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1 – 10 of over 52000The main purpose of this paper is to systematically identify important factors that are considered in decision making of maintenance cost and discuss how these factors affect…
Abstract
Purpose
The main purpose of this paper is to systematically identify important factors that are considered in decision making of maintenance cost and discuss how these factors affect maintenance performance.
Design/methodology/approach
This paper employs triangulation technique, which combines quantitative and qualitative approaches. The paper starts with the identification of dominant factors through literature reviews followed by semi‐structured interviews with ten building managers and questionnaire survey. A set of questionnaires are distributed to 200 selected buildings managers in Malaysia. The results from 62 completed questionnaires form a database for the quantitative analysis.
Findings
This paper concludes that the maintenance performance suffers from the insufficient allocation of maintenance cost. The main factors that are usually considered by the building managers in allocation of maintenance costs are availability of funding, client's preference, and economic situation. Associative test results reveal that variance in maintenance cost could be improved by considering condition of building and complaint about building performance during decision making of maintenance cost.
Practical implications
This paper provides information for building manager on important factors that need to be considered during decision making of maintenance cost allocation. This would help the manager improve effectiveness and accuracy in preparing a maintenance budget.
Originality/value
With the building maintenance sector in Malaysia being conditionally driven and usually carried out only when there is money, it is critical that organization make effective decisions on priority. This paper determines the most important factors in decision making of maintenance budget.
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Alexandros Bousdekis, Babis Magoutas, Dimitris Apostolou and Gregoris Mentzas
The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for…
Abstract
Purpose
The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM.
Design/methodology/approach
Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed.
Findings
In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified.
Practical implications
The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required.
Originality/value
The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.
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Roberto Sala, Marco Bertoni, Fabiana Pirola and Giuditta Pezzotta
This paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance…
Abstract
Purpose
This paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.
Design/methodology/approach
The Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.
Findings
The interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.
Originality/value
The paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.
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Ravdeep Kour, Phillip Tretten and Ramin Karim
The purpose of this paper is to demonstrate how research within the railway sector is developing eMaintenance solutions using the cloud and web-based applications for improved…
Abstract
Purpose
The purpose of this paper is to demonstrate how research within the railway sector is developing eMaintenance solutions using the cloud and web-based applications for improved condition monitoring, better maintenance and increased uptime. This eMaintenance solution is based on the on-line data acquisition, integration and analysis leading to effective maintenance decision making.
Design/methodology/approach
In the proposed methodology, data are acquired from railway measurement stations to the eMaintenance cloud, where they are filtered, fused, integrated and analysed to assist maintenance decisions. Extensive consultation with stakeholders has resulted in the analysis of railway data.
Findings
The paper provides a concept for a web-based eMaintenance solution for railway maintenance stakeholders for making fact-based decisions and develops more efficient and economically sound maintenance policies. Train wheels reaching their maintenance and safety limits are visualised in grids and graphs to assist stakeholders in making the appropriate maintenance decisions.
Practical implications
In this paper the authors have demonstrated that the wheel profile and force data can be remotely collected through cloud utilisation. The information generated can be used for maintenance decision making. Similarly, other measurable data can also be utilised for maintenance decision making.
Originality/value
This paper describes the importance of eMaintenance solution through online data analysis to make effective and efficient railway maintenance decisions, as a case study.
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Amitkumar Patil, Gunjan Soni, Anuj Prakash and Kritika Karwasra
In today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an…
Abstract
Purpose
In today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an extensively difficult problem for maintenance engineers. Over the years, a diverse range of solution methodologies have been developed for solving this multi-criteria decision-making (MCDM) problem. In this paper, the authors have presented a comprehensive review of latest maintenance strategy paradigms and solution approaches proposed for the selection of an appropriate strategy in various industries. It would provide a systematic mapping of developments in this field and identify some research gaps to explore further studies.
Design/methodology/approach
A systematic state-of-the-art comprehensive literature review on maintenance strategy paradigms and selection approaches is presented in this study. In this study, 87 research articles published in peer-reviewed journals, since year 2012, are reviewed.
Findings
For the selection of a suitable maintenance strategy, a variety of criteria are considered to better evaluate the alternatives. In this study, contemporary strategies are discussed, and their applications in different industries are also depicted. Moreover, through the analysis of extant literature, critical criteria are selected and classified in six major categories (namely, economic, technical, safety, environmental, feasibility and social) and further sub-categorized in quantitative and qualitative classes. These clusters of criteria can be helpful as an initial set of criteria for survey and then case- or industry-specific criteria can be shortlisted for further alternative evaluation.
Practical implications
From the perspective of maintenance managers, maintenance management can be a very difficult task, considering the numerous factors affecting the decision-making process. In order to help in the decision-making process, this study presents the contemporary maintenance strategies in a systematic manner. In a previous study (Kothamasu et al., 2006), these strategies were classified into repair and prevent classes only. With the developments of autonomous maintenance and design out maintenance (DOM), it was fair to include continuous improvement class. It will help managers and practitioners to identify, according to organization policy, appropriate maintenance strategy alternatives for the asset. A benchmark set of state-of-the-art maintenance strategies are laid out with their applications. The industrial case studies discussed in this study summarizes the optimal maintenance strategies for respective industries. Also, most critical criteria are identified from the existing studies for various industries that can help maintenance practitioners in acknowledging the critical factors and making appropriate decisions. Evaluation parameters for the maintenance strategy selection (MSS) generally conflict with each other, and considering the difficulty of quantifying the qualitative measures, it is a challenging task to determine the optimal trade-off. In order to overcome these challenges, popular MCDM approaches, demonstrating effective results across different industries are discussed with their limitations and applications. Decision-makers can refer this study to identify best suitable decision-making technique for the MSS problem in the industry of their choice. Maintenance managers and engineers can refer the case studies illustrated in Tables 1 and 2 to analyse the MSS techniques proposed by previous studies with industry-specific applications.
Social implications
This study is an attempt to provide a reference point for research scholars interested in the field of maintenance management and/or development of maintenance strategy framework. This study provides a critical state-of-the-art review of efforts made in the field of MSS. The prominent maintenance strategies being implemented in contemporary industries are discussed with respective case studies. Interested researchers and academicians can familiarize themselves with these strategies and their distinct features in this study. In order to guide future studies and provide a reference point for academicians, MSS critical criteria used in extant literature are identified and classified into a comprehensive benchmark framework. Moreover, the industrial case studies are discussed with the most critical criteria of MSS for different industries and which strategy is most suitable for the respective industries based on these criteria. Table 1 presents different MCDM techniques and their hybrid applications for solving MSS problem that can help researchers in identifying research gaps. Future research can be directed at addressing the limitation of MCDM approach employed in existing studies and comparing the differences in results obtained by the proposed approach. Different industrial case studies with considered maintenance strategy alternatives are presented in Table 2, which can help researchers in identifying the industries that have not been studied yet. Moreover, not all of the existing studies are carried out by considering all the presented benchmark strategies, which can be addressed in future studies by interested researchers. More detailed discussion on research gaps is presented in the following section.
Originality/value
From the analysis of the extant literature, the authors could observe that the decision-making process adopted in numerous studies was limited to the classical maintenance strategies and not inclusive of aggressive maintenance strategy alternatives. To overcome these limitations and help maintenance managers in the decision-making, this study depicts the contemporary maintenance strategies, critical evaluation criteria and MCDM frameworks (employed to solve the MSS problem with industrial case studies) in a structured manner.
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Mohammad A. Hassanain, Sadi Assaf, Abdul-Mohsen Al-Hammad and Ahmed Al-Nehmi
The purpose of this paper is to present the development of a multi-criteria decision-making model for use by maintenance managers to consider before making a decision on…
Abstract
Purpose
The purpose of this paper is to present the development of a multi-criteria decision-making model for use by maintenance managers to consider before making a decision on outsourcing.
Design/methodology/approach
Thirty-eight factors were identified for outsourcing maintenance services. These factors were grouped under six categories, namely: “strategic”, “management”, “technological”, “quality”, “economic” and “function characteristics”. The Analytic Hierarchy Process, as a multi-criteria decision-making model, was introduced and applied as an approach for maintenance managers in Saudi Arabian universities to consider before making a decision on outsourcing. A case study on the outsourcing decision of maintenance services of air-conditioning systems was carried out to apply the developed model.
Findings
Data analysis indicated that all outsourcing decision groups of factors have almost equal weight, with the “quality” group of factors having the highest weight and the “technological” group of factors having the least weight. Further, the analysis indicated, in general, that the recommended decision for the maintenance managers is to outsource. However, an application of the developed model through a case study on the outsourcing of maintenance services of air-conditioning systems showed that the recommended action is not to outsource.
Originality/value
The presented approach in this paper could be of practical benefit to maintenance managers in their decision making of whether or not to outsource maintenance services. The factors in the model were identified through a literature survey of research carried out in different countries. Therefore, the model could be applied in different settings, depending on the relative weight of the factors by the users.
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Maren Hinrichs, Loina Prifti and Stefan Schneegass
With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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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Rosmaini Ahmad and Shahrul Kamaruddin
The purpose of this paper is to present the development of a maintenance engineering policy in the context of a decision support model based on a production machine process…
Abstract
Purpose
The purpose of this paper is to present the development of a maintenance engineering policy in the context of a decision support model based on a production machine process perspective.
Design/methodology/approach
The structure of the policy is called the maintenance decision support (MDS) model, which consists of three steps: initial setup, deterioration monitoring, and decision making. A detailed presentation of each step of the proposed model together with a real case example from the pulp manufacturing industry proves the applicability of the model.
Findings
Validation of the proposed MDS model is as follows. In Task 1 of Step 1, the cutting, sealing, and perforating line processes are classified as critical machining processes. The analysis of Task 2 of Step 1 found that cutting knife, bearing, and motor are classified as the components that most possibly contribute to the cutting appearance quality. In Task 3 of Step 1, it was found that the cutting knife is classified as a maintenance-significant component with non-repairable and single-component type characteristics. The result of Step 2 suggested that at the 29th hour of operating time, the decision of do-something was suggested. In the following step (Step 3), for the case of the cutting knife, which has been classified as a non-repairable type component, the decision to perform preventive replacement of cutting knife is recommended to be carried out at the 29th hour of operating time.
Research limitations/implications
The uniqueness of this model is that it systematically considers different machinery component(s) characteristics, including single- and multiple-component cases, repairable and non-repairable types, and functional or/and physical failure types, to make maintenance decisions.
Practical implications
The proposed MDS model provides a systematic guideline for identifying, evaluating, and monitoring, which makes maintenance-related decisions. Three significant maintenance decisions can be determined based on the proposed MDS model, which includes an appropriate time-to-perform maintenance, correct maintenance actions to be performed, and the right component required for maintenance (for multi-component cases).
Originality/value
One of the vital elements in considering the production machine process perspective toward the development of the MDS model is the need to use product output/quality characteristics for machine deterioration-monitoring and decision-making processes.
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Christopher M. Williams and Patrick T. Hester
US Navy warships are capital-intensive national defense assets that require periodic depot and intermediate level maintenance availabilities (periods). Oftentimes, ship maintenance…
Abstract
US Navy warships are capital-intensive national defense assets that require periodic depot and intermediate level maintenance availabilities (periods). Oftentimes, ship maintenance is deferred or forgone altogether due to geopolitical strife or fiscal challenges. The impacts of missed maintenance are not only a burden on ships’ crews, but they also have a deleterious effect on current and future readiness. It is a difficult task to strike a balance between current and future readiness when insufficient resources are available to sustain a fleet of warships. This paper draws from multi-attribute utility theory (MAUT) to develop a ship maintenance decision-making model that considers attributes from the current and life cycle readiness cohorts. Using the current maintenance plans for two DDG 51-class ships entering availabilities in same fiscal year, this model determines which ship is more capable of absorbing a loss of maintenance and planned modernizations relative to the context of the decision environment. Five attributes are considered for the overall decision: mandatory maintenance, non-mandatory maintenance, mission impact from maintenance, mission impact from planned modernizations, and maintenance backlog. The model presented here is generalizable to a number of U.S. Navy ships and watercraft and can be used to inform decision-makers of the short- and long-term impacts of deferring critical maintenance.
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A.T. de Almeida and G.A. Bohoris
Advances in decision theory have allowed it to make an effectivecontribution to the modelling of the decision‐making process. Researchwork on maintenance decision making using…
Abstract
Advances in decision theory have allowed it to make an effective contribution to the modelling of the decision‐making process. Research work on maintenance decision making using decision theory, however, has received little emphasis to date. For this reason, very little has been done in utilizing two very important decision theory topics, namely utility theory and multi‐attribute utility theory. Investigates possible contributions from decision theory to the maintenance area and develops a framework to solve maintenance decision problems. This framework includes elicitation of both utility functions and prior probability distributions, optimization and sensitivity analysis modules. Details this framework and applies it to a real‐life maintenance problem.
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