Journal of Quality in Maintenance EngineeringTable of Contents for Journal of Quality in Maintenance Engineering. List of articles from the current issue, including Just Accepted (EarlyCite)https://www.emerald.com/insight/publication/issn/1355-2511/vol/30/iss/1?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestJournal of Quality in Maintenance EngineeringEmerald Publishing LimitedJournal of Quality in Maintenance EngineeringJournal of Quality in Maintenance Engineeringhttps://www.emerald.com/insight/proxy/containerImg?link=/resource/publication/journal/dc0be394b77a5ae242a92a75d903cacf/urn:emeraldgroup.com:asset:id:binary:jqme.cover.jpghttps://www.emerald.com/insight/publication/issn/1355-2511/vol/30/iss/1?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestDiscussing the applicability of the maintenance management framework for asset management (MMFAM): a hydroelectric power plant case studyhttps://www.emerald.com/insight/content/doi/10.1108/JQME-06-2022-0038/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe Maintenance Management Framework for Asset Management (MMFAM) is a recently modeled framework to ensure the alignment of maintenance management with physical asset management based on the ISO 55000 series for asset management. In this context, the purpose of this paper is to discuss the applicability of the MMFAM considering the operational context of a hydroelectric power plant. The paper adopted the case study method for the discussion of the applicability of the MMFAM to a real operational context. A hydroelectric power plant was chosen as the scope of the case study due to its relevance since the electricity sector is an example of an asset-intensive industry in which asset management performance is fundamental. To gain a detailed understanding of the organization, data were collected through direct requests to the plant, informal meetings with technical collaborators, a technical visit to the hydroelectric plant and on-site data collection. Then, the MMFAM processes were demonstrated based on this information and the results supported the discussion of the MMFAM applicability. The case study provided a deeper understanding of the processes included in the MMFAM. In addition, the results suggested the applicability of the framework to other organizations besides the hydroelectric sector due to its generic approach and the possibility of choosing appropriate tools to support and implement the MMFAM processes. The case study is expected to contribute to the practical understanding of the MMFAM processes within an operational context and assist maintenance professionals and researchers in their implementation in other organizations. Although the literature provides different maintenance management frameworks, their practical discussion based on a real operational context is still a gap. Accordingly, this paper discusses the MMFAM under a case study method to expand its understanding beyond theory and contribute to practical comprehension in depth.Discussing the applicability of the maintenance management framework for asset management (MMFAM): a hydroelectric power plant case study
Renan Favarão da Silva, Gilberto Francisco Martha de Souza
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.1-25

The Maintenance Management Framework for Asset Management (MMFAM) is a recently modeled framework to ensure the alignment of maintenance management with physical asset management based on the ISO 55000 series for asset management. In this context, the purpose of this paper is to discuss the applicability of the MMFAM considering the operational context of a hydroelectric power plant.

The paper adopted the case study method for the discussion of the applicability of the MMFAM to a real operational context. A hydroelectric power plant was chosen as the scope of the case study due to its relevance since the electricity sector is an example of an asset-intensive industry in which asset management performance is fundamental. To gain a detailed understanding of the organization, data were collected through direct requests to the plant, informal meetings with technical collaborators, a technical visit to the hydroelectric plant and on-site data collection. Then, the MMFAM processes were demonstrated based on this information and the results supported the discussion of the MMFAM applicability.

The case study provided a deeper understanding of the processes included in the MMFAM. In addition, the results suggested the applicability of the framework to other organizations besides the hydroelectric sector due to its generic approach and the possibility of choosing appropriate tools to support and implement the MMFAM processes.

The case study is expected to contribute to the practical understanding of the MMFAM processes within an operational context and assist maintenance professionals and researchers in their implementation in other organizations.

Although the literature provides different maintenance management frameworks, their practical discussion based on a real operational context is still a gap. Accordingly, this paper discusses the MMFAM under a case study method to expand its understanding beyond theory and contribute to practical comprehension in depth.

