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Article
Publication date: 14 December 2023

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.

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 30 January 2024

Mahnaz Ensafi, Walid Thabet and Deniz Besiktepe

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a…

Abstract

Purpose

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

Design/methodology/approach

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

Findings

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Originality/value

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

Details

Facilities , vol. 42 no. 5/6
Type: Research Article
ISSN: 0263-2772

Keywords

Open Access
Article
Publication date: 31 July 2023

Jingrui Ge, Kristoffer Vandrup Sigsgaard, Bjørn Sørskot Andersen, Niels Henrik Mortensen, Julie Krogh Agergaard and Kasper Barslund Hansen

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups…

Abstract

Purpose

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

Design/methodology/approach

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

Findings

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Originality/value

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

Details

International Journal of Quality & Reliability Management, vol. 41 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 23 January 2024

Chinedu Onyeme and Kapila Liyanage

This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on…

60

Abstract

Purpose

This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.

Design/methodology/approach

The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.

Findings

The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.

Originality/value

The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).

Details

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

Keywords

Article
Publication date: 21 March 2024

Nanda Kumar Karippur, Pushpa Rani Balaramachandran and Elvin John

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the…

Abstract

Purpose

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.

Design/methodology/approach

The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.

Findings

This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.

Practical implications

This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.

Originality/value

This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

Details

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

Keywords

Open Access
Article
Publication date: 17 November 2023

Peiman Tavakoli, Ibrahim Yitmen, Habib Sadri and Afshin Taheri

The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin…

Abstract

Purpose

The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.

Design/methodology/approach

Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.

Findings

Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.

Practical implications

The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.

Originality/value

The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

Details

Smart and Sustainable Built Environment, vol. 13 no. 1
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 29 September 2023

Beatriz Campos Fialho, Ricardo Codinhoto and Márcio Minto Fabricio

Facilities management (FM) plays a key role in the performance of businesses to ensure the comfort of users and the sustainable use of natural resources over operation and…

Abstract

Purpose

Facilities management (FM) plays a key role in the performance of businesses to ensure the comfort of users and the sustainable use of natural resources over operation and maintenance. Nevertheless, reactive maintenance (RM) services are characterised by delays, waste and difficulties in prioritising services and identifying the root causes of failures; this is mostly caused by inefficient asset information and communication management. While linking building information modelling and the Internet of Things through a digital twin has demonstrated potential for improving FM practices, there is a lack of evidence regarding the process requirements involved in their implementation. This paper aims to address this challenge, as it is the first to statistically characterise RM services and processes to identify the most critical RM problems and scenarios for digital twin implementation. The statistical data analytics approach also constitutes a novel practical approach for a holistic analysis of RM occurrences.

Design/methodology/approach

The research strategy was based on multiple case studies, which adopted university campuses as objects for investigation. A detailed literature review of work to date and documental analysis assisted in generating data on the FM sector and RM services, where qualitative and statistical analyses were applied to approximately 300,000 individual work requests.

Findings

The work provides substantial evidence of a series of patterns across both cases that were not evidenced prior to this study: a concentration of requests within main campuses; a balanced distribution of requests per building, mechanical and electrical service categories; a predominance of low priority level services; a low rate of compliance in attending priority services; a cumulative impact on the overall picture of five problem subcategories (i.e. Building-Door, Mechanical-Plumbing, Electrical-Lighting, Mechanical-Heat/Cool/Ventilation and Electrical-Power); a predominance of problems in student accommodation facilities, circulations and offices; and a concentration of requests related to unlisted buildings. These new patterns form the basis for business cases where maintenance services and FM sectors can benefit from digital twins. It also provides a new methodological approach for assessing the impact of RM on businesses.

Practical implications

The findings provide new insights for owners and FM staff in determining the criticality of RM services, justifying investments and planning the digital transformation of services for a smarter provision.

Originality/value

This study represents a unique approach to FM and provides detailed evidence to identify novel RM patterns of critical service provision and activities within organisations for efficient digitalised data management over a building’s lifecycle.

Details

Facilities, vol. 42 no. 3/4
Type: Research Article
ISSN: 0263-2772

Keywords

Article
Publication date: 20 March 2024

Gang Yu, Zhiqiang Li, Ruochen Zeng, Yucong Jin, Min Hu and Vijayan Sugumaran

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due…

25

Abstract

Purpose

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Design/methodology/approach

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

Findings

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

Originality/value

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 10 October 2023

Sou-Sen Leu, Yen-Lin Fu and Pei-Lin Wu

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect…

Abstract

Purpose

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

Design/methodology/approach

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

Findings

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

Originality/value

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

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 22 September 2023

Mulatu Tilahun Gelaw, Daniel Kitaw Azene and Eshetie Berhan

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

Abstract

Purpose

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

Design/methodology/approach

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

Findings

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

Research limitations/implications

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

Practical implications

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

Originality/value

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

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

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