•Adaptive ARIMA model is proposed to predict the machinery breakdown.•The oil contamination is the parameter identified for analysis in the foundry.•The proposed model reduces catastrophic breakdown ...by 84%.•The system focused towards zero downtime by predicting the signature parameter.
The unpredicted breakdowns in any industrial plant paves way for huge loss to the industry in terms of production and profit. If near future breakdowns are known well ahead of time, zero downtime can be achieved, maintaining the demand–supply chain which leads to industry-4.0 standard. This paper addresses the challenges involved in such transformation and proposes a monitoring and control procedure to reduce catastrophic breakdown by 84%. The parameters and signatures related to the breakdown phenomenon which are restricting zero down time in industry-3.0 are analysed. Adaptive ARIMA model based machine learning to support adaptive error prediction model through varying windowing technique is proposed to predict the future breakdown before its occurrence by forecasting the important signature parameters in the machinery. The proposed maintenance approach is implemented in a high-pressure hydraulic sand moulding machine in an automotive grey casting manufacturing foundry. In this work, the oil contamination level is the parameter identified for analysis in the high pressure sand moulding line in foundry. The breakdown in minutes per month and the number of breakdown occurrences in a month due to various phenomena are analysed after implementing the proposed approach. The proposed system ensures promising results which increases Mean Time Between-Failure by 800% and thereby achieving zero downtime in industries.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Purpose
The purpose of this paper is to investigate the impact of total productive maintenance (TPM) and total quality management (TQM) practices on operational performance and their ...inter-relationship.
Design/methodology/approach
The present study includes three main constructs, namely, TPM, TQM and operational performance of pharmaceutical industry. Under TPM, four constructs, namely, disciplined maintenance, information tracking, housekeeping and operator involvement has been considered with the help of literature. In TQM, four constructs, namely, quality data and reporting, product innovation, research and development (R&D) management and technology management has been considered. Out of 410 Indian pharmaceutical plants contacted for survey, 254 responses have been used in the study for analysis. The factor analysis, path model and structural equation modeling has been used to analyze the proposed framework. The results for alternate models has been studied, interpreted and reported. Finally the direct and indirect effect of TPM and TQM on operational performance has been tested and checked for proving and disproving the hypotheses.
Findings
TPM practices have a significant impact on plant-level operational performance. When TPM and TQM practices are coming together to achieve operational performance, then TPM is having strong influence on operational performance. TQM is having significant support from TPM to achieve operational performance. TPM impact TQM and TQM in turn helps to achieve operational performance. TPM practices impact significantly R&D, product innovation and technology management, whereas quality data and reporting is the least contributor toward TQM. This may help industry to understand implications of implementation of TPM and TQM to achieve plant-level operational performance. TPM will help to reduce the cost of quality in terms of reduced scrap and less defective products.
Practical implications
The present study provides the useful insights to practicing managers. In literature it has been mentioned that TQM helps in TPM implementation. In practice TPM plays a great role to achieve quality in processes and therefore in products. In turn quality products, with reduced work in process inventory, less defective products and reduced scrap helps to achieve the operational performance at plant level. TPM practices will help the organization to improve the pace of product innovation and improvement in productivity, which is critical to pharmaceutical industry. The continuous monitoring of TPM practices can help organizations to run day to day operations and maintenance requirement of each machine over a specified period of time.
Originality/value
The present study diagnoses the inter-dimensional linkage between TPM, TQM and operational performance. The pharmaceutical industry is complex system of advance equipment’s and processes. After human resources, the health of machines/equipment’s describe the strength of an organization. The machines require the regular maintenance to produce the products with desired specifications. The specifications in medicines and very tight, which can be achieved only if machines/testing equipment’s are updated and maintained regularly. The TPM practices will helps the plants to achieve the operational performance by having quality in processes.
Predictive maintenance (PdM) is a prominent anomaly prediction strategy in the manufacturing system given the increasing need to minimize downtime and economic losses. It is available for PdM to ...monitor industrial equipment continuously with smart electrical sensors and predict the health condition with machine learning algorithms. However, the performance of previous algorithms is often limited by lacking consideration of both attribute contribution to final results and temporal dependence. To solve the problem, this article introduces a general PdM framework based on Internet-of-Things technology, cloud computing, and total productive maintenance. In this framework, an attribute attentioned long short-term memory network (A2-LSTM) is proposed. The A2-LSTM takes a sequence of electrical records as input to extract attributes. Afterwards, different attributes are fused into the attribute attention network, which can adjust the importance of each attribute automatically. Next, the reweighted attributes are fed into the health prediction component to establish temporal dependence for the manufacturing system. Finally, the output of A2-LSTM, i.e., remaining useful life, can support workers to carry out equipment maintenance. The effectiveness of the method is verified by real-world cases and the comparison results show that the A2-LSTM is promising.
•An A2-LSTM network is proposed to remove redundant attributes.•An attention mechanism is proposed to assign higher weights to important attributes.•A general PdM framework is established to model system temporal dependence.•The effectiveness of the proposed method is verified by real-world cases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Total Productive Maintenance (TPM), an important maintenance management approach grounded on lean principles, has found widespread applicability in the manufacturing sector since the 1950's. More ...recently, Industry 4.0 (I4.0) has promoted the digitalization of several functional areas bringing disruptive innovation to manufacturing environments. This paper aims at examining the integration of I4.0 technologies into Total Productive Maintenance (TPM) practices in multiple large-sized manufacturing companies. Case-based research is adopted to investigate four manufacturers located in Southern Brazil. Guided by the Diffusion of Innovation Theory (DIT) as a theoretical lens, within-case and cross-case analyses are performed. Findings indicate that the integration of I4.0 into TPM results in benefits and barriers associated with DIT's attributes (real advantage, compatibility, complexity, trialability, and observability), although their degree and orientation vary according to the attribute. Identifying aspects that could constrain a successful digitalization of TPM may help companies develop countermeasures to promote innovation adoption. Our results may help practitioners with the digitalization of TPM by offering a more realistic vision of the difficulties and benefits they may experience in practice. We conclude the paper with five research propositions derived from the multiple case data findings.
•Integration of Industry 4.0 (I4.0) into Total Productive Maintenance (TPM).•Multiple case-study conducted in large-sized manufacturers.•Diffusion of Innovation Theory (DIT) used as a theoretical lens.•Benefits and barriers are associated with DIT's attributes.•Five research propositions derived from data analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Correction of: https://josi.ft.unand.ac.id/index.php/josi/article/view/642 . DOI: 10.25077/josi.v22.n1.p52-60.2023 A ventilator machine is a medical device that plays an important role in handling ...Covid-19 cases during a pandemic. Covid 19 patients are arriving at a referral hospital in Jakarta, meaning that the hospital must prepare its medical equipment, including a ventilator machine. The ventilator machine experienced problems because the efficiency of the machine decreased so many patients waited in the ICU room. Machine effectiveness has an average value of 62.26% so it has an impact on disrupting patient services at home. The purpose of this research is to look for factors that cause the lack of effective value of ventilator machines and find effective solutions to increase the effectiveness of ventilator machines so that they can serve Covid-19 patients during a pandemic. This research method combines Focus Group Discussion in determining 5W+1H and implementing the pillars of Total Productive Maintenance to improve the Overall Effectiveness of Equipment on Ventilator machines. This study found a breakdown factor of 54.10%, idle and minor loss of 41.20%, and others of 4.70%. The effect of overall improvement has been able to increase the average effectiveness value by 85.00% from Sep 2021-Feb 2022, so this is supported by the hospital's program in terms of increasing the effectiveness of machines in patient care.
The Handbook of Maintenance Management and Engineering covers a wide range of topics in maintenance management and engineering. It includes extensive references to the theoretical foundations, recent ...research and future directions of this important subject. Using applications and examples which reflect the growing importance of maintenance, this book presents readers with an inter-disciplinary perspective on topical issues which affect any organization engaged in manufacturing, process, or service industry, no matter how large or small. Contributors to the book are maintenance experts with both academic and industrial backgrounds, who are able to offer a comprehensive analysis of the subject matter, including both quantitative treatment and discussion of management issues. The Handbook of Maintenance Management and Engineering features both fundamental and applied works from across the whole maintenance spectrum. It will provide professionals with the solutions and management skills needed to evaluate and to continuously improve maintenance systems. This handbook will also be an invaluable resource for researchers and graduate students working in this area.
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FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Purpose: This paper develops a "light" total productive maintenance (TPM) model suitable for small and medium-sized enterprises (SMEs). By design, the system is rudimentary, using a relatively small ...sum of capital investment and resources. The model recommends TPM implementation in three stages, namely plan, improve, and sustain. Design/methodology/approach: The literature review provides the inputs to the model development. Action research is used to demonstrate and verify the effectiveness and practicability of the framework, in an SME manufacturing hydraulic parts in China. Overall Equipment Effectiveness (OEE) and awareness of employees were studied before and after the implementation. Findings: The case study shows a significantly improved production efficiency of the equipment. The framework structuralizes TPM deployment and binding different levels of the organization into the program, from planning, implementation to sustaining the practices. To break the barrier of shop-floor resistance, the leader must drive many activities unassisted, it, therefore, necessitates an open endorsement of authority by the steering committee composed of top management. The Prudent pilot run of TPM helped to accelerate the implementation at critical equipment, in addition to cultivating experience and hence confidence among staff. Research limitations/implications: This study provides a pragmatic reference to other researchers and practitioners to promote a light TPM model in SMEs, without losing the essence of TPM. Being action research with the case study in a specific manufacturing industry, the resultant evidence, therefore, is anecdotal. Originality/value: The model adopts a phased method to implement TPM, without aggravating the financial and human resource burden of the enterprise. It promotes the cultivation of employees' TPM awareness and active involvement, which can lay a solid foundation for the wide implementation of TPM in SMEs.
•Waterborne epoxy was used to improve the performance of bitumen emulsion.•Florescence tests were conducted to monitor the distribution of waterborne epoxy in bitumen matrix.•Lab tests were conducted ...to evaluate the durability of the coating layer prepared with modified emulsion and fine aggregate.•Optimum waterborne epoxy content and aggregate size were identified.•The coating layer with modified emulsion significantly improved the durability of conventional coating layer.
Bitumen emulsions have been widely used for preventive maintenance of pavements in spite of the existing concerns regarding their inadequate mechanical strength and low early strength. Thus, the conventional preventive maintenance techniques using bitumen emulsions are not sufficiently durable in many cases. This study aims to investigate the performance of waterborne epoxy resin modified bitumen emulsions (WEB) as a pavement preventive maintenance material. Florescence tests were performed to characterize the morphology of the bitumen emulsions containing different percentages of waterborne epoxy resin. Pull-off tests were adopted to evaluate the adhesion strength evolution of the modified emulsions with time. Wet track abrasion tests were conducted to optimize the aggregate size for WEB coating layer. Finally, custom-designed rubbing tests were conducted to simulate tyre wearing on pavement surface, and the skid resistances of the WEB coating layer under different wearing conditions were measured and compared with those of a conventional sand fog sealing layer. The laboratory testing results revealed that waterborne epoxy distributed evenly within the bitumen matrix and significantly improved the skid resistance and durability of the pavements maintained with bitumen emulsion coating layer. The optimum dosage of waterborne epoxy and optimum aggregate size range for the WEB coating layer were also identified, which were 50% by weight of bitumen emulsion and 0.45–0.9 mm, respectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
It generally takes 2.5 to 3 years for an enterprise to implement total productive maintenance (TPM) in a full swing; however, the actual duration depends on each company's corporate status. Generally ...speaking, for a company with big staff, it even takes 3 to 5 years, but the major consideration should depend on whether the actual benefit is remarkable or not. This research aims to find out whether TPM was carried out by enterprises, to inspect the enterprises’ current performance, and to make comparison with those enterprises that have successfully carried out TPM. Through the findings, we hope to provide reference for enterprise owners when they carry out TPM, so as to reduce the cost for fumble.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP