Asset Integrity Management (AIM) is vital for ensuring the safety, reliability, and sustainability of high-risk industrial assets in sectors such as advanced manufacturing, oil and gas, energy, and petrochemicals. As industrial systems grow increasingly complex, interconnected, and digitally enabled, traditional maintenance approaches often struggle to manage dynamic risks, performance variability, and long-term asset health. This study presents a comprehensive review of AIM methodologies and proposes a proactive, integrated framework that unifies Risk-Based Inspection (RBI), Reliability-Centered Maintenance (RCM), and Total Productive Maintenance (TPM) within a single strategic model. The framework aligns strategic objectives, operational processes, and key performance indicators (KPIs), emphasizing risk mitigation, operational efficiency, workforce capability, and sustainability, while addressing implementation challenges such as leadership engagement, skill gaps, data governance, and digital integration. Conceptually validated across high-reliability industrial contexts, the model enhances Reliability, Availability, Maintainability, and Safety (RAMS) and, by integrating Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twins, advances Maintenance 4.0—enabling predictive, intelligent, and sustain-able maintenance ecosystems that minimize unplanned downtime, reduce lifecycle costs, and strengthen organizational resilience and long-term value creation.

This study presented a comprehensive review of Overall Equipment Effectiveness as a theoretical and practical framework for production line improvement, with particular emphasis on the bakery patisserie confectionery industry. Rooted in the principles of Total Productive Maintenance, OEE was examined as an integrative performance measure that combined availability, performance, and quality to identify hidden losses and guide improvement priorities. The review showed that, in multi–stock-keeping unit production environments characterized by frequent changeovers, setup and adjustment losses represented a major constraint on equipment availability and effective capacity utilization. Drawing on empirical and conceptual studies, the research demonstrated that OEE was most effective when it was applied as a diagnostic and improvement-oriented tool rather than as a standalone numerical indicator. The analysis further highlighted the interdependence of the three OEE dimensions, indicating that availability losses often propagated into performance inefficiencies and quality defects, particularly in time-sensitive food manufacturing operations. In addition, the study identified limitations related to inconsistent OEE calculation and interpretation, which reduced its comparability and weakened its role as a driver of continuous improvement. Overall, the findings confirmed that OEE provided a robust and holistic framework for uncovering concealed capacity, reducing reliance on overtime.

A steam turbine is an energy conversion machine whose converted energy is used by other machines to generate power. This study aims to analyze the reliability of turbine engines using the Total Productive Maintenance (TPM) method and the 4 Disciplines of Execution (4DX). In this study, TPM was used to identify various major causes of production time loss, such as breakdowns, setup time, and loading time. Meanwhile, 4DX focuses on achieving key objectives through consistent and measurable actions, namely zero breakdowns and zero stagnation. The results of this study indicate that the Availability value is 98.69%, the Performance value is 98.14%, the Quality value is 99.74%, and the OEE value is 96.61%, which means that the turbine is in a reliable condition. In addition, 4DX plays a role in accelerating the achievement of improvement targets by providing a more focused approach, monitoring daily activities, and enhancing accountability. The research results indicate that the combination of these two methods is effective in improving turbine reliability, extending equipment lifespan, and fostering employee discipline. Keywords: Steam Turbine, Reliability, TPM, OEE, 4DX

This study investigates the application of Total Productive Maintenance (TPM) to improve the performance of the Mitsui Seiki Inverter screw compressor at PT SEI. The research focuses on evaluating the compressor's Overall Equipment Effectiveness (OEE), which combines availability, performance, and quality metrics to assess the effectiveness of the equipment. Data collection involved primary data from observations and interviews with maintenance staff, along with secondary data from production records. The analysis employed TPM techniques, specifically focusing on reducing downtime and improving machine performance by addressing key issues such as nozzle blockages, cooling system failures, and fluctuating speed losses. The results revealed that the average OEE for 2025 was below the ideal standard of 85%, mainly due to performance losses and recurring breakdowns. The study highlights the importance of consistent maintenance, operational parameter control, and enhanced quality control practices. Recommendations include more frequent nozzle cleaning, regular checks of the cooling system, and implementing regular OEE monitoring as a Key Performance Indicator (KPI). The findings provide valuable insights into optimizing the maintenance of industrial compressors and improving overall production efficiency.

The technology used in a company will continue to advance with time. The technology frequently used involves reliable production machines for smooth production processes. Therefore, machines that play a crucial role in the production process must be well-maintained. Inaccuracies in quality checks on goods to be sold in the market will result in complaints or losses for the company from the released products. Such losses include insufficient attention to checking the quality of products and machines, leading to imperfections in surface evenness and hindering the process. Total Productive Maintenance (TPM) is a maintenance approach that optimizes machine efficiency, reduces breakdowns, and includes autonomous maintenance performed by machine operators. Based on data from weeks 1 to 4 in March 2024, it can be concluded that calculations using the TPM method resulted in an OEE value of 94%, indicating that the productivity and efficiency level of machines or equipment at PT. G has surpassed world-class standards and is near-perfect. Factors that can be improved to achieve an OEE value of 100% are Availability and Rate of Quality. Keywords: Overall Efectiveness Equipment, Production, Total Productive Maintenance

The application of Total Productive Maintenance (TPM) using the Overall Equipment Effectiveness (OEE) method at PT. DEF, The issue of maintenance has become crucial as it affects not only the reliability of the machines but also the speed and quality of production. This study will explore how the implementation of TPM using the OEE approach can improve machine effectiveness in a production environment, with a focus on timely and optimal maintenance strategies.The research aimed to assess the efficiency and effectiveness of machine operations, particularly injection molding machines, within the company's production line. Through the collection and processing of operational data, it was found that the OEE value for the machines in question ranged from 94% to 95%, surpassing the company's set standard of 93%. The high OEE score reflects effective machine utilization, availability, performance, and quality rates. The research highlights that while the current system meets international standards for machine maintenance, continuous improvements can further optimize machine productivity and reduce downtime. Keywords: Injection, Molding, OEE, TPM

Purpose This study aims to present a novel and flexible systematic method of Lean Maintenance designed to develop solutions and foster scientifically sustainable maintenance excellence. The method integrates the five Lean principles with short plan-do-check-act cycles promoted by the Toyota Kata approach to establish sequential target conditions. This systematic method highlights the active involvement of maintenance process executors in creating sustainable improvements. Design/methodology/approach The proposed systematic method was applied through action research in a thermoplastics manufacturing company. The research team conducted the daily routine of Improvement Kata and Coaching Kata, and employees took part in the initiative. The results were assessed from the perspective of analyzing the improvement process with the team and the benefits for performance indicators. Findings From the point of view of the company, the systematic method promoted problem-solving by strengthening the learning capacity developed through formalizing the maintenance sector’s learning. There was an increase in the machines’ Overall Equipment Effectiveness through improvements such as implementing Total Productive Maintenance, strengthening the 5S program and reducing the setup process. In addition, there was a change in how those involved in the process think by conducting small improvement cycles, which began to direct efforts and promote reflection on the results obtained with each action taken. Originality/value While many companies face challenges in implementing Lean Maintenance due to the absence of a systematic and participatory approach, this study makes an original contribution by offering a replicable pathway that bridges theory and practice. The proposed method stands out for its flexibility, scalability and emphasis on the active involvement of shop-floor personnel, features that distinguish it from conventional top-down implementations. Furthermore, this work addresses a gap in the literature by formalizing a structured improvement routine in maintenance environments aligned with the Lean philosophy.

On the Crankcase K-58 machining line, several performance gaps were identified, including inconsistent cycle time, frequent unplanned downtime, and tool-change irregularities that caused the output to fall below the company targets. This study aims to enhance the production effectiveness of the K-58 crankcase machining line by applying an integrated Total Productive Maintenance (TPM) and Failure Mode and Effects Analysis (FMEA) approach. The initial performance evaluation, using Overall Equipment Effectiveness (OEE) in 2024, showed an average score of 76.2%, indicating significant losses in availability and performance rates. Quantitative analysis was conducted through OEE and Six Big Losses assessments, while FMEA was used to prioritize failure points based on Risk Priority Number (RPN). Improvement actions implemented included autonomous maintenance reinforcement, scheduled preventive maintenance, operator skill development, coolant-condition control, and quick-change adjustments. After implementation, OEE of the machining line increased to 85.5%, meeting the its target. Reduced speed losses improved by more than 45%, while setup and adjustment losses decreased by over 50%. These results confirm that the integrated TPM–FMEA approach is effective for enhancing machining-line performance and reliability.

It is a wonderful place where organizations across various fields and industries ought to work hard to venture off the trail of continuous definition of their operations, and this is made possible through the inclusion of the cross-functional team and the use of the Six Sigma practice. The review article will look at the application of the cross-functional team to the Six Sigma on the continuous improvement system. Our research on basing was guided by the researches in the three industries: manufacturing, services, and technology; structure; integration of knowledge; issues of dominance in functions; and new paradigms of Quality 4.0 and Machine Learning. The cross-functional teams assist in better problem solving, better interaction and communication within and between the departments, and in making sure that the Six Sigma projects do lead to a tangible quality improvement and efficiency. These incremental changes, which had been implemented in these fields such as financial services and integration with Total Productive Maintenance to SMEs (Small and Medium Enterprises), were found out during this review. Summing up, this review has shown that the trans-functional and information-driven implications of quality in the framework of such a comprehensive approach is one of the directions to which the organizations will be forced to evolve to be topical and sustainable in the future.

This study aims to improve the effectiveness of ampoule filling machines in a pharmaceutical manufacturing company through the implementation of Total Productive Maintenance (TPM) supported by Overall Equipment Effectiveness (OEE) evaluation. The study was motivated by a decline in machine performance during the period from January to August 2024, characterized by low production output, high downtime, and high defect rates. A quantitative research approach was employed using primary and secondary data collected from 30 production batches through field observations, interviews, and documentation review. Machine effectiveness was evaluated using OEE, which consists of availability, performance, and quality components, while efficiency losses were identified using the Six Big Lossesframework. Root causes were analyzed using a Fishbone diagram, and improvement actions were formulated based on relevant TPM pillars. The results show that the initial average OEE value was 56%, significantly below the world-class benchmark of 85%, with breakdown losses and quality defect losses identified as the dominant contributors to inefficiency. After implementing TPM-based improvements—specifically the replacement and recalibration of malfunctioning swing conveyor sensors and the redesign of the ampoule outfeed system—the average OEE value increased to 71.9%. Improvements were also observed in the OEE components, with availability increasing to 86.5%, performance to 92.0%, and quality to 90.5%. These results indicate a substantial reduction in downtime and defect rates. The study confirms that the integration of TPM and OEE is effective in enhancing machine effectiveness and production efficiency in sterile pharmaceutical manufacturing.

This study develops and empirically validates an integrated conceptual model that investigates the causal relationships among Total Productive Maintenance (TPM), Jidoka, and economic sustainability (ECSU) within the manufacturing industry. Based on Resource and Capability Theory as well as Systems Theory, the model posits that TPM directly influences both jidoka and ECSU, while jidoka acts as a mediator in the relationship between TPM and ECSU. Utilizing structural equation modeling (SEM-PLS) and data collected from 357 surveys of the maquiladora industry in Ciudad Juárez, Mexico, the analysis confirms that TPM positively affects jidoka (β=,632, p<0,001) and ECSU (β=0,340, p<0,001). Furthermore, jidoka contributes significantly to ECSU (β=0,358, p<0,001) and mediates the effect of TPM on ECSU (β=0,226), resulting in an increased total effect of β=0,566. The researchers conducted graphical analyses that demonstrate nonlinear patterns in relationships. These findings underscore the synergies between TPM and jidoka, which work together to maximize sustainable economic benefits in lean manufacturing environments.

This study explores the enhancement of soybean grinding machine reliability in tofu production by integrating Overall Equipment Effectiveness (OEE) and Failure Mode and Effects Analysis (FMEA). Conducted at CV. Restu Mulia Jaya between November 2024 and April 2025, the research utilized quantitative analysis of machine operational data. The findings revealed consistently low OEE values (39.04%–46.03%), attributed primarily to frequent downtime and variable performance rates. Equipment failures and speed losses were identified as the main sources of inefficiency. FMEA highlighted critical failure modes, notably clogged or corroded spirals and damaged filters. Recommended improvements include the adoption of Total Productive Maintenance (TPM), operator training, real-time monitoring, and the use of durable materials for key components. This integrated OEE-FMEA approach effectively prioritizes corrective actions and supports sustainable reliability improvement for soybean grinding machines in small-scale tofu production. Keywords: machine reliability, OEE, FMEA, tofu production, maintenance

Purpose This paper introduces MedMaintBot, an AI chatbot designed to support biomedical technicians and non-expert users like nurses. The study explores the impact of integrating such an AI chatbot into Total Productive Maintenance (TPM) practices in healthcare, aligned with Industry 5.0 (I5.0) principles. Design/methodology/approach This study adopts a multi-phase methodology, starting with a literature review on technology integration in TPM within healthcare settings. It presents the chatbot development pipeline and conducts a large-scale validation study across 250 queries covering five medical devices (MDs) to demonstrate the chatbot's real-time, context-aware guidance capabilities. Performance analysis further evaluates MedMaintBot's potential to optimize TPM practices and support sustainability goals in healthcare maintenance. Findings The study reveals that MedMaintBot enhances TPM within healthcare by delivering accurate, context-aware guidance (Accuracy = 0.713, Relevance = 0.810), supporting nurse autonomy in routine maintenance and reducing technician dependency. While clarity and completeness were slightly below ideal for complex tasks, over 80% of autonomy-related queries were validated, showing strong support for first-level interventions. Combined with dynamic Large Language Model (LLM) switching between GPT-4 and MedLLaMA2, MedMaintBot strikes a balance between performance, cost and privacy, positioning it as a scalable and sustainable tool for healthcare maintenance. Research limitations/implications This research provides valuable insights for practitioners and researchers on enhancing autonomous maintenance (AM) through AI–chatbot integration, offering a scalable framework for integrating AI into TPM practices. It also encourages further studies to address gaps in procedural completeness and contextual continuity and assess scalability across diverse maintenance environments. Practical implications By providing real-time, context-aware guidance, the chatbot helps reduce user-induced errors, allowing non-expert users, such as nurses, to perform maintenance tasks. This not only reduces the burden on specialized technicians but also ensures better equipment availability, contributing to more streamlined healthcare operations and improved patient care. Social implications MedMaintBot promotes a more inclusive and resilient healthcare environment by empowering non-expert users with AI-driven support. Its adaptability aligns with the human-centric principles of Industry 5.0, fostering collaboration between technology and healthcare personnel. Originality/value This research is among the first to examine the integration of innovative AI chatbot with TPM practices within the healthcare sector, particularly in the context of I5.0. It demonstrates how such a system can significantly enhance operational efficiency, empower non-expert users and support sustainability in healthcare, offering a roadmap extending AI-assisted maintenance to broader industrial and resource-constrained environments.

This study explores the preventive maintenance strategies and the challenges in implementing Overall Equipment Effectiveness (OEE) in managing the logistics truck fleet at PT KIM. The primary objective of this study is to gain an in-depth understanding of how preventive maintenance strategies are carried out within the company and to what extent the application of the OEE concept supports operational efficiency. Utilizing a qualitative approach and case study method, the research reveals that although PT KIM has implemented various preventive maintenance procedures, their effectiveness is still hindered by factors such as limited driver involvement, the absence of a reward and punishment system, and the lack of integration in reporting systems across departments. Moreover, the implementation of OEE has not been optimal due to low data integration and the use of manual reporting systems, which lead to delays in information flow and ineffective decision-making. An analysis based on the Total Productive Maintenance (TPM) theory highlights that weaknesses in administrative aspects (TPM in office) serve as a major obstacle to improving fleet performance effectiveness. The findings underscore the importance of a holistic and digitally integrated maintenance management approach as a foundation for enhancing the company’s logistics productivity and competitiveness. Therefore, it is recommended to strengthen technology-based reporting systems, implement cross-functional training, and cultivate an organizational culture that supports collaboration within the TPM framework.

The FMCG (fast-moving consumer goods) business is challenged with increasing requirements for production efficiency, equipment reliability, product safety, and sustainability in shop floors characterized by fast-moving and changing production lines. In the past, Lean Manufacturing and Total Productive Maintenance (TPM) have been used to drive process improvement, but these approaches are designed without real-time data (key to advances in Industry 4.0). When we combine AI/ML with Lean TPM, it has transformative possibilities: it can allow us to do predictive maintenance, advanced anomaly detection, and deploy cyber-physical systems. This study distills the merits of AI augmentation and resultant frameworks and its implementation challenges for Lean TPM in FMCG processes. Based on cross-sector case studies and extensive literature, the results show that AI application assists in equipment efficiency, fault diagnosis, and dynamic scheduling, which results in an increase in OEE as well as sustainability performance. However, obstacles including data interoperability difficulties, skills strain, SME uptake challenges, and lack of standardized benchmarking continue to limit wider adoption. We present a conceptual integration model and pragmatist application approach to support stakeholders and academics as they negotiate this new terrain. The latter is set to take maintenance paradigms further from “reactive” into “proactive,” and eventually “condition-based” and “predictive” with a focus on the data-driven framework that is necessary to build up industry resilience and competitiveness in the digital era.

Purpose This research paper aims to examine the impact of lean six sigma (LSS) practices on organizational performance, emphasizing the mediating role of operational performance in Ethiopia’s public automotive manufacturing sector. Design/methodology/approach A quantitative research design was used by using a structured five-point Likert scale questionnaire distributed to employees in four public automotive manufacturing factories. Data analysis included calculating means, standard deviations, correlations and regression techniques, along with mediation analysis to investigate the relationships among LSS practices as independent variables, operational performance as a mediating variable and organizational performance as a dependent variable. Four hypotheses were developed and tested using SPSS for statistical analysis. Findings The research found a significant positive relationship between LSS practices and organizational performance, with operational performance acting as a crucial mediator. Structured improvement procedures and statistical process control had the strongest effects on both operational performance and organizational performance, while total productive maintenance showed an insignificant impact on operational performance. Inventory levels also did not significantly affect organizational performance. The results indicate that LSS practices are interdependent and should be implemented holistically, as they support one another. Operational performance partially mediates the influence of LSS practices on organizational performance. Research limitations/implications The study is limited to Ethiopia’s public automotive sector, which may affect the generalizability of the findings. This research enhances the theoretical understanding of LSS practices by elucidating the mediating role of operational performance in influencing organizational outcomes. Practical implications Automotive managers should prioritize LSS practices to improve operational metrics and enhance organizational performance. Originality/value This study provides empirical evidence on the relationship between LSS practices and organizational performance in Ethiopian manufacturing, underscoring operational performance vital mediating role.

Purpose This study examines the effect of lean manufacturing practices (LMPs), namely total productive maintenance (TPM) and continuous improvement (CI), on the environmental, social and economic sustainability performance of manufacturing firms. Design/methodology/approach A cross-sectional method was employed in the current study. A total of 236 responses were collected to validate the developed conceptual framework. The proposed relationships were confirmed through the application of partial least squares structural equation modelling (PLS-SEM) in SmartPLS 4.0.9.5 software. Findings The study illustrates that LMPs, notably TPM and CI, positively affect economic, environmental and social sustainability performance (SOCSP). The findings emphasize the necessity of implementing LMPs as crucial resources for improving the performance of manufacturing firms. Originality/value This study is one of the few that examines the effect of LMPs on the economic, environmental and SOCSP of manufacturing firms in Tanzania, as the literature is largely based in developed nations. The results demonstrate that proper use of LMPs improves firms’ sustainability performance. The findings offer managers and policymakers valuable insights into effectively using lean practices to enhance the sustainability performance of manufacturing firms in developing countries.

Purpose This study investigates the barriers to implementing Total Productive Maintenance (TPM) in the context of Industry 4.0 (TPM 4.0). It employs a synthesis of existing literature and theoretical frameworks to delineate key barriers. Design/methodology/approach The research utilizes Fuzzy Interpretive Structural Modeling (FISM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) methodologies to identify significant barriers hindering TPM 4.0 implementation. Findings The results indicate that behavioral barriers, stemming from resistance to change and lack of adaptation to new technologies, pose substantial barriers to TPM 4.0 implementation. Organizational barriers, including inadequate senior management commitment and poor communication, further impede the effective execution of TPM initiatives. Technological barriers, such as the absence of computerized maintenance management systems and insufficient real-time monitoring data, present additional barriers to integrating TPM with Industry 4.0 technologies. Social implications Effective implementation of TPM 4.0 can lead to improved operational efficiency, reduced maintenance costs and better utilization of resources, which can have positive social implications in terms of sustainability and economic growth. Originality/value The novelty of this research lies in the simultaneous application of two fuzzy-based multi-criteria decision-making tools (FISM and FDEMATEL), which has not been previously employed for TPM 4.0 in the context of Industry 4.0. This dual-framework enables not only classification but also causal inference regarding barrier dynamics, which is a methodological contribution to the field.

Purpose Nowadays, many industries, including the electrical and electronic equipment (EEE) industry, are facing significant environmental challenges. The remanufacturing process is an effective strategy for conserving resources and reusing them in subsequent production cycles, making it a key factor in reducing environmental impacts. Therefore, identifying lean, agile, resilience and green (LARG) activities in remanufacturing is essential for the growth of the remanufacturing industries, while this issue has been neglected in previous research. Design/methodology/approach The aim of this paper is to identify and evaluate LARG activities in the remanufacturing process of the EEE industries in the US and Canada. Using fuzzy analytic hierarchy process (FAHP), fuzzy stepwise weighted ratio analysis (FSWARA) and the newly proposed method of importance-performance-productivity analysis (IPPA), 24 LARG activities were evaluated. Finally, the results were validated using data mining. Finally, a benchmarking index based on IPPA was introduced. Findings According to the proposed IPPA method, eight octants were defined based on the importance, performance and productivity scores. It indicates that only the demand management activity is placed in the first octant and six activities (multi-skilled workers, total productive maintenance (TPM), customer relationship management, sustainable cost management, sustainable total quality management (TQM) and eco-responsive decision-making) with poor importance, performance and productivity are placed in the eighth octant. Originality/value The manuscript presents a novel integration of fuzzy MCDM techniques (FAHP and FSWARA) with a newly proposed importance-performance-productivity analysis (IPPA) framework, specifically tailored for evaluating LARG activities in remanufacturing. Unlike prior studies, it uniquely combines qualitative prioritization with quantitative benchmarking and validation through data mining, offering a comprehensive and data-driven approach to improve sustainability practices in the EEE remanufacturing sector.

The urgent global challenge of environmental sustainability has intensified interest in integrating Lean Management practices with environmental objectives, particularly within the automotive industry, a sector known for both innovation and high environmental impact. This study investigates the systemic relationships between 16 lean practices and three environmental performance metrics: energy consumption, CO2 emissions, and waste generation. Using the Fuzzy Decision-Making Trial And Evaluation Laboratory (DEMATEL) methodology, data were collected from seven lean experts in the Italian automotive industry to model the cause–effect dynamics among the selected practices. The analysis revealed that certain practices, such as Total Productive Maintenance (TPM), just-in-time (JIT), and one-piece-flow, consistently act as influential drivers across all environmental objectives. Conversely, practices like Statistical Process Control (SPC) and Total Quality Management (TQM) were identified as highly dependent, delivering full benefits only when preceded by foundational practices. The results suggest a strategic three-step implementation roadmap tailored to each environmental goal, providing decision-makers with actionable guidance for sustainable transformation. This study contributes to the literature by offering a structured perspective on lean and environmental sustainability in the context of the automotive sector in Italy. The research is supported by a data-driven method to prioritize practices based on their systemic influence and contextual effectiveness.