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Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

The Key Performance Indicators (KPIs) of manufacturing, especially for our case, Overall Equipment Effectiveness (OEE), scrap rate, and IPPM (Issues per Million), are important metrics that measure the efficiency and quality of manufacturing processes. Low OEE, high scrap rate, and high IPPM are all indicative of inefficient manufacturing processes that can lead to production delays, increased costs, and reduced customer satisfaction. Therefore, it is crucial for manufacturing companies to improve these KPIs. In automotive industry we have adopted a strategy to improve these KPIs in all areas by using industry 4.0 and Lean six sigma. In this paper we will present a case of study. [ABSTRACT FROM AUTHOR]

This study successfully employed lean manufacturing techniques in conjunction with the DMAIC methodology to enhance the efficiency of a sweet manufacturing production line. By using key performance indicators (KPIs) and value stream mapping, we were able to quantify the improvements achieved. A notable 36% increase in Overall Equipment Effectiveness (OEE) was realized, driven by a 19.4% improvement in performance and a 17.58% increase in quality. While availability experienced a minor decline, the overall gains in performance and quality outweighed this effect. Value stream mapping revealed a significant reduction in rework from 388 kg to 273 kg per production shift, representing a substantial 30% decrease. This reduction directly contributed to a 18.8% increase in production yield, from 612 kg to 727 kg. These improvements were facilitated by the implementation of a Poka-Yoke device and targeted bottleneck analysis. The achievement of the present work encourages to extend the implementation of lean and DMAIC combined methodology to other production lines of the company. [ABSTRACT FROM AUTHOR]

In many industrial settings, there are several problems that can arise during the production process. These include machine breakdowns, quality issues, and unexpected delays, which can impact productivity, reduce overall efficiency, and result in lower quality output. In addition, without an effective monitoring system, it can be difficult to identify the root causes of these problems and take appropriate corrective actions.To address these challenges, the implementation of a smart andon system can be highly beneficial. This system enables real-time monitoring of the production process, allowing operators and management to quickly identify and respond to any issues as they arise. By providing instant notifications and alerts, the smart andon system can help reduce downtime, increase productivity, and improve product quality. It also enables more accurate and comprehensive data collection, facilitating better analysis and decision-making by management. Overall, the smart andon system can play a critical role in improving operational efficiency, reducing costs, and enhancing overall competitiveness in today's highly competitive industrial landscape.the implementation of a smart andon system has been shown to improve production efficiency, reduce downtime, and increase overall equipment effectiveness (OEE). The system allows for real-time monitoring of the production process, early detection of problems, and quick resolution of issues through timely alerts and notifications. This can result in significant cost savings for the industry, improved product quality, and increased customer satisfaction.

In the last few decades, the industrial sector has experienced rapid growth, driven by increasing demand and intense competition among manufacturers, especially in the electronics sector. This competition focuses on providing superior products with competitive prices, maintained quality, and optimal delivery times. Optimizing manufacturing processes and effectively utilizing company resources have become key to competitiveness in the manufacturing industry. To ensure comprehensive optimization and smooth manufacturing workflows, it is crucial to engage in systematic evaluation and analytical processes. One of the key performance metrics in assessing manufacturing process efficiency is Overall Equipment Efficiency (OEE), which is used to uncover improvement opportunities and inefficient areas. Accurate OEE measurement requires a data mining systems with automated quantitative data collection methods and real-time calculations. These systems visualize process losses in six (pareto) groups, aiding users in analyzing processes and determining process improvements. The implementation of OEE and alert systems for management can bring an 11.82% increase in overall production efficiency. This achievement can serve as a model for other companies embarking on the initial stages of digital transformation processes.

CT scan is a medical tool used by hospitals to provide radiology patient services. The working principle of a CT scan tool is to utilize ionizing radiation in the form of X-rays and a combination of a computer system. The sophistication of existing equipment does not guarantee that it can be free from disasters such as sudden breakdowns. According to global medical equipment failure statistics, 80% of all failures are caused by preventable factors. Implementation of the Total Productive Maintenance (TPM) model, which is tailored to top management in service provider organizations such as hospitals, is considered a powerful tool for maintenance systems and can minimize the occurrence of failures. The effectiveness of TPM on CT-scan equipment can be measured using the Overall Equipment Effectiveness (OEE) method, this method is able to describe equipment performance in theory and is an accurate calculation of how effectively the machine is used. This type of research is Research and Development (R&D) with an experiment quasi-design and a pretest post-test with a control group design. The researcher used the research stages of the Borg & Gall development model which consists of 10 steps, then the researcher modified it into 6 steps. The research results explain that the TPM model that has been prepared uses the concept of autonomous maintenance with modification and replication of William N. Dunn's policy analysis; The OEE value of the experimental group after the model intervention (36%) was lower than before the model intervention (53%), this was because there were other factors that influenced it. [ABSTRACT FROM AUTHOR]

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring. [ABSTRACT FROM AUTHOR]

At the end of April 2024, national car sales for the domestic market were recorded to have decreased by 34.91% compared to the previous month. This negative trend, of course, disrupts company profits, including PT XYZ as a supplier of automotive components in Indonesia. This decline triggered the company to carry out cost reduction activities in the production area. However, in reality there is still a lot of waste during production, one of which is in the 650T injection molding process. The high cycle time of the injection process resulted in the Overall Equipment Effectiveness (OEE) target not being achieved. The lean manufacturing approach and DMAIC cycle as a research method aims to identify non-value added activities as well as make improvements by reducing the injection process cycle time from 86 to 57 seconds. These improvements made the OEE target for the 650T injection machine achieved because it increased from 67.9% to 76.3%. [ABSTRACT FROM AUTHOR]

Within a lean context, the aim is to eliminate all forms of waste, including environmental waste, to improve productivity and reduce costs. Key to achieving this objective are operational performance and sustainability indicators. Lean companies must prioritize both operational performance and sustainability, remaining cognizant of their current status. With this in mind, the authors sought to ascertain whether lean companies demonstrate enhanced sustainability. Thus, the authors raised the following research question: does a lean company exhibit greater sustainability? However, these indicators have traditionally been measured independently, and few studies have indicated the need for a global indicator that could simultaneously address both. Such a global indicator would enable a clearer assessment and understanding of the trade-offs between operational performance and sustainability. This paper introduces such an integrated indicator, aiming to measure companies' lean–green compliance by intertwining sustainability issues with overall equipment effectiveness (OEE). The authors have termed this indicator business overall performance and sustainability effectiveness (BOPSE). Its primary goal is to evaluate business effectiveness by considering both operational performance and sustainability compliance. The sustainability strand was drawn from, adapted, and simplified based on the Global Reporting Initiative (GRI). This development was framed in a lean–green environment, emphasizing continuous efforts to identify and reduce all sources of lean waste, alongside the waste prevention perspectives of cleaner production, environmental compliance, and social responsibility, which play crucial roles in shaping the factories of the future. This paper presents the background and development of the BOPSE model. To answer the research question, two research methods were undertaken: a survey and case studies. The model was applied in three distinct case studies, demonstrating its usefulness in discerning varying levels of lean–green compliance through this integrated indicator. [ABSTRACT FROM AUTHOR]

Improving equipment effectiveness is crucial for flexible manufacturing, ensuring that machines and tools perform their functions efficiently and consistently. Our study aimed to enhance the Overall Equipment Effectiveness (OEE) in Bangladesh's Ready-Made Garments (RMG) manufacturing system. We used the DMADV methodology incorporating Visual Stream Mapping (VSM) and OEE. By utilising VSM, we identified issues, eliminated them in the design phase, and evaluated performance in the verification phase. We automated the material handling system to reduce handling time, and the result was a significantly improved OEE in the automated manufacturing system compared to the manual one. This study has numerous benefits in flexible manufacturing and operations management, from immediate efficiency improvements to long-lasting organisational cultural transformations. Thus, it's a noteworthy topic for practical applications and research. Enhancing Equipment Effectiveness through Visual Stream Mapping 4.0 has broad-reaching implications, including improved productivity, reduced waste, increased efficiency, better resource utilisation, and a more agile and responsive manufacturing environment. Although OEE and VSM are frequently used separately in different manufacturing systems, this study's novelty lies in their combined application within garment manufacturing. [ABSTRACT FROM AUTHOR]

This research project focuses on developing a mathematical model that allows us to understand the behavior of the balancing loops in system dynamics in greater detail and precision. Currently, simulations give us an understanding of the behavior of these loops, but under the premise of an ideal scenario. In practice, however, accurate models often operate with varying efficiencies due to various irregularities and particularities. This discrepancy is the primary motivation behind our research proposal, which seeks to provide a more realistic understanding of the behavior of the loops, including their different levels of efficiency. To achieve this goal, we propose the introduction of fractional calculus in system dynamics models, focusing specifically on the balancing loops. This innovative approach offers a new perspective on the state of the art, offering new possibilities for understanding and optimizing complex systems. [ABSTRACT FROM AUTHOR]

From both economic as well as environmental perspective energy demand is of strong and even more increasing relevance for manufacturing nowadays. But typically energy demand and manufacturing system performance (e.g. output, time demands, quality) are considered separately while actually being inherently connected in technical systems. Against this background, the paper presents a data-based approach to analyze and model those important aspects and interdependencies in an integrated manner. The developed theoretical framework and defined methodology enable deeper understanding of energy related impacts of operative influencing factors and eventually to derive fields of action for improvement. The approach was exemplary applied in an industrial case study. Results underline the feasibility, applicability, and potential benefits of the approach. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of the automotive industry, operational efficiency is a pivotal factor for sustained growth. This study delves into the intricacies of enhancing manufacturing operations, mainly focusing on the blowing machines at RMA Ltd, a key player in the Indonesian automotive SME sector. The primary objective is to optimize Overall Equipment Effectiveness (OEE) by implementing a Total Productive Maintenance (TPM) approach. The Indonesian automotive sector, vital to national economic growth, needs help maintaining optimal production efficiency. This study centres on the blowing machines at RMA Ltd's Plant 7, emphasizing the need to address breakdowns, particularly in the blowing machine, which has been identified as the primary source of production losses. A comprehensive research methodology is outlined, beginning with an extensive literature review on TPM and OEE. The study then focuses on the Indonesian automotive SME sector, with RMA Ltd as the primary research subject. Data collection involves an initial survey to assess the current state of blowing machines, encompassing OEE, Six Big Losses, and other relevant factors. Post-implementation of improvements, the study reveals substantial enhancements in OEE. Availability rates increased (93.19%), Performance Efficiency improved (84.84%), and Quality Rate remained consistently high (98.41%). The calculated OEE rose from 67.42% to an impressive 77.80%. Noteworthy reductions in Six Big Losses, particularly in breakdowns, setup losses, and reduced speed losses, validate the efficacy of TPM implementation. This research introduces a novel approach by integrating socialization strategies, detailed work instructions, and proactive maintenance practices. Through a comprehensive research methodology, including an initial survey and post-implementation analysis, this study demonstrates significant OEE improvements of 11%. The findings underscore the novelty of this research in emphasizing the importance of holistic TPM implementation strategies in enhancing manufacturing operations within the Indonesian automotive SME sector. Furthermore, this study provides actionable insights for SMEs in the Indonesian automotive sector, highlighting the relevance of TPM in achieving operational excellence and competitive advantage. Ultimately, this research contributes a valuable blueprint for SMEs seeking to navigate the complexities of the automotive industry, offering a roadmap to optimize manufacturing operations and thrive in a competitive market. [ABSTRACT FROM AUTHOR]

Goal: This research investigated the application of real-time Overall Equipment Efficiency (OEE) at three assembly work centers in a medium-sized company. The objective was to demonstrate the feasibility of integrating Industry 4.0 technologies, such as the Internet of Things, Big Data, and Cloud Computing, in manual work center environments. It aimed to underscore the potential improvements achievable through data-driven actions facilitated by Industry 4.0 technologies, while emphasizing the significance of acquiring real-time OEE data. Design / Methodology / Approach: The research involved theoretical exploration, implementation, data collection (Nov 2022-May 2023), and analysis on assembly workstations in a medium-sized Brazilian eyewear manufacturer. Results: Based on the captured data, the factory implemented a series of corrective actions, leading to a reduction in unplanned stops. The obtained results were significant, as the average efficiency of the studied work centers improved by 12.3% in 7 months, with an increase in performance and in availability. Limitations of the investigation: The analysis faces challenges due time constraints, potentially limiting the full assessment of IoT impact. Seasonal variations in eyeglass production and style-specific demand complicate evaluating the true benefits of Industry 4.0 tools, making effective OEE improvement hard to determine. Practical implications: The study demonstrates a method to gauge manual labor efficiency through Industry 4.0 technologies. Originality / Value: This study shows how Industry 4.0 technologies (IoT, Big Data, Cloud Computing) can be integrated into manual workforces, enhancing efficiency and providing real-time OEE for workers to self-assess. [ABSTRACT FROM AUTHOR]

PT. Marunda Grahamineral is a coal company, in its mining activities using the services of the contractor PT. Riung Mitra Lestari in Pit East Kawi. The objective of this study is to analyze the productivity of the plywood on overloading with the Overall Equipment Effectiveness method. (OEE). With quantitative descriptive methods the researchers developed mathematical models and theories that relate to the land clearance activities of the cover. The result of the calculation of the actual productivity of the Excavator CAT 395 (1215) is 569.61 BCM/hour with an OEE value of 66%, whereas the excavator Cat 395 (1226) is 391.81 BCM / hour with a OEE rate of 45%, where the target productivities of the excavator CAT 396 are 620 BCM per hour. Advanced calculations of the Six Big Losses and analyzed with the pareto chart the greatest factor resulting in the low OEE values of the exhaustor CAT 395 (1215) are reduced speed of 12.9%, and of the extractor CAT 395, (1216) are breakdown losses of 30.8%. In order to efficiency improvements are performed on the OEE factor, with the utilization factor and speed factor the value of OEE is obtained after improvement is 72%, and the performance after improvements is 620,31 BCM./hour on the excavador CAT395 (1215). This value does not meet the worldclass OEE value of ≥ 85%, but for the post-improvement productivity value of 629.31 BCM/hour, the value exceeds the plan's productive target of 620 BCM /hour. [ABSTRACT FROM AUTHOR]

This paper considers the problem of evaluating the effectiveness of the finished goods warehouse of a manufacturing company, in which a modified TPM method - Total Productive Management (TPM2) - was applied to improve productivity. A multi-stage methodology was proposed, including a decision to modify the system, determination of the scope of changes, monitoring the results obtained and a multi-criteria evaluation of the changes made. The decision to make modifications to the existing system was motivated by the lower-than-expected quality of customer service (frequent delivery delays). With regard to the transport department, lean flow pillar activities were focused on analysing losses (muda) in warehouse processes (product loading and package unloading). The purpose of these activities was to minimise interruptions in warehouse processes (product loading and package unloading). "The steps for solving the problem" methodology based on Deming's PDCA cycle was used to solve the problem. The analysis covered, among other things, the information flow processes between production planning and the customer service department, the planning processes of the dispatcher, the efficiency of the loading processes, and the causes of interruptions in warehouse operations. The analyses employed the chronometry of selected works, the 5W + 1H method and the Pareto method. By using the 5S method and some characteristics of the SMED method, the organisation of loading work was decisively changed (shunting yard changes, appropriate buffers for transport equipment). The changes introduced in the system were monitored for several months. Appropriately defined OEE indicators were used to assess the behaviour of the system after the changes. The indicators consider the use of available warehouse time, the efficiency of the loading process and the quality of the tasks performed. The results that can be achieved are presented using the specific example of the finished goods warehouse of a manufacturing company in the FMCG sector.

Coal companies have an overburden stripping production target of 95,000 bcm in January 2022, while the realization of production with the Sumitomo SH 350 LHD excavator and Hitachi Zaxis 350 H excavator is only 77,000 bcm or 82% of the production target. The purpose of this study was to obtain an analysis of the productivity of the Sumitomo SH 350 LHD Excavator (40) and Hitachi Zaxis 350 H Excavator (31) in the overburden stripping activity in January 2022, analyze the obstacle factors that caused the available working hours to be reduced by using the Fishbone diagram method, get an analysis of the Overall Equipment Effectiveness (OEE) value of the Sumitomo SH 350 LHD Excavator (40) and Hitachi Zaxis 350 H (31) Excavator before being optimized, and get the analysis and productivity of the Sumitomo SH 350 LHD Excavator (40) and the Hitachi Zaxis 350 H Excavator (31) which has been optimized with the implementation of Overall Equipment Effectiveness (OEE) to achieve the overburden stripping production target. After analysis and improvement efforts, the total overburden stripping production was 149,000 bcm, which means that it has reached the target and even exceeded the production target of 95,000 bcm/month with the OEE value of the digging equipment of 41% and 43%, respectively. However, the OEE value is still very low compared to the world-class standard OEE value, which is 85% and there is still room for improvement.

Productivity is a key factor in the success of a company, and real-time monitoring systems are necessary to achieve this goal. Manual data collection is time-consuming and exhausting. Industrial Internet of Things (IIoT) technology has been rapidly advancing in monitoring and optimizing industrial processes. Production processes can be disrupted due to machine problems; hence, the need to analyze machine efficiency using the overall equipment effectiveness (OEE) method. This study implements a system that uses Industrial Internet of Things technology based on mobile application to monitor production processes, report production results, assess machine performance using the OEE method and provide maintenance notifications based on time-based maintenance. Research findings indicate that the production monitoring system implemented on a prototype press machine based on an interactive mobile application interface is capable of monitoring production processes and reporting production results. The system can also assess machine performance using the OEE method, with a calculation accuracy of 99.95% and maintenance notifications with a delay time of 1.04 seconds.

Goal: This research investigated the application of real-time Overall Equipment Efficiency (OEE) at three assembly work centers in a medium-sized company. The objective was to demonstrate the feasibility of integrating Industry 4.0 technologies, such as the Internet of Things, Big Data, and Cloud Computing, in manual work center environments. It aimed to underscore the potential improvements achievable through data-driven actions facilitated by Industry 4.0 technologies, while emphasizing the significance of acquiring real-time OEE data. Design / Methodology / Approach: The research involved theoretical exploration, implementation, data collection (Nov 2022–May 2023), and analysis on assembly workstations in a medium-sized Brazilian eyewear manufacturer. Results: Based on the captured data, the factory implemented a series of corrective actions, leading to a reduction in unplanned stops. The obtained results were significant, as the average efficiency of the studied work centers improved by 12.3% in 7 months, with an increase in performance and in availability. Limitations of the investigation: The analysis faces challenges due time constraints, potentially limiting the full assessment of IoT impact. Seasonal variations in eyeglass production and style-specific demand complicate evaluating the true benefits of Industry 4.0 tools, making effective OEE improvement hard to determine. Practical implications: The study demonstrates a method to gauge manual labor efficiency through Industry 4.0 technologies. Originality / Value: This study shows how Industry 4.0 technologies (IoT, Big Data, Cloud Computing) can be integrated into manual workforces, enhancing efficiency and providing real-time OEE for workers to self-assess.

The following work aims to show how a combination of continuous improvement (CI) and Lean tools can reduce waste and process variability along an air-conditioned pipe production line (PL), calculate its capacity, and improve its efficiency to achieve the expected productivity. A variability study focused on the PL's balancing was conducted to identify and reduce possible bottlenecks, as well as to evaluate the line's real capacity. Several layout improvements were made to upgrade the line's operational conditions and reduce unnecessary movements from the workers. The Constant Work-In-Progress (CONWIP) methodology was also applied to ease the component's production management in the preparation stage. Additional modifications were implemented to support production and to contribute to the increases in efficiency, quality, and safety on the line. The results revealed an increase in the line's capacity, associated with an efficiency rise from 28.81% to 47.21% from February to June 2023. The overall equipment effectiveness (OEE) in the same period increased by 18%. This demonstrates that, by interactively applying a mix of tools and methodologies, it is possible to achieve better performance of production lines. This knowledge can help scholars and practitioners to apply the same set of tools to solve usual problems in cell and production lines with performance below expectations. [ABSTRACT FROM AUTHOR]

We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures sustainability by capturing product quality (e.g., less scrap) and measures resilience by capturing availability. A major advantage of the proposed approach is that the engineering models are likely to be available long before the corresponding digitalized smart factory becomes fully operational. Specifically, for BN structure learning, we propose to use analytical queueing theory models of the factory to elicit the structure, and to carry out intervention we propose to use designed experiments based on discrete-event simulation models of the factory. For parameter learning, we apply a qualitative maximum a posteriori (QMAP) method and propose additional expert constraints based on the law of propagation of uncertainty from queueing theory. Furthermore, the proposed approach overcomes the challenge of obtaining balanced-class data in BN learning for fault diagnostics. We apply the proposed BN learning approach to (i) a 4-robot cell in our laboratory and (ii) a robotic machining cell in a commercial vehicle factory. In both cases, the proposed method is found to be efficacious in accurately learning the BN structure and parameter, as measured using structural-hamming distance and Kullback–Leibler divergence score, respectively. The proposed approach can pave the way for a new class of resilient and sustainable smart manufacturing systems. [ABSTRACT FROM AUTHOR]

Purpose: The buffer allocation problem (BAP) arises in the design of production systems; it involves analyzing and defining the optimal distribution of buffers within a production line. This paper presents a BAP formulation in a parallel series line from a cup sublimation process with unreliable operating conditions. The main objective of this study is to develop a new BAP solution proposal, considering the optimization of the OEE indicator used in Lean Manufacturing. Design/methodology/approach: The BAP was analyzed under an optimization approach from two different criteria: firstly, the maximization of the OEE indicator (Overall Equipment Effectiveness) utilized in Lean Manufacturing, as well as the maximization of the average production rate (Throughput). The case study involves unreliable operating conditions. Process times, and timeframes between failures and repairs, consider normal distribution functions. The evaluation method employed in the study includes the use of simulation meta-models built from experiment designs and production line simulations; on the other hand, the nonlinear GRG algorithm is used to solve the mathematical models. Findings: In the study carried out, it is shown that the OEE indicator can be affected when more buffers are allocated than necessary, hence it is important to calculate and establish the best configuration for them through an analysis such as the one proposed in this document. Research limitations/implications: The research is limited to a case study of an unreliable production line from a cup sublimation process. Practical implications: The proposed solution established in this study can be used in other production lines with configurations different from the one analyzed, considering the optimization criterion of the OEE indicator. Originality/value: Seeking that the allocation of buffers within the production line improves the OEE indicator is something new in the literature, therefore, the results achieved in this research become even more relevant. [ABSTRACT FROM AUTHOR]

The main objective of this study is to improve productivity of the rolling mill of the steel plant by implementing Total Productive Maintenance (TPM). Due to the increasing demand of steel products, it is necessary to improve productivity of the rolling mill as various types of structural items are produced by rolling mills. Machine maintenance plays an important role in achieving maximum production in steel industry. The TPM was implemented in an integrated steel plant and its effect on production was observed. First, six months of data were collected, and then the breakdowns were analyzed using TPM. Then, TPM was implemented for the next six months and the productivity of the rolling mill was analyzed. The productivity of the rolling mill was enhanced by~10% by implementing TPM, and equipment availability was also improved by ~14%. However, after the implementation of TPM, the overall equipment efficiency (OEE) reached about 80.19%, which is close to the world steel OEE of 85%. [ABSTRACT FROM AUTHOR]