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Arming at the coexistence dilemma of inventory overhang and shortage of different maintenance equipment supply nodes, a steady-state coefficient of a supply node has been described with which contains inventory level, the support degree between nodes each other, the demand status of the area guaranteed by the node and the cost of risk when the demand cannot be met. The steady-state coefficient of the inventory network was calculated on the base of all of support nodes. The active adustment is carried out flowing the corresponding algorithm according to the gap between the steady-state coefficient and the threshold which has been set before. The scientific, automatic and intelligent level of equipment adjustment can be greatly improved, consequently the success rate of maintenance support is improved if the passive stock adjustment is combined be- cause of out of stock at the same time. [ABSTRACT FROM AUTHOR]

Arming at the coexistence dilemma of inventory overhang and shortage of different maintenance equipment supply nodes, a steady-state coefficient of a supply node has been described with which contains inventory level, the support degree between nodes each other, the demand status of the area guaranteed by the node and the cost of risk when the demand cannot be met. The steady-state coefficient of the inventory network was calculated on the base of all of support nodes. The active adjustment is carried out flowing the corresponding algorithm according to the gap between the steady-state coefficient and the threshold which has been set before. The scientific, automatic and intelligent level of equipment adjustment can be greatly improved, consequently the success rate of maintenance support is improved if the passive stock adjustment is combined because of out of stock at the same time.

Maintenance equipment consumption forecast usually adopts a single prediction model, which has a narrow scope of application and is not accurate in predicting the consumption of complete maintenance equipment. In this paper, the stability consumption characteristics of equipment and maintenance equipment are considered. The comprehensive prediction method of the classification of equipment consumption forecast is carried out. The combination prediction method combines the advantages of the exponential smoothing method, Croston method and TSB method to predict the materials with sTab.consumption and unsTab.consumption respectively. The results verify that the combination forecasting method which avoids the limitation of single forecasting model, has more extensive applicability and improves the accuracy of maintenance material consumption forecasting.

Copyright of Electroplating & Finishing is the property of Electroplating & Finishing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maintenance equipment consumption forecast usually adopts a single prediction model, which has a narrow scope of application and is not accurate in predicting the consumption of complete maintenance equipment. In this paper, the stability consumption characteristics of equipment and maintenance equipment are considered. The comprehensive prediction method of the classification of equipment consumption forecast is carried out. The combination prediction method combines the advantages of the exponential smoothing method, Croston method and TSB method to predict the materials with sTab.consumption and unsTab.consumption respectively. The results verify that the combination forecasting method which avoids the limitation of single forecasting model, has more extensive applicability and improves the accuracy of maintenance material consumption forecasting. [ABSTRACT FROM AUTHOR]

As Permeable Interlocking Concrete Pavement's (PICP) age, the effective porosity of the surface decrease due to accumulation of sediments, causing surface infiltration rates (SIR) to decline leading to reduced performance and requirement for maintenance. Past investigations of maintenance technologies on PICP have produced inconclusive results as most studies have not used standardized experimental designs. This study aims to address these inconsistencies by evaluating five maintenance technologies on uniformly clogged PICP cells. All five evaluated technologies significantly restored the surface infiltration capacity, with one restoring SIRs to original conditions. Mean overall joint penetration depth (JPD) of the applied maintenance treatment was significantly correlated to mean SIRs. However, when comparing PICP cells clogged with cohesive material, SIRs were lower while having higher JPDs. The findings categorize the equipment by maintenance results and any operational considerations of the equipment, which will help advance maintenance guidelines to support long-term performance. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose a decision support system (DSS) for maintenance management of a service system, namely, a street cleaning service vehicle. Referring to the information flow management, the blockchain technology is integrated in the proposed DSS to assure data transparency and security. Design/methodology/approach: The DSS is designed to efficiently handle the data acquired by the network of sensors installed on selected system components and to support the maintenance management. The DSS supports the decision makers to select a subset of indicators (KPIs) by means of the DEcision-MAaking Trial and Evaluation Laboratory method and to monitor the efficiency of performed preventive maintenance actions by using the mathematical model. Findings: The proposed maintenance model allows real-time decisions on interventions on each component based on the number of alerts given by sensors and taking into account the annual cost budget constraint. Research limitations/implications: The present paper aims to highlight the implications of the blockchain technology in the maintenance field, in particular to manage maintenance actions' data related to service systems. Practical implications: The proposed approach represents a support in planning, executing and monitoring interventions by assuring the security of the managed data through a blockchain database. The implications regard the monitoring of the efficiency of preventive maintenance actions on the analysed components. Originality/value: A combined approach based on a multi-criteria decision method and a novel mathematical programming model is herein proposed to provide a DSS supporting the management of predictive maintenance policy. [ABSTRACT FROM AUTHOR]

We consider a stochastically deteriorating system with self-announcing failures that require immediate reactive replacement. For such a system, we consider an age-replacement policy (without minimal repair) under which the system is replaced at failure (reactive replacement) or at a prescribed replacement time (preventive replacement), whichever occurs first. Motivated by factors such as decreasing salvage value or increasing costs associated with obtaining spare parts, we assume that replacement costs are non-decreasing in system age. We formulate a long-run expected cost-rate minimization model with instantaneous replacements that captures this dependency, and provide conditions under which there exists a unique optimal solution. We provide analytical and numerical results that compare the cost-rate minimizing optimal replacement policy, and its performance, to those for the case in which replacement costs are assumed to be constant. Finally, we also consider non-instantaneous replacements, and compare cost-rate minimizing and availability maximizing policies. [ABSTRACT FROM AUTHOR]

The presence of the spare parts stock is a necessity to ensure the continuity of services. The supply of spare parts is a special case of the global supply chain. The main objective of our research is to propose a global spare parts management approach which allows decision makers to determine the essential points in stock management. Thus, it is important for the stock manager to evaluate the system considered from time to time based on performance indicators. Some of these indicators are presented in the form of a dashboard. The presentation of this chapter chronologically traces the progress of our research work. In the first part, we present the work related to the forecast of spare parts needs through parametric and statistical methods as well as a Bayesian modelling of demand forecasting. To measure the appreciation of the supply of spare parts inventory, the second part focuses on work related to the evaluation of the performance of the spare parts system. Thus, we concretize the link between the management of spare parts and maintenance in the third part, more precisely, in the performance evaluation of the joint -management of spare parts and maintenance, in order to visualize the influence of parameters on the system. In the last section of this chapter, we will present the metaheuristic methods and their use in the management of spare parts and maintenance and make an analysis on work done in the literature. [ABSTRACT FROM AUTHOR]

Tanzania water supply systems (WSSs) are facing resilience problems, compromising the quality and coverage of water supply services during floods. The expected rise in flood frequency and magnitude due to climate change and rapid urbanization will result in more frequent and severe impacts to the systems, prompting the need to strengthen resilience to potential impacts and minimize recovery times. This study adopted a five-stage process—literature review, preassessment, pretesting, a three-round Delphi survey, and tool evaluation—to develop a qualitative technical resilience assessment tool (QTRAT). Thematic analysis and standard descriptive statistical analysis were applied during the study. A qualitative tool with 4 principles and 10 indicators is proposed with a final semiquantitative technical index (FSTRI), where system maintenance, future expansion capability, renewal plans, and spare parts and equipment availability are the leading indicators. Evaluation results suggest that Tanzania urban WSSs have moderate resilience and are in particular need of improved design guidelines encompassing resilience aspects. The tool developed in this study contributes to informed decision-making about priority planning and budgeting of water organizations to improve the urban WSSs' technical resilience. [ABSTRACT FROM AUTHOR]

Featured Application: Spare parts distribution network design and planned maintenance intervals applied to the aviation industry. Maintenance management plays a key role in many industries, as maintenance determines the availability of systems, influences their lifespan, impacts customer satisfaction, and as a result affects overall investment profitability. In this context, the aviation industry seeks models to improve efficiency. Researchers seek to provide conceptual models that help to shape the industry's operations. Spare parts management plays a fundamental role in aviation, considering the predominance of planned maintenance. In this study, we analyzed the impact of the distribution network design for spare parts management and the fixed and dynamic planned maintenance intervals on the overall efficiency of an aircraft fleet. We present a conceptual model considering a variety of topics, such as distribution network design, that have been managed to a limited extent based on maintenance management. A simulation model was developed by applying the conceptual model for the aviation industry considering an aircraft fleet over its whole life cycle. The simulation model provides results concerning the impact of the distribution network, maintenance intervals, and other key factors on the efficiency of the aircraft fleet. The simulation enables a comparison of different distribution networks and maintenance strategies to decide which of them is the best fit for each spare part. The approach we propose enables companies and managers to make decisions informed by a centralized tool with all the relevant factors concerning the maintenance management of an aircraft fleet over its life cycle. As a result, managers are provided with a conceptual and simulation model for the assessment of future what-if scenarios based on aggregated databases from multiple sources without delays and with a dynamic vision of the relevant relationships between factors. [ABSTRACT FROM AUTHOR]

Maintenance approaches have been evolving from reactive to a preferred proactive conception with operational reliability as a new implemented strategy. This scenario suggests that a review of conceptual basis is needed. The paper resumes the study carried out with the main objective of building a new concept of operational reliability. Bibliographic research and poll methods were used. Selected specialists with remarkable profiles in Microsoft Academic Search, Google Schoolar and Research Gate were consulted in order to validate the formulated concept. A poll instrument based on Likert Method was designed whereas for analyzing the confidence, Cronbach Alpha Method was used. As a result, a novel concept of operational reliability was built and validated. This concept sets up a synergic based relationship among equipment, human resources and technological processes and, for the first time, provides support to obtain an indicator as a numerical representation of operational reliability. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A properly implemented maintenance management system has an impact at different levels. Maintenance is defined as the set of actions to maintain a property in a specified state. The unavailability of the spare parts required, to carry out the maintenance intervention, causes an extension of the inactivity time of the installation. On the contrary, an excessive stock of spare parts confines enormous capital and entails an enormous cost of ownership. According to the literature already made, we have directed in our work to propose a model of joint management of maintenance and spare parts based on stochastic-deterministic batch Petri networks. We studied this model by simulation using a graphical interface dedicated to the graphical tool used. So, we present, in this paper, the analytical study of the model by defining the performance indicators and viewing the influence of system parameters on these indicators. The main stages of the analytical study are developing the μ-marking graph, the associated Markov process which gives the associated transition matrix, and the definition of performance indicators using the probability distribution of the states. We deal with an application of the analytical evaluation of the proposed model. We end this article with an analysis and synthesis. [ABSTRACT FROM AUTHOR]

• Novel multiobjective model to replacement policy decisions. • Imperfect repair and multiobjective approach in unique replacement policy decision model. • Availability of spare parts affects reliability and cost performances of the policy. • Genetic algorithm coupled with event simulation is a powerful tool to maintenance optimization. This paper proposes a multi-objective approach to model a replacement policy problem applicable to equipment with a predetermined period of use (a planning horizon), which may undergo critical and non-critical failures. Corrective replacements and imperfect repairs are taken to restore the system to operation respectively when critical and non-critical failures occur. Generalized Renewal Process (GRP) is used to model imperfect repairs. The proposed model supports decisions on preventive replacement intervals and the number of spare parts purchased at the beginning of the planning horizon. A Multi-Objective Genetic Algorithm (MOGA) coupled with discrete event simulation (DES) is proposed to provide a set of solutions (Pareto-optimum set) committed to the different objectives of a maintenance manager in the face of a replacement policy problem, that is, maintenance cost, rate of occurrence of failures, unavailability, and investment on spare parts. The proposed MOGA is validated by an application example against the results obtained via the exhaustive approach. Moreover, examples are presented to evaluate the behavior of objective functions on Pareto set (trade-off analysis) and the impact of the repair effectiveness on the decision making. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is an essential task to estimate the remaining useful life (RUL) of machinery in the mining sector aimed at ensuring the production and the customer's satisfaction. In this study, a conceptual framework was used to determine the RUL under the reliability analysis in a frailty model. The proposed framework was implemented on a Komatsu PC-1250 excavator from the Sungun copper mine. Also, the Weibull-frailty model was selected to describe the failure behavior and compare it with the classical-exponential model. The frailty model was also used to evaluate the impact of unobserved environmental conditions on the RUL values. Both applied models were fitted to the obtained data from 80 operational hours of the Komatsu PC-1250 excavator. Plotting the results from the reliability analysis of two models demonstrated that the mine system with the frailty model performs better than the classical model before reaching the reliability of 80%. Besides, the frailty model shows a coherent with the operational time of the excavator, while the classical model demonstrates a sinusoid variation. The obtained results may be used for the development of maintenance, preventive repairs planning, and the spare parts replacement intervals. [ABSTRACT FROM AUTHOR]

The maintenance are defined as all activities which repair, renovation, inspection, etc. so that a system can perform its functions at desired level. The means of system that may be any machine or equipment from the simplest to the most complex in a factory, as well as a hospital's electricity, water, oxygen, heatingventilation etc. system or heating-cooling in a hotel, electricity, water, door locking system, pool etc. The maintenance applications in the hotels are very important in terms of ensuring customer satisfaction, reducing security and operating expenses. It is very important to manage maintenance applications with a management understanding. The purpose of this research is to determine the maintenance views of the unit managers who work in the hotel businesses and who are responsible for maintenance, determining which of the more common maintenance methods of care such as predictive, periodical, break down, to investigate the maintenance management practices and to determine the obstacles in the implementation of these applications in the hotel managment. The universe of the research is composed of managers with 3, 4, 5 star hotels and boutique accommodation businesses in Denizli. In the study, it was observed that the subject that the participants attach great importance to was maintenance and monitoring activities. The biggest obstacle in maintenance management was found to be the inefficient inventory system and the problem of materials and spare parts encountered. [ABSTRACT FROM AUTHOR]

Reducing costs is an important part in todays buisness. Therefore manufacturers try to reduce unnecessary work processes and storage costs. Machine maintenance is a big, complex, regular process. In addition, the spare parts required for this must be kept in stock until a machine fails. In order to avoid a production breakdown in the event of an unexpected failure, more and more manufacturers rely on predictive maintenance for their machines. This enables more precise planning of necessary maintenance and repair work, as well as a precise ordering of the spare parts required for this. A large amount of past as well as current information is required to create such a predictive forecast about machines. With the classification of motors based on vibration, this paper deals with the implementation of predictive maintenance for thermal systems. There is an overview of suitable sensors and data processing methods, as well as various classification algorithms. In the end, the best sensor-algorithm combinations are shown. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to analyse the current system of maintenance in selected company and after identification of crucial problems, to propose the new system of maintenance that will be built based on predictive maintenance. The paper is divided into three sections. First section treats predictive maintenance in theoretical plane. Second section deals with analysis of current situation in the selected company in the maintenance area, while in this section you can find information about spare parts consumption and failure rate per year. Third section is about the proposal of improving maintenance status by introducing predictive maintenance. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to analyse the current system of maintenance in selected company and after identification of crucial problems, to propose the new system of maintenance that will be built based on predictive maintenance. The paper is divided into three sections. First section treats predictive maintenance in theoretical plane. Second section deals with analysis of current situation in the selected company in the maintenance area, while in this section you can find information about spare parts consumption and failure rate per year. Third section is about the proposal of improving maintenance status by introducing predictive maintenance. [ABSTRACT FROM AUTHOR]

Logistics is a support tool for maintenance management. The objective of this research was to develop a model with logistical approach to diagnose maintenance management and, on this basis, to project solutions that would contribute to increasing the availability of assets in the organization studied. The use of methods such as observation, documentary analysis, diagnostic model, bar graph, Ishikawa Diagram, General Organization Model, Estimates Method, Gantt Diagram, Scoring Multi-Attribute Method, Pareto Diagram and the Process Assurance Model, allowed to identify the causes and sub-causes that affect maintenance management, calculate supply logistics cycles, prioritize critical equipment as the basis for determining the most prioritized spare parts and supplies for purchase and documenting the activities that make up the assurance of the maintenance process, contributing to a greater availability of the entity's assets. [ABSTRACT FROM AUTHOR]

In this paper, two new joint maintenance and spare parts provisioning policies for a multi-unit production system are proposed. The production system consists of N identical, independent units, each subject to gradual deterioration. Production rates of the units depend on their operating states. Every maintenance action incurs a high set-up cost which includes the cost of sending a crew to the field, and it is therefore cost effective to maintain several units at the same time. The paper's main contribution is an analytical modeling of a multi-unit production system and the development of effective joint maintenance and spare parts ordering policies for such system. The states of the units are observable through regular inspections and the maintenance and spare part ordering decisions depend on the number of the failed units and the number of available spare parts. The process is formulated as a semi-Markov decision process with the optimality criterion being the minimization of the total long-run expected average cost per unit time. The objective is to determine the optimal levels of the number of failed units to place an order for the spare parts or to initiate group maintenance, and to find the optimal inspection interval. Numerical examples are provided to illustrate the proposed optimization model and to compare the two maintenance policies. Sensitivity analysis is conducted to analyze the effect of several cost components on the optimal levels and on the long run expected average cost rate. [ABSTRACT FROM AUTHOR]

The study was conducted in one of the Federal universities in Nigeria. The aim was to evaluate the effectiveness of the maintenance practices in a power generating plant using an availability ratio method. The plant was placed under study for a period of six months. The operation records were obtained from which the availability ratio was obtained. Questionnaires were also distributed to the 21 staff members in the unit and oral interviews were conducted to ascertain the maintenance strategy practiced by the unit. The study revealed that the unit has an availability of 61%, which is low compared to the standard availability ratio of power generating plants (99.9%). The maintenance practices of the unit were determined ineffective. This ineffectiveness was attributed to poor documentation, low-skilled manpower, lack of a spare parts stock, and deficient operating maintenance strategy, as well as poor maintenance funding and management policy. The study recommended autonomy of the maintenance unit, a better inventory system for work materials and spare parts, practice of a predictive or condition monitoring maintenance strategy, training and retraining of the workforce and acquisition of relevant equipment, such as vehicles and computers, as measures that will, in part, improve the effectiveness of maintenance of the plant. The recommendations of this study can be applied to the power sector in Nigeria, which is considered by many to be grossly ineffective. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study develops an analytical model in order to determine an optimal integrated maintenance plan and spare parts management. We consider a manufacturing system, producing only one type of product, over a finite planning horizon H equal to the sum of all production periods and the production quantity of each period is known. This system is subject to a continuously increasing degradation rate. That is why a preventive maintenance strategy is adopted in order to face the increasing failure rate. We noted that contrarily to the majority of studies in literature, we take into account the impact of the production rate variation on the manufacturing system degradation and consequently on the adopted optimal maintenance strategy. In addition, the real need of spare parts relative to the scheduled maintenance actions is taken into account. In fact, the purpose of our study consists at determining the optimal preventive maintenance frequency and the optimal quantity of spare parts to order by minimizing a total cost, including maintenance and spare parts management. Numerical examples are presented along with a sensitivity study in order to prove the use of the developed model for deriving the optimal integrated strategy for any instance of the problem. [ABSTRACT FROM AUTHOR]

Kitting – meaning to supply assembly with components in presorted kits – is widely seen as beneficial for assembly quality and efficiency when there is a multitude of component variants. However, the process by which kits are prepared – the kit preparation – is labour-intensive, and kit errors are problematic at assembly processes. The use of robotics to support kit preparation has received some attention by researchers, but literature is lacking with respect to how collaborative robots – cobots – can support kit preparation activities. The purpose of this paper is to identify the potential of a cobot to support time-efficient batch preparation of kits. To address the purpose, the paper presents a mathematical model for estimation of the cycle time associated with cobot-supported kit preparation. The model is applied in a numerical example with experimental data from laboratory experiments, and cobot-supported kit preparation is compared with manual kit preparation. The findings suggest that cobot-supported kit preparation is beneficial with diverse kits and smaller components quantities per SKU (Stock Keeping Unit) and provides less variability of the outcome, when compared to manual kit preparation. The paper reveals several insights about cobot-supported kit preparation that can be valuable for both academics and practitioners. The model developed can be used by practitioners to assess the potential of cobots to support kit-batch preparation in association with assembly, spare parts, repair and maintenance, or business to business industry. [ABSTRACT FROM AUTHOR]

In order to increase the inventory of automotive maintenance spare parts, this article takes automobile maintenance spare parts as the research object, and proposes a distributed multi-level spare parts inventory algorithm based on centralized control to address the problems of low satisfaction of the automotive maintenance spare parts warehouse and poor effectiveness of spare parts inventory control. The characteristics of automobile maintenance spare parts supply, vehicle maintenance spare parts supply and vehicle maintenance spare parts inventory management are introduced, and the current status of automobile maintenance spare parts inventory management is analyzed. Using the conditions of centralized ordering and mutual allocation, the functions of inventory cost and estimated inventory cost were calculated. A mathematical model for centralized ordering and mutual spare parts inventory was established. Through the establishment of a model, the research of distributed multi-level inventory algorithm for automobile maintenance spare parts is completed to realize inventory optimization. The experimental results show that the algorithm not only has the advantage of high satisfaction rate of automobile maintenance spare parts library, but also improves the efficiency and control ability of spare parts inventory turnover. [ABSTRACT FROM AUTHOR]

Spare parts provisioning strategies for critical components are essential for maintenance management in a wide range of industrial applications. In practice, some types of spare parts may suffer from on-shelf deterioration, which will not only affect the reliability and availability of the parts themselves but also the overall operational costs. In this context, a natural question that arises is: "Should we use new parts or degraded ones first for component replacement?" Indeed, developing mathematical models to answer this question has both theoretical and practical values. This article studies component replacement and spare parts reordering policies for a system with spare parts experiencing on-shelf deterioration. A two-phase continuous-time Markov chain is utilized to model the on-shelf deterioration process for each spare part, and two different part consumption strategies, i.e., Degraded-First (DF) and New-First (NF) strategies, are formulated. An optimization framework and a solution algorithm are developed for the two strategies to determine the optimal order intervals and order quantities of spare parts for a fixed planning horizon. The monetary performance measures of the two strategies are studied and compared to a random selection (RS) alternative. Numerical examples show that when the overall system-wise replacement demand rate is approximately independent of the part consumption strategies, the DF strategy leads to the biggest savings compared to the RS strategy while the NF strategy results in the highest expected cost among the three alternatives. [ABSTRACT FROM AUTHOR]

Purpose: The maintenance department of today, like many other departments, is under sustained pressure to slash costs, show outcome and to support the assignments of the organization, as it is a commonsensical prospect from the business perspective. Therefore, the purpose of this paper is to find out the wastes level present in maintenance and, after that, do the ranking of maintenance wastes currently present in the operations of Indian organizations with the help of a questionnaire survey. Design/methodology/approach: For this, a database of 421 Indian industries was identified and a structured questionnaire was designed. The questionnaire was divided into two sections A and B to assist with data interpretation. The aim of section A was to build general information of participants and their position, type of organization, number of employees, annual turnover of the organization, etc. Section B was also a structured questionnaire developed based on a five-point Likert scale for assessing the level of maintenance wastes currently present in the maintenance. Findings: Waste of inventory in terms of spare parts storage and obsolesce, etc., waste of rejects/rework/scrap in case of poor maintenance, waste of processing that leads to increases in maintenance and waste of overproduction/excessive maintenance activity has a high level of presence in the maintenance of Indian organizations and gets the top ranks in all the seven types of maintenance wastes. Practical implications: This paper will be extremely useful for the researchers, maintenance professionals and others concerned with maintenance to understand the after effects of maintenance wastes in Indian industries. Originality/value: These findings will be highly valuable for professionals relating to manufacturing sector desiring to implement effective maintenance approach in the maintenance management system. [ABSTRACT FROM AUTHOR]

Multiple studies have analyzed the importance of preventive maintenance. Similarly, they have developed and/or evaluated various spare part inventory management policies that help companies reduce the duration of production disruptions resulting from sudden mechanical failures. All of these studies assess the impact of mechanical failures on local aspects, such as idle times or management policies. However, they do not holistically evaluate how machine failures affect key performance indicators. To address this gap, we proposed a System Dynamics (SD) model to systemically analyze the impact of machine part failures on order shipping, a key performance indicator. Similarly, we assessed the impact of machine part failures on the levels of the company's finished goods inventory. The research was conducted in a glass bottle manufacturing company. To increase order shipping as a performance indicator, we identified the key variables of the production process and conducted a sensitivity analysis of the variables. Our results indicate that it is possible to reach the company's 98–100% complete delivery policy. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values. Design/methodology/approach: A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts. Findings: This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity. Research limitations/implications: Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study. Practical implications: The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities. Originality/value: This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity. [ABSTRACT FROM AUTHOR]

Spare parts have become ubiquitous in modern societies, and managing their requirements is an important and challenging task with tremendous cost implications for the organisations that are holding relevant inventories. Demand for spare parts arises whenever a component fails or requires replacement, and as such the relevant patterns are different from those associated with ‘typical’ stock keeping units. Such demand patterns are most often intermittent in nature, meaning that demand arrives infrequently and is interspersed by time periods with no demand at all. A number of distributions have been discussed in the literature for representing these patterns, but empirical evidence is lacking. In this paper, we address the issue of demand distributional assumptions for spare-parts management, conducting a detailed empirical investigation on the goodness-of-fit of various distributions and their stock-control implications in terms of inventories held and service levels achieved. This is an important contribution from a methodological perspective, since the validity of demand distributional assumptions (i.e. their goodness-of-fit) is distinguished from their utility (i.e. their real-world implications). Three empirical datasets are used for the purposes of our research that collectively consist of the individual demand histories of approximately 13,000 SKUs from the military sector (UK and USA) and the Electronics Industry (Europe). Our investigation provides evidence in support of certain demand distributions in a real-world context. The natural next steps of research are also discussed, and these should facilitate further developments in this area from an academic perspective. [ABSTRACT FROM PUBLISHER]

We develop an economic model for the optimization of maintenance procedures in a production process with two quality states. In addition to deteriorating with age, the equipment may experience a jump to an out-of-control state (quality shift), which is characterized by lower production revenues and higher tendency to failure. The times to quality shift and failure are allowed to be generally distributed random variables. We consider two types of maintenance: minimal maintenance (MM) that upgrades the quality state of the equipment without affecting its age and perfect preventive maintenance (PM) that fully upgrades the equipment to the as-good-as-new condition. We derive the expression for the expected profit per time unit and we investigate, through a large number of numerical examples, the type of the optimal solution. It is concluded that in practically every case the optimal maintenance policy is an extreme one: it either calls for immediate MM as soon as a quality shift occurs (active policy) or it allows operation in the out-of-control state until the time of a scheduled PM action (passive policy). [ABSTRACT FROM AUTHOR]

Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipment's state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown but can be estimated based on observed condition. We use a proportional hazards model (PHM) to represent the system's degradation. Since equipment's state is unknown, the optimization of the optimal maintenance policy is formulated as a partially observed Markov decision process (POMDP), and the problem is solved using dynamic programming. Practical advantages of combining the PHM with the POMDP are shown. [ABSTRACT FROM AUTHOR]

The Department of Defense (DoD) must sustain aircraft through the platform's life cycle. Additive manufacturing could enable DoD organizations to produce spare parts themselves (i.e., organic production), at the pointof- need, anywhere around the globe. For the government to produce spare parts in-house, new business models between the manufacturer and the government have to be established for transacting technical data packages instead of physical spare parts. Can a digital business model be profitable, and can a return on investment (ROI) be achieved within a commercial time horizon? The research includes a survey within government and industry and an analysis using an aviation case study to examine the profitability of four business models. The results show that under the right conditions of nonrecurring costs, digital service costs, and file demand quantities, digital business models can indeed be profitable, although conditions leading to an ROI within 2 or 4 years are limited. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to identify the main reasons for spare parts logistics failures and address logistics distribution design in order to achieve the desired level of after-sales maintenance service. Design/methodology/approach: This research is based on an empirical case study on a large corporation providing worldwide with retail banking hardware, software and services. The case study focuses on the automated teller machine (ATM) part of activities, with a focus on the spare parts distribution and after-sales service network in the Eastern Europe. Findings: The proposed network solution of multiple distribution centers with short-cut distance saving approach will enable the case study company to redesign their spare part logistics architecture in order to achieve short response time. Research findings reveal possible spare parts delivery delays and thus the service-level agreement failures with clients in the case study company. Research limitations/implications: This research covers a particular supply chain environment and identified research gaps. It discusses a time-based responsive logistics problem and develops a conceptual framework that would help researchers to better understand logistics challenges of installed equipment maintenance and after-sales service. Originality/value: This case study research shows the "big picture" of spare parts logistics challenges as vital part of installed equipment after-sales and maintenance service network, as well as emphasizes how the unique context of a market like Russian Federation can set-up a distribution network efficiently. Strategies applied to handle such service-level failures, reverse logistics aspects of repairable and non-repairable spare parts to such large ATM after-sales service network based on this longitudinal case offer value for similar scale companies. [ABSTRACT FROM AUTHOR]

We consider the problem of optimally maintaining a stochastically degrading, single‐unit system using heterogeneous spares of varying quality. The system's failures are unannounced; therefore, it is inspected periodically to determine its status (functioning or failed). The system continues in operation until it is either preventively or correctively maintained. The available maintenance options include perfect repair, which restores the system to an as‐good‐as‐new condition, and replacement with a randomly selected unit from the supply of heterogeneous spares. The objective is to minimize the total expected discounted maintenance costs over an infinite time horizon. We formulate the problem using a mixed observability Markov decision process (MOMDP) model in which the system's age is observable but its quality must be inferred. We show, under suitable conditions, the monotonicity of the optimal value function in the belief about the system quality and establish conditions under which finite preventive maintenance thresholds exist. A detailed computational study reveals that the optimal policy encourages exploration when the system's quality is uncertain; the policy is more exploitive when the quality is highly certain. The study also demonstrates that substantial cost savings are achieved by utilizing our MOMDP‐based method as compared to more naïve methods of accounting for heterogeneous spares. [ABSTRACT FROM AUTHOR]