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Discussing the applicability of the maintenance management framework for asset management (MMFAM): a hydroelectric power plant case study10.1108/JQME-06-2022-0038Journal of Quality in Maintenance Engineering2023-09-21© 2023 Emerald Publishing LimitedRenan Favarão da SilvaGilberto Francisco Martha de SouzaJournal of Quality in Maintenance Engineering3012023-09-2110.1108/JQME-06-2022-0038https://www.emerald.com/insight/content/doi/10.1108/JQME-06-2022-0038/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Modeling and solving the multi-objective energy-efficient production planning and scheduling with imperfect maintenance activitieshttps://www.emerald.com/insight/content/doi/10.1108/JQME-10-2022-0068/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions. This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems. The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms. This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.Modeling and solving the multi-objective energy-efficient production planning and scheduling with imperfect maintenance activities
Iman Rastgar, Javad Rezaeian, Iraj Mahdavi, Parviz Fattahi
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.26-50

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

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Modeling and solving the multi-objective energy-efficient production planning and scheduling with imperfect maintenance activities10.1108/JQME-10-2022-0068Journal of Quality in Maintenance Engineering2023-11-24© 2023 Emerald Publishing LimitedIman RastgarJavad RezaeianIraj MahdaviParviz FattahiJournal of Quality in Maintenance Engineering3012023-11-2410.1108/JQME-10-2022-0068https://www.emerald.com/insight/content/doi/10.1108/JQME-10-2022-0068/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Assessment of critical success factors, barriers and initiatives of total productive maintenance (TPM) in selected Ethiopian manufacturing industrieshttps://www.emerald.com/insight/content/doi/10.1108/JQME-11-2022-0073/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia. This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques. According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies. The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this. Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation. This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth.Assessment of critical success factors, barriers and initiatives of total productive maintenance (TPM) in selected Ethiopian manufacturing industries
Mulatu Tilahun Gelaw, Daniel Kitaw Azene, Eshetie Berhan
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.51-80

This research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia.

This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques.

According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies.

The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this.

Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation.

This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth.

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Assessment of critical success factors, barriers and initiatives of total productive maintenance (TPM) in selected Ethiopian manufacturing industries10.1108/JQME-11-2022-0073Journal of Quality in Maintenance Engineering2023-09-22© 2023 Emerald Publishing LimitedMulatu Tilahun GelawDaniel Kitaw AzeneEshetie BerhanJournal of Quality in Maintenance Engineering3012023-09-2210.1108/JQME-11-2022-0073https://www.emerald.com/insight/content/doi/10.1108/JQME-11-2022-0073/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Dynamic civil facility degradation prediction for rare defects under imperfect maintenancehttps://www.emerald.com/insight/content/doi/10.1108/JQME-01-2023-0001/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions. A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach. The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects. This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.Dynamic civil facility degradation prediction for rare defects under imperfect maintenance
Sou-Sen Leu, Yen-Lin Fu, Pei-Lin Wu
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.81-100

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

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Dynamic civil facility degradation prediction for rare defects under imperfect maintenance10.1108/JQME-01-2023-0001Journal of Quality in Maintenance Engineering2023-10-10© 2023 Emerald Publishing LimitedSou-Sen LeuYen-Lin FuPei-Lin WuJournal of Quality in Maintenance Engineering3012023-10-1010.1108/JQME-01-2023-0001https://www.emerald.com/insight/content/doi/10.1108/JQME-01-2023-0001/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Evaluation of TPM adoption factors in manufacturing organizations using fuzzy PIPRECIA methodhttps://www.emerald.com/insight/content/doi/10.1108/JQME-11-2020-0115/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe aim of this paper states that total productive maintenance (TPM) is an improvement tool which employs the effective utilization of employees in order to enhance the reliability of the equipment in consideration. This paper identifies and evaluates the factors accountable for the adoption of TPM methodology in manufacturing organizations. Twenty-four factors affecting the TPM implementation are explored and categorized into five significant categories. Afterwards, these identified TPM factors have been evaluated by using a most popular Multi-criteria decision-making (MCDM) approach namely fuzzy pivot pairwise relative criteria importance assessment (F-PIPRECIA). In this paper, through application of F-PIPRECIA, “Behavioural factor” is ranked first while “Financial factor” the last. Considering the sub-factors, “Top management support and commitment” is ranked first while “Effective use of performance indices” is ranked the last. A further sensitivity analysis indicates the factors that need higher level of attention. The result of current research work may be exploited by the top administration of manufacturing enterprises for assessing their TPM adoption status and to recognize the frail links of TPM application and improve accordingly. Moreover, significant factors of TPM can be identified and deploy them successfully in their implementation procedure. The conclusion obtained from this research enables the management to clearly understand the significance of each considered factor on the adoption of TPM in the organization and hence, provides effective utilization of resources.Evaluation of TPM adoption factors in manufacturing organizations using fuzzy PIPRECIA method
Vikas, Akanksha Mishra
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.101-119

The aim of this paper states that total productive maintenance (TPM) is an improvement tool which employs the effective utilization of employees in order to enhance the reliability of the equipment in consideration.

This paper identifies and evaluates the factors accountable for the adoption of TPM methodology in manufacturing organizations. Twenty-four factors affecting the TPM implementation are explored and categorized into five significant categories. Afterwards, these identified TPM factors have been evaluated by using a most popular Multi-criteria decision-making (MCDM) approach namely fuzzy pivot pairwise relative criteria importance assessment (F-PIPRECIA).

In this paper, through application of F-PIPRECIA, “Behavioural factor” is ranked first while “Financial factor” the last. Considering the sub-factors, “Top management support and commitment” is ranked first while “Effective use of performance indices” is ranked the last. A further sensitivity analysis indicates the factors that need higher level of attention.

The result of current research work may be exploited by the top administration of manufacturing enterprises for assessing their TPM adoption status and to recognize the frail links of TPM application and improve accordingly. Moreover, significant factors of TPM can be identified and deploy them successfully in their implementation procedure.

The conclusion obtained from this research enables the management to clearly understand the significance of each considered factor on the adoption of TPM in the organization and hence, provides effective utilization of resources.

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Evaluation of TPM adoption factors in manufacturing organizations using fuzzy PIPRECIA method10.1108/JQME-11-2020-0115Journal of Quality in Maintenance Engineering2023-10-30© 2023 Emerald Publishing Limited VikasAkanksha MishraJournal of Quality in Maintenance Engineering3012023-10-3010.1108/JQME-11-2020-0115https://www.emerald.com/insight/content/doi/10.1108/JQME-11-2020-0115/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
A mixture non-parametric regression prediction model with its application in the fault prediction of rocket engine thrusthttps://www.emerald.com/insight/content/doi/10.1108/JQME-08-2023-0070/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestIt is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is an important indicator for its health monitoring. By predicting the changing value of the thrust, it can be judged whether the engine will fail at a certain time. However, the thrust is affected by various factors, and it is difficult to establish an accurate mathematical model. Thus, this study uses a mixture non-parametric regression prediction model to establish the model of the thrust for the health monitoring of a liquid rocket engine. This study analyzes the characteristics of the least squares support vector regression (LS-SVR) machine . LS-SVR is suitable to model on the small samples and high dimensional data, but the performance of LS-SVR is greatly affected by its key parameters. Thus, this study implements the advanced intelligent algorithm, the real double-chain coding target gradient quantum genetic algorithm (DCQGA), to optimize these parameters, and the regression prediction model LSSVRDCQGA is proposed. Then the proposed model is used to model the thrust of a liquid rocket engine. The simulation results show that: the average relative error (ARE) on the test samples is 0.37% when using LS-SVR, but it is 0.3186% when using LSSVRDCQGA on the same samples. The proposed model of LSSVRDCQGA in this study is effective to the fault prediction on the small sample and multidimensional data, and has a certain promotion. The original contribution of this study is to establish a mixture non-parametric regression prediction model of LSSVRDCQGA and properly resolve the problem of the health monitoring of a liquid rocket engine along with modeling the thrust of the engine by using LSSVRDCQGA.A mixture non-parametric regression prediction model with its application in the fault prediction of rocket engine thrust
Hao Xiang
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.120-132

It is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is an important indicator for its health monitoring. By predicting the changing value of the thrust, it can be judged whether the engine will fail at a certain time. However, the thrust is affected by various factors, and it is difficult to establish an accurate mathematical model. Thus, this study uses a mixture non-parametric regression prediction model to establish the model of the thrust for the health monitoring of a liquid rocket engine.

This study analyzes the characteristics of the least squares support vector regression (LS-SVR) machine . LS-SVR is suitable to model on the small samples and high dimensional data, but the performance of LS-SVR is greatly affected by its key parameters. Thus, this study implements the advanced intelligent algorithm, the real double-chain coding target gradient quantum genetic algorithm (DCQGA), to optimize these parameters, and the regression prediction model LSSVRDCQGA is proposed. Then the proposed model is used to model the thrust of a liquid rocket engine.

The simulation results show that: the average relative error (ARE) on the test samples is 0.37% when using LS-SVR, but it is 0.3186% when using LSSVRDCQGA on the same samples.

The proposed model of LSSVRDCQGA in this study is effective to the fault prediction on the small sample and multidimensional data, and has a certain promotion.

The original contribution of this study is to establish a mixture non-parametric regression prediction model of LSSVRDCQGA and properly resolve the problem of the health monitoring of a liquid rocket engine along with modeling the thrust of the engine by using LSSVRDCQGA.

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A mixture non-parametric regression prediction model with its application in the fault prediction of rocket engine thrust10.1108/JQME-08-2023-0070Journal of Quality in Maintenance Engineering2023-11-01© 2023 Emerald Publishing LimitedHao XiangJournal of Quality in Maintenance Engineering3012023-11-0110.1108/JQME-08-2023-0070https://www.emerald.com/insight/content/doi/10.1108/JQME-08-2023-0070/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Identification of optimal maintenance parameters for best maintenance and service management system in the SMEshttps://www.emerald.com/insight/content/doi/10.1108/JQME-10-2022-0070/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant. To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations. The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%. The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs
Velmurugan Kumaresan, S. Saravanasankar, Gianpaolo Di Bona
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.133-152

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

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Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs10.1108/JQME-10-2022-0070Journal of Quality in Maintenance Engineering2023-11-27© 2023 Emerald Publishing LimitedVelmurugan KumaresanS. SaravanasankarGianpaolo Di BonaJournal of Quality in Maintenance Engineering3012023-11-2710.1108/JQME-10-2022-0070https://www.emerald.com/insight/content/doi/10.1108/JQME-10-2022-0070/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Joint maintenance planning and production scheduling optimization model for green environmenthttps://www.emerald.com/insight/content/doi/10.1108/JQME-05-2023-0047/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint. A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions. It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%. The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling. Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint. This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.Joint maintenance planning and production scheduling optimization model for green environment
Ahmed M. Attia, Ahmad O. Alatwi, Ahmad Al Hanbali, Omar G. Alsawafy
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.153-174

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

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Joint maintenance planning and production scheduling optimization model for green environment10.1108/JQME-05-2023-0047Journal of Quality in Maintenance Engineering2023-12-04© 2023 Emerald Publishing LimitedAhmed M. AttiaAhmad O. AlatwiAhmad Al HanbaliOmar G. AlsawafyJournal of Quality in Maintenance Engineering3012023-12-0410.1108/JQME-05-2023-0047https://www.emerald.com/insight/content/doi/10.1108/JQME-05-2023-0047/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Design of a remote assistance model for truck maintenance in the mining industryhttps://www.emerald.com/insight/content/doi/10.1108/JQME-02-2023-0024/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestIt is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources. In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization. It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction. The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.Design of a remote assistance model for truck maintenance in the mining industry
Rodolfo Canelón, Christian Carrasco, Felipe Rivera
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.175-201

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

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Design of a remote assistance model for truck maintenance in the mining industry10.1108/JQME-02-2023-0024Journal of Quality in Maintenance Engineering2023-11-14© 2023 Emerald Publishing LimitedRodolfo CanelónChristian CarrascoFelipe RiveraJournal of Quality in Maintenance Engineering3012023-11-1410.1108/JQME-02-2023-0024https://www.emerald.com/insight/content/doi/10.1108/JQME-02-2023-0024/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical studyhttps://www.emerald.com/insight/content/doi/10.1108/JQME-04-2023-0038/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestWith production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches. Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers. The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making. This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study
Maren Hinrichs, Loina Prifti, Stefan Schneegass
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.202-220

With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

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Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study10.1108/JQME-04-2023-0038Journal of Quality in Maintenance Engineering2023-12-14© 2023 Emerald Publishing LimitedMaren HinrichsLoina PriftiStefan SchneegassJournal of Quality in Maintenance Engineering3012023-12-1410.1108/JQME-04-2023-0038https://www.emerald.com/insight/content/doi/10.1108/JQME-04-2023-0038/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Analyzing the role of Multi-Agent Technology in minimizing breakdown probabilities in Manufacturing Industrieshttps://www.emerald.com/insight/content/doi/10.1108/JQME-04-2023-0040/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestEvery company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries. This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them. The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency. The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency. This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.Analyzing the role of Multi-Agent Technology in minimizing breakdown probabilities in Manufacturing Industries
Vikram Singh, Nirbhay Sharma, Somesh Kumar Sharma
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.221-247

Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.

This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.

The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.

The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.

This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.

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Analyzing the role of Multi-Agent Technology in minimizing breakdown probabilities in Manufacturing Industries10.1108/JQME-04-2023-0040Journal of Quality in Maintenance Engineering2023-12-28© 2023 Emerald Publishing LimitedVikram SinghNirbhay SharmaSomesh Kumar SharmaJournal of Quality in Maintenance Engineering3012023-12-2810.1108/JQME-04-2023-0040https://www.emerald.com/insight/content/doi/10.1108/JQME-04-2023-0040/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Spare parts management in industry 4.0 era: a literature reviewhttps://www.emerald.com/insight/content/doi/10.1108/JQME-04-2023-0037/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestSpare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this article is aimed toward a systematic literature review on SPM in Industry 4.0 era and identification of research gaps in the field with prospects. Research articles were reviewed and analyzed through a content-based analysis using four step process model. The proposed framework consists of five categories such as Inventory Management, Types of Spares, Circularity based on 6Rs, Performance Indicators and Strategic and Operational. Based on these categories, a total of 118 research articles published between 1998 and 2022 were reviewed. The technological solutions of Industry 4.0 concepts have provided numerous opportunities for SPM. Industry 4.0 hi-tech solutions can enhance agility, operational efficiency, quality of product and service, customer satisfaction, sustainability and profitability. The review of articles provides an integrated framework which recognizes implementation issues and challenges in the field. The proposed framework will support academia and practitioners toward implementation of technological solutions of Industry 4.0 in SPM. Implementation of Industry 4.0 in SPM may help in improving the triple bottom line aspect of sustainability which can make significant contribution to academia, practitioners and society. The examination uncovered a scarcity of research in the intersection of SPM and Industry 4.0 concepts, suggesting a significant opportunity for additional investigative efforts.Spare parts management in industry 4.0 era: a literature review
Nishant Kulshrestha, Saurabh Agrawal, Deep Shree
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.248-283

Spare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this article is aimed toward a systematic literature review on SPM in Industry 4.0 era and identification of research gaps in the field with prospects.

Research articles were reviewed and analyzed through a content-based analysis using four step process model. The proposed framework consists of five categories such as Inventory Management, Types of Spares, Circularity based on 6Rs, Performance Indicators and Strategic and Operational. Based on these categories, a total of 118 research articles published between 1998 and 2022 were reviewed.

The technological solutions of Industry 4.0 concepts have provided numerous opportunities for SPM. Industry 4.0 hi-tech solutions can enhance agility, operational efficiency, quality of product and service, customer satisfaction, sustainability and profitability.

The review of articles provides an integrated framework which recognizes implementation issues and challenges in the field. The proposed framework will support academia and practitioners toward implementation of technological solutions of Industry 4.0 in SPM. Implementation of Industry 4.0 in SPM may help in improving the triple bottom line aspect of sustainability which can make significant contribution to academia, practitioners and society.

The examination uncovered a scarcity of research in the intersection of SPM and Industry 4.0 concepts, suggesting a significant opportunity for additional investigative efforts.

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Spare parts management in industry 4.0 era: a literature review10.1108/JQME-04-2023-0037Journal of Quality in Maintenance Engineering2024-01-04© 2023 Emerald Publishing LimitedNishant KulshresthaSaurabh AgrawalDeep ShreeJournal of Quality in Maintenance Engineering3012024-01-0410.1108/JQME-04-2023-0037https://www.emerald.com/insight/content/doi/10.1108/JQME-04-2023-0037/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
An innovative method to solve the maintenance task allocation and packing problemhttps://www.emerald.com/insight/content/doi/10.1108/JQME-08-2023-0069/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs. The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans. The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results. This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.An innovative method to solve the maintenance task allocation and packing problem
José Nogueira da Mata Filho, Antonio Celio Pereira de Mesquita, Fernando Teixeira Mendes Abrahão, Guilherme C. Rocha
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.284-305

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

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An innovative method to solve the maintenance task allocation and packing problem10.1108/JQME-08-2023-0069Journal of Quality in Maintenance Engineering2024-02-13© 2024 Emerald Publishing LimitedJosé Nogueira da Mata FilhoAntonio Celio Pereira de MesquitaFernando Teixeira Mendes AbrahãoGuilherme C. RochaJournal of Quality in Maintenance Engineering3012024-02-1310.1108/JQME-08-2023-0069https://www.emerald.com/insight/content/doi/10.1108/JQME-08-2023-0069/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Performance analysis of a complex process industrial unit utilizing intuitionistic fuzzy-based integrated frameworkhttps://www.emerald.com/insight/content/doi/10.1108/JQME-08-2023-0077/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestAn integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed. For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed. For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure. The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit. The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers. Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.Performance analysis of a complex process industrial unit utilizing intuitionistic fuzzy-based integrated framework
Dinesh Kumar Kushwaha, Dilbagh Panchal, Anish Kumar Sachdeva
Journal of Quality in Maintenance Engineering, Vol. 30, No. 1, pp.306-337

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

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Performance analysis of a complex process industrial unit utilizing intuitionistic fuzzy-based integrated framework10.1108/JQME-08-2023-0077Journal of Quality in Maintenance Engineering2024-02-13© 2024 Emerald Publishing LimitedDinesh Kumar KushwahaDilbagh PanchalAnish Kumar SachdevaJournal of Quality in Maintenance Engineering3012024-02-1310.1108/JQME-08-2023-0077https://www.emerald.com/insight/content/doi/10.1108/JQME-08-2023-0077/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industryhttps://www.emerald.com/insight/content/doi/10.1108/JQME-06-2023-0058/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestMaintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0. The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization. The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance. This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry
Nuno Miguel de Matos Torre, Andrei Bonamigo
Journal of Quality in Maintenance Engineering, Vol. ahead-of-print, No. ahead-of-print, pp.-

Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.

The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.

The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.

This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

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Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry10.1108/JQME-06-2023-0058Journal of Quality in Maintenance Engineering2024-03-18© 2024 Emerald Publishing LimitedNuno Miguel de Matos TorreAndrei BonamigoJournal of Quality in Maintenance Engineeringahead-of-printahead-of-print2024-03-1810.1108/JQME-06-2023-0058https://www.emerald.com/insight/content/doi/10.1108/JQME-06-2023-0058/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Repair part service level differentiation based on holding other parts shortage costshttps://www.emerald.com/insight/content/doi/10.1108/JQME-07-2023-0061/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant. Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand. The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time. The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement. The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.Repair part service level differentiation based on holding other parts shortage costs
John Maleyeff, Jingran Xu
Journal of Quality in Maintenance Engineering, Vol. ahead-of-print, No. ahead-of-print, pp.-

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

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Repair part service level differentiation based on holding other parts shortage costs10.1108/JQME-07-2023-0061Journal of Quality in Maintenance Engineering2024-03-19© 2024 Emerald Publishing LimitedJohn MaleyeffJingran XuJournal of Quality in Maintenance Engineeringahead-of-printahead-of-print2024-03-1910.1108/JQME-07-2023-0061https://www.emerald.com/insight/content/doi/10.1108/JQME-07-2023-0061/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited