underscore the critical role of knowledge sharing in enhancing the performance of maintenance teams. The proposed optimization model demonstrated that forming optimal work teams can effectively reduce repair time, manage maintenance costs, and improve key performance indicators. Based on these results, several practical measures can be implemented to enhance maintenance performance through effective knowledge sharing and optimal team formation. First, organizations are encouraged to adopt a performance evaluation system grounded in the Motivation–Opportunity–Ability (MOA) framework to better assess and develop individual and team capabilities. In parallel, targeted incentive schemes should be designed to actively promote knowledge sharing among team members. Additionally, establishing structured and interactive platforms within maintenance processes can facilitate communication and collaboration. Strategic investment in training programs aimed at improving both technical and team-based skills is also essential. Finally, ongoing monitoring of team performance and the use of data-driven decision-making can ensure continuous improvement and alignment with organizational goals. Collectively, these actions provide a comprehensive approach to enhancing the efficiency and effectiveness of maintenance operations
