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While COVID-19 mitigation measures (CMMs) aided in steady recovery during the pandemic, they also impeded movement across economies/borders, affecting quality assurance (QA) of Cross-border Construction Logistics and Supply Chain (Cb-CLSC). However, prior studies on the pandemic in the construction project industry have not revealed how CMMs have impacted QA. Thus, this study aims to evaluate the impact of the CMMs on the QA of Cb-CLSC.

This is achieved by adopting an embedded mixed-method approach involving a desk literature review and engaging 150 experts from different economies across the globe using expert surveys, and results verified via semi-structured expert interviews. Structural equation modelling-based multiple regression analysis (SEM-MRA) was integrated to examine the impact of the CMMs on the QA, along with descriptive and content analysis.

The study confirmed that CMMs have not only impacted the QA negatively but also influenced the positioning of the QA for the post-pandemic era and probably to survive the risks of future pandemics. Among all the identified CMMs, the top three critical measures include “lockdown (CMM2)”, “use of personal protective equipment, such as nose masks, disinfects, etc. (CMM5)”, and “electronic/virtual meetings (CMM7)”. However, CMM5 possesses the highest contributory power to form CMM in impacting the QA, and this can be regarded as largely positive by strengthening health and safety management systems. Its negative impact lies with the project cost increment and the inconveniences of using nose and face masks.

This study provides a better understanding to construction practitioners and policy makers on how the pandemic policies, i.e. CMMs, have impacted QA and can aid in formulating planning and operational decisions to adequately position the QA for the post-pandemic era and to endure the risks of future pandemics.

The study contributes to knowledge in that it provides a better understanding of how the pandemic policies, such as CMMs, have impacted QA and can aid in formulating planning and operational decisions to adequately position the QA for the post-pandemic era and to endure the risks of future pandemics. This area of study has been given limited attention among prior studies during the pandemic.

The purpose of this paper is to identify details of technological, organizational and people (TOP) factors affecting maintenance technicians’ use of computerized maintenance management systems (CMMS) in manual collection of asset data.

In addition to TOP factor details, results from six case studies in Finland, India and the Caribbean are presented. Interviews and observations clarify the role of TOP factors in CMMS use in industrial maintenance.

In total, 17 detailed TOP factors are identified and criteria for analyzing CMMS contexts with them are defined. Analyzing the cases with these factors reveals that technicians who collect good quality data have received good training and instructions for the CMMS, are competent, and understand how manually collected data benefits them in their own work. However, even these sites struggle with the usability of the CMMS.

The 17 TOP factors and the criteria for CMMS evaluation extend understanding on context and usability in manual data collection. Case study method does not imply the relative importance of the TOP factors, which calls for future research using quantitative methods.

Management can use the criteria to analyze the context of manual data collection for improvements, e.g., in CMMS usability.

Insights from industrial environments and a new way of studying contextual factors of CMMS use are presented. The results extend a data quality research framework with details to manual data collection and define the TOP factors in CMMS context.

The purpose of this paper is to explore and study the aspects of usability related to eMaintenance solutions. The study aims to expand the domain of eMaintenance by increasing the usefulness of the computerized maintenance management systems (CMMS) through improved usability.

The paper opted for an exploratory study using interviews, one expert focus group discussion, and observations.

The paper provides insights on specific usability characteristics that can be adapted to eMaintenance solutions for industrial usage, e.g. aviation and process industry. The findings show that the current implementations of eMaintenance solutions in CMMS, in many cases, suffer from an insufficient level of usability. This has led to usability issues resulting in errors and mistakes. The result is a call for a more user-based focus, in which, the system needs to be easily understood, easily navigated, containing the necessary information to conduct maintenance tasks, tracking of the work conducted and who was involved, and the system needs to be compatible with other systems so that necessary information can be accessed via the CMMS.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The paper includes implications for the development of a CMMS, which could have positive effects for maintenance tasks.

This paper fulfills an identified need to study how CMMS actually fulfill the task they are designed to do.

To provide a structured methodology to permit an optimal selection of the best suited Computer Managed Maintenance System (CMMS) software within process industries.

The analysis has been executed adopting a multi‐attribute decision making methodology, namely the analytic hierarchic process (AHP) technique. A specific hierarchic structure has been defined considering 46 criteria outlined via questionnaires and interviews with administration, production and maintenance managers of several industries. To improve the effectiveness of the methodology, AHP has been coupled with a sound sensitivity analysis.

The application of the proposed approach allows the maintenance practitioners to concentrate on a limited subset of CMMS applications and to compare their actual capabilities in order to select the right one, rather than considering only their purchase cost.

The methodology enables decision makers to restrict the selection process to a limited number of software programmes that better suit the actual requirements of the corporation's personnel and to help the managers involved in the choice to better understand what each software can offer to them to effectively help the management of maintenance‐related activities. Finally, the choice is driven by objective considerations rather than by subjective opinions, and the purchase and the following implementation of the CMMS can be better justified to the corporation top‐level management

The paper proposes a robust approach, structured and useful in practice, for the selection of a CMMS software, that takes into account multiple, often conflicting, criteria and overcomes the intrinsic limitations of subjective decisions

The application of automated systems is rapidly increasing in different industries and organizations. In this regard, computerized maintenance management systems (CMMS) using information technology play an important role in the automating production systems. The purpose of this paper is to investigate the impacts of CMMSs and relevant supportive organizational factors on the effectiveness of total productive maintenance.

This study is classified as a quantitative survey-based research using structural equation modeling. The scope of the study includes manufacturing companies in Iran. A total of 125 questionnaires from 60 companies were collected from January to March 2014 to help validate the conceptual model and test the hypotheses.

The results support the concept CMMSs positively relates to relevant supportive organizational factors (resource allocation, decision-making structure, senior management support, employees’ involvement and effective instruction) on the effectiveness of total productive maintenance. The relevant supportive organizational factors can also be seen as the predictors of CMMSs.

This study integrates the CMMSs and relevant supportive organizational factors in a robust model to examine the effectiveness of total productive maintenance. This study also examines the impacts of CMMSs and relevant supportive organizational factors on total productive maintenance which seems to not be done previously.

This paper aims to describe the development of a tailorable framework of practices for maintenance delivery (MD) and present a range of examples to demonstrate the tailoring process. The framework covers the entire scope of MD in detail, including several related subjects where significant business process interaction occurs. It offers a wide range of optional practices throughout, complete with expert guidance to enable tailoring based on the business context.

The framework was developed in two stages: firstly, via a review of existing MD processes from the literature to establish a preliminary version; this was then developed further via a Delphi study utilising the opinion of experts from industry to validate, critique and improve the initial framework design.

The completed framework was implemented and tested by the industrial sponsor of this research and was found to deliver significant improvement to their MD practices.

The tailorable nature of the framework means that it can be utilised by any business to design an MD process that is fully effective within their specific context. Alongside a tailored MD process, the framework will also generate a fully aligned implementation specification for the supporting computerised maintenance management system (CMMS), which is also tailored according to the same contextual requirements. This will enable the end user of the framework to procure, implement and configure a CMMS that has all of the functionality required to fully support their business requirements.

Innovation is delivered by combining a novel business process design tool with a software specification tool to solve a common industry problem (i.e. poor CMMS implementation).

New maintenance hypotheses such as lean smart maintenance emphasized internal integration. Since the maintenance process is not fully integrated with other business processes, it indicates that some of the problems in the maintenance process are caused by other departments. Additionally, nothing can be managed or improved without first measuring it. In order to enhance internal integration, this study developed a model that makes use of information systems data to examine synchronization and collaboration across departments engaged in maintenance operations.

This research connects maintenance management and business process management through information systems. A conceptual module model based on CMMS is proposed that will use data which are already available in CMMS and, using process mining, will assess the level of synchronization between departments within an organization.

This conceptual model will serve as a roadmap for creating better value-added CMMS software. This system operates as a performance measurement tool in three majors, including organizational analysis, workflow analysis and eventually, a future simulation of maintenance processes. This module will serve as a decision support system, highlighting opportunities for improvement in maintenance processes.

A practical guideline is provided for the future development of CMMSs and their enhancement to intelligence. All assumptions are based on maintenance theories, techniques for measuring maintenance performance and business process management and process mining.

The purpose of this paper is to provide a structured methodology to permit the optimal selection of the best‐suited computerized maintenance management system (CMMS) software within maintenance information technologies.

The analysis has been executed adopting a multi‐attribute decision‐making methodology, namely the technique for order preference by similarity to an ideal solution (TOPSIS). For the selection process, 17 criteria under five main heading have been defined. Data obtained from questionnaires and interviews with the company's maintenance managers have been used in fuzzy TOPSIS.

The application of the proposed approach allows the maintenance practitioners to concentrate on a limited subset of CMMS applications and to compare their actual capabilities in order to select the right one, rather than considering only their purchase cost.

Comparisons with other multi‐attribute decision‐making techniques such as AHP (analytic hierarchy process) and ELECTRE (elimination and choice expressing reality) under fuzzy conditions can be done for further research.

This paper is a very useful source of information both for maintenance managers and stakeholders in making decisions about the selection of CMMS software.

This paper addresses CMMS software evaluation and selection criteria for practitioners and proposes a new multi‐attribute decision‐making methodology, hierarchical fuzzy TOPSIS, for the problem.

Details how Land‐Rover was one of the leading companies in the UK to adopt total quality management (TQM). Shows that in order to provide both effective and efficient maintenance in accordance with TQM needs, Land‐Rover (LR) reintroduced in 1994 total productive maintenance (TPM) in its manufacturing plant in Birmingham, UK. TQM is not possible without TPM so demonstrates how, in order to achieve its goals, TPM in LR is assisted by a computerized maintenance management system (CMMS). Describes in full the implementation steps of TPM, the difficulties encountered, and the usefulness and necessity of a computerized maintenance management system (CMMS) for the successful implementation of TPM in LR.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.