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Technological advancements are reshaping the manufacturing industry toward digitalized manufacturing. Despite the importance of top-class maintenance in such systems, many industrial companies lack a clear strategy for maintenance in digitalized manufacturing. The purpose of this paper is to facilitate the implementation of maintenance in digitalized manufacturing by proposing a strategy development process for the Smart Maintenance concept.

This study is designed as a multiple-case study, where the strategy development in three industrial cases is analyzed. Several methods were used to collect data on the case companies' development of smart maintenance strategies. The data were analyzed with an inductive approach.

A process of strategy development for smart maintenance is proposed, including six steps: benchmarking, setting clear goals, setting strategic priority, planning key activities, elevating implementation and follow-up.

The proposed process provides industry practitioners with a step-by-step guide for the development of a clear smart maintenance strategy, based on the current state of their maintenance organization. This creates employee engagement and is a new way of developing maintenance strategies.

Maintenance strategies are traditionally regarded as a selection of corrective/reactive and preventive maintenance actions using a top-down approach. By contrast, the proposed process is starting from the current state of the maintenance organization and allows a mixture of top-down and bottom-up approaches, supporting organizational development. This is a rare perspective of maintenance strategies and will make maintenance organizations ready for the demands of digitalized manufacturing.

The purpose of this study is to ensure productive, robust and sustainable production systems and realise digitalised manufacturing trough implementation of Smart Maintenance – “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies”. This paper aims to support industry practitioners in selecting performance indicators (PIs) to measure the effects of Smart Maintenance, and thus facilitate its implementation.

Intercoder reliability and negotiated agreement were used to analyse 170 maintenance PIs. The PIs were structurally categorised according to the anticipated effects of Smart Maintenance.

Companies need to revise their set of PIs when changing manufacturing and/or maