Purpose: Total productive maintenance (TPM) has been widely recognized as a strategic weapon for improving manufacturing performance. Evaluate efficiency of TPM implementation is considered as a key element in order to motivate staff and to give decision-makers more confidence.Design/methodology/approach: This study consists in developing a new method of evaluating TPM implementation, relying on analytical models and considering two preventive maintenance strategies: periodic and age-dependent.Findings: The preventive maintenance period and TPM period defined as decision variables are obtained simultaneously by maximizing the expected profit under TPM implementation. A numerical example is presented and a sensitivity study is developed to validate the proposed models.Originality/value: The aim of this research is to quantify, through analytic development, the impact of TPM implementation in a company by calculating and comparing the profit made with and without TPM.

Purpose: The purpose of this paper is to optimize the maintenance strategies for wind turbine (WT) gearboxes to minimize costs associated with PM actions, cooling, production loss and gearbox replacement. Two approaches, periodic imperfect maintenance and a novel design incorporating alternating gearboxes are compared to identify the most cost-effective solution.Design/methodology/approach: This study employs mathematical modeling to analyze the design, operation and maintenance of WT gearboxes. Two maintenance strategies are investigated, involving periodic imperfect maintenance actions and the incorporation of two similar gearboxes operating alternately. The models determine optimal preventive maintenance (PM) and switching periods to minimize total expected costs over the operating time span.Findings: The research findings reveal, for the considered case of a moroccan wind farm, that the use of two similar gearboxes operating alternately is more cost-effective than relying on a single gearbox. The mathematical models developed enable the determination and comparison of optimal strategies for various WT gearbox scenarios and associated maintenance costs.Research limitations/implications: Limitations may arise from simplifications in the mathematical models and assumptions about degradation, temperature monitoring and maintenance effectiveness. Future research could refine the models and incorporate additional factors for a more comprehensive analysis.Practical implications: Practically, the study provides insights into optimizing WT gearbox maintenance strategies, considering the trade-offs between PM actions, cooling, production loss and gearbox replacement costs. The findings can inform decisions on maintenance planning and design modifications to enhance cost efficiency.Social implications: While the primary focus is on cost optimization, the study indirectly contributes to the broader societal goal of sustainable energy production. Efficient maintenance strategies for WTs help ensure reliable and cost-effective renewable energy, potentially benefiting communities relying on wind power.Originality/value: This paper introduces two distinct strategies for WT gearbox maintenance, extending beyond traditional periodic maintenance. The incorporation of alternating gearboxes presents a novel design approach. The developed mathematical models offer a valuable tool for determining and comparing optimal strategies tailored to specific WT scenarios and associated maintenance costs.

Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world.

As the Industry 5.0 transition unfolds, engineering education must evolve to integrate Lean manufacturing with advanced digital tools and sustainable, human-centred practices. This study presents the design and implementation of a Lean Learning Factory (LLF) that addresses this challenge by combining traditional Lean methods with technologies such as simulation, robotics, and virtual reality in a modular educational environment. At the University Centre Pitești, six hands-on projects were implemented to guide students through key concepts, including production system layout, digital assistance, sustainability, and human–robot collaboration. Through experiential learning, students engage in iterative design, data analysis, and practical validation using real equipment and software platforms. The results indicate that the LLF effectively supports the development of technical, digital, transversal, and human-centred competencies aligned with EUR-ACE^® standards. Students acquire skills in process optimisation, ergonomics, and sustainable production, while also reflecting on the ethical and social implications of automation. The study concludes that the LLF model provides a scalable and adaptable framework for engineering education. It fosters competence-based learning and prepares students for the demands of Industry 5.0. This paper contributes a replicable educational approach that blends Lean efficiency, digital transformation, and human-centred values into a cohesive learning ecosystem.

Remote teaching has been becoming increasingly important in higher education since the COVID-19 pandemic. Additionally, fast and precise modeling of the main operating values of industrial systems is a key point for production and efficiency calculation. After a review of the main modeling equations of an induction motor, this paper proposes to include them in spreadsheet software in the form of practical work so that readers will improve their level in multiple competency fields, such as data processing, the use of spreadsheet software, deepening understanding of induction motors, classical induction motor testing, and modeling and the use of the models to solve classical electrical engineering issues. Furthermore, the fact that this work was designed to be conducted autonomously and asynchronously gives readers the feeling of freedom, which is very useful for developing their desire to progress by their own means and to do their utmost to succeed in their task. Finally, the provided Excel file, which includes an initial spreadsheet with gaps, allows readers to familiarize themselves with the concepts, experiment with them, and extend the base file to their own topics of interest. This work can serve as a proof of the readers’ ability to succeed in industrial modeling tasks and aid in their employment prospects.