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Article

Muhammad Najib Razali, Ain Farhana Jamaluddin, Rohaya Abdul Jalil and Thi Kim Nguyen

This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.

Abstract

Purpose

This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.

Design/methodology/approach

This study uses several empirical analyses such as vector autoregression (VAR), vector error correction model (VECM), ARMA model and Granger causality to analyse predictive maintenance by using big data analytics concept.

Findings

The results indicate that there are strong correlations among these variables, which indicate reciprocal predictive maintenance of maintenance management job function. The findings also showed that there are significant needs of application of big data analytics for maintenance management in Putrajaya, Malaysia, to ensure the efficient maintenance of government buildings.

Originality/value

The conducted case study has demonstrated the empirical perspective which streamlines with the big data analytics' concept in maintenance, especially for analytics' support with appropriate empirical methodology

Details

Property Management, vol. 38 no. 4
Type: Research Article
ISSN: 0263-7472

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Article

Anna Gustafson, Håkan Schunnesson, Diego Galar and Uday Kumar

The purpose of this paper is to evaluate and analyse the production and maintenance performance of a manual and a semi‐automatic load haul dump (LHD) machine to find…

Abstract

Purpose

The purpose of this paper is to evaluate and analyse the production and maintenance performance of a manual and a semi‐automatic load haul dump (LHD) machine to find similarities and differences.

Design/methodology/approach

Real time process‐, operational‐ and maintenance data, from an underground mine in Sweden, have been refined and aggregated into KPIs in order to make the comparison between the LHDs.

Findings

The main finding is the demonstration of how production and maintenance data can be improved through information fusion, showing some unexpected results for maintenance of automatic and semi‐automatic LHDs in the mining industry. It was found that up to one third of the manually entered workshop data are not consistent with the automatically recorded production times. It is found that there are similarities in utilization and filling rate but differences in produced tonnes/machine hour between the two machines.

Originality/value

The originality in this paper is the information fusion between automatically produced production data and maintenance data which increases the accuracy of reliability analysis data. Combining the production indicator and the maintenance indicator gives a common tool to the production and maintenance departments. This paper shows the difference in both maintenance and production performance between a manual and semi‐automatic LHD.

Details

Journal of Quality in Maintenance Engineering, vol. 19 no. 1
Type: Research Article
ISSN: 1355-2511

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Article

Melinda Hodkiewicz and Mark Tien-Wei Ho

The purpose of this paper is to identify quality issues with using historical work order (WO) data from computerised maintenance management systems for reliability…

Abstract

Purpose

The purpose of this paper is to identify quality issues with using historical work order (WO) data from computerised maintenance management systems for reliability analysis; and develop an efficient and transparent process to correct these data quality issues to ensure data is fit for purpose in a timely manner.

Design/methodology/approach

This paper develops a rule-based approach to data cleansing and demonstrates the process on data for heavy mobile equipment from a number of organisations.

Findings

Although historical WO records frequently contain missing or incorrect functional location, failure mode, maintenance action and WO status fields the authors demonstrate it is possible to make these records fit for purpose by using data in the freeform text fields; an understanding of the maintenance tactics and practices at the operation; and knowledge of where the asset is in its life cycle. The authors demonstrate that it is possible to have a repeatable and transparent process to deal with the data cleaning activities.

Originality/value

How engineers deal with raw maintenance data and the decisions they make in order to produce a data set for reliability analysis is seldom discussed in detail. Assumptions and actions are often left undocumented. This paper describes typical data cleaning decisions we all have to make as a routine part of the analysis and presents a process to support the data cleaning decisions in a repeatable and transparent fashion.

Details

Journal of Quality in Maintenance Engineering, vol. 22 no. 2
Type: Research Article
ISSN: 1355-2511

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Article

Arian Razmi-Farooji, Hanna Kropsu-Vehkaperä, Janne Härkönen and Harri Haapasalo

The purpose of this paper is twofold: first, to understand data management challenges in e-maintenance systems from a holistically viewpoint through summarizing the…

Abstract

Purpose

The purpose of this paper is twofold: first, to understand data management challenges in e-maintenance systems from a holistically viewpoint through summarizing the earlier scattered research in the field, and second, to present a conceptual approach for addressing these challenges in practice.

Design/methodology/approach

The study is realized as a combination of a literature review and by the means of analyzing the practices on an industry leader in manufacturing and maintenance services.

Findings

This research provides a general understanding over data management challenges in e-maintenance and summarizes their associated proposed solutions. In addition, this paper lists and exemplifies different types and sources of data which can be collected in e-maintenance, across different organizational levels. Analyzing the data management practices of an e-maintenance industry leader provides a conceptual approach to address identified challenges in practice.

Research limitations/implications

Since this paper is based on studying the practices of a single company, it might be limited to generalize the results. Future research topics can focus on each of mentioned data management challenges and also validate the applicability of presented model in other companies and industries.

Practical implications

Understanding the e-maintenance-related challenges helps maintenance managers and other involved stakeholders in e-maintenance systems to better solve the challenges.

Originality/value

The so-far literature on e-maintenance has been studied with narrow focus to data and data management in e-maintenance appears as one of the less studied topics in the literature. This research paper contributes to e-maintenance by highlighting the deficiencies of the discussion surrounding the perspectives of data management in e-maintenance by studying all common data management challenges and listing different types of data which need to be acquired in e-maintenance systems.

Details

Journal of Quality in Maintenance Engineering, vol. 25 no. 3
Type: Research Article
ISSN: 1355-2511

Keywords

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Article

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…

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.

Details

Journal of Manufacturing Technology Management, vol. 32 no. 9
Type: Research Article
ISSN: 1741-038X

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Article

James Mutuota Wakiru, Liliane Pintelon, Peter Muchiri and Peter Chemweno

The present study empirically compares maintenance practices under asset performance management (APM), employed by firms in developed and developing countries (Belgium and…

Abstract

Purpose

The present study empirically compares maintenance practices under asset performance management (APM), employed by firms in developed and developing countries (Belgium and Kenya, respectively).

Design/methodology/approach

Empirical observations and theoretical interpretations on maintenance practices under APM are delineated. A comparative cross-sectional survey study is conducted through an online questionnaire with 151 respondents (101 Kenya, 50 Belgium). Descriptive statistics and inferential statistics like independent t-test and phi coefficient were used for analyzing the data.

Findings

In both countries, reduction of maintenance and operational budget, return on assets, asset ageing and compliance aspects were established as critical factors influencing the implementation of asset maintenance and performance management (AMPM). A significant difference in staff competence in managing vibration, ultrasound and others like predictive algorithms was found to exist between the firms of the two countries. The majority of firms across the divide utilize manual and computer-based tools to integrate and analyse various maintenance data sets, while standardization and maintenance knowledge loss were found to adversely affect maintenance data management.

Research limitations/implications

The study findings are based on the limited number of returned responses of the survey questionnaire and focused on only two countries representing developed and developing economies. This study not only provides practitioners with the practical guidelines for benchmarking, but also induces the need to improve the asset maintenance strategies and data application practices for asset performance management.

Practical implications

The paper provides insights to researchers and practitioners in the articulation of imperative effective maintenance strategies, benchmarking and challenges in their implementation, considering the different operational context.

Originality/value

The paper contributes to theory and practice within the field of AMPM where no empirical research comparing developed and developing countries exist.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

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Article

Salla Marttonen-Arola, David Baglee, Antti Ylä-Kujala, Tiina Sinkkonen and Timo Kärri

Big data and related technologies are expected to drastically change the way industrial maintenance is managed. However, at the moment, many companies are collecting large…

Abstract

Purpose

Big data and related technologies are expected to drastically change the way industrial maintenance is managed. However, at the moment, many companies are collecting large amounts of data without knowing how to systematically exploit it. It is therefore important to find new ways of evaluating and quantifying the value of data. This paper addresses the value of data-based profitability of maintenance investments.

Design/methodology/approach

An analytical wasted value of data model (WVD-model) is presented to quantify how the value of data can be increased through eliminating waste. The use of the model is demonstrated with a case example of a maintenance investment appraisal of an automotive parts manufacturer.

Findings

The presented model contributes to the gap between the academic research and the solutions implemented in practice in the area of value optimization. The model provides a systematic way of evaluating if the benefits of investing in maintenance data exceed the additional costs incurred. Applying the model to a case study revealed that even though the case company would need to spend more time in analyzing and processing the increased data, the investment would be profitable if even a modest share of the current asset failures could be prevented through improved data analysis.

Originality/value

The model is designed and developed on the principle of eliminating waste to increase value, which has not been previously extensively discussed in the context of data management.

Details

Journal of Quality in Maintenance Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2511

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Article

Daniel Bumblauskas, William Meeker and Douglas Gemmill

The purpose of this paper is to review cotemporary maintenance programs and analyze factory production data for an SF6 gas filled circuit breaker population. Various…

Abstract

Purpose

The purpose of this paper is to review cotemporary maintenance programs and analyze factory production data for an SF6 gas filled circuit breaker population. Various maintenance techniques and studies are reviewed to understand the reliability of circuit breaker models and the impact manufacturing can have on long term maintenance considerations.

Design/methodology/approach

Production and field event data were analyzed using statistical analysis tools. The population data were formatted so that a recurrent event analysis could be conducted to establish the mean cumulative function (MCF) by model and product family (class). Average Field Two‐year Recorded Event Rate (AFTRER) is introduced and compared to commonly used Field Incident Rate (FIR) and Mean‐Time between Failure (MTBF) measures.

Findings

Common managerial operating questions can be answered as exhibited for the provided circuit breaker population. This includes the longevity of field issues, the anticipated life cycle of a model or class, and AFTRER for models or classes of interest. These statistical analysis tools are used to make critical production quality and asset management observations and aid in decision‐making.

Research limitations/implications

Due to limitations in existing database systems, the cost of events and explanatory variables related to event rates were not included in the analyses. There remains much work to be done in terms of the installation and retro‐fitting of breakers with conditions monitors in the field.

Practical implications

A framework to analyze maintenance data from fleet of similar assets using recurrent event data analysis is provided. The methods illustrated here would be useful for quality and asset managers to make operating decisions. This includes resource allocation decisions across a network of equipment.

Social implications

Data analyzed are for power circuit breakers which are a critical element in the operation and reliability of the US power grid.

Originality/value

Using recurrent event data analysis to review and develop solutions to production quality and asset management problems including a comparison of AFTRER to FIR and MTBF measures.

Details

International Journal of Quality & Reliability Management, vol. 29 no. 5
Type: Research Article
ISSN: 0265-671X

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Article

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…

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.

Details

Journal of Quality in Maintenance Engineering, vol. 20 no. 3
Type: Research Article
ISSN: 1355-2511

Keywords

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Article

Basim Al‐Najjar and Mirka Kans

Purpose – The purpose of this paper is to help build up a relevant database for mapping technical and financial effectiveness of production in order to make cost‐effective…

Abstract

Purpose – The purpose of this paper is to help build up a relevant database for mapping technical and financial effectiveness of production in order to make cost‐effective maintenance decisions. Design/methodology/approach – A theoretical model is developed based on past research and experience adopting a holistic systems approach on the production. A case study, which includes databases of two maintenance‐used software programs, verifies the potential of applying the model. Findings – The main result achieved is a model for identifying relevant data required for accurate problem tracing and localisation within maintenance and production processes using a top down approach. The main conclusions are integration of IT and data resources within the enterprise is needed for developing a holistic view of the production process and a well‐formulated and documented procedure of data identification will ensure that the data can be traced back to root sources and in this way we can support the work of continuous cost‐effective improvement by eliminating root causes of problems at an early stage. Research limitations/implications – Further model verification by industrial case studies would be of interest. Practical implications – The holistic approach and the model presented are applicable especially in capital intensive industries, where maintenance budget is not negligible and the amount of data to process is large. By structuring the data need and data identification process relevant performance measures will be monitored and advanced maintenance concepts can be applied. Originality/value – By applying the proposed model in industry, the data identification process itself and not the data contents is necessary to be standardised and structured. It shifts the focus of the quality aspect from just data level to both data and data collection level. The performance measures will therefore not be chosen depending on what the IT applications can provide in first hand, but upon what is needed for cost‐effective mapping, analysis, following up and assessment of maintenance performance.

Details

International Journal of Productivity and Performance Management, vol. 55 no. 8
Type: Research Article
ISSN: 1741-0401

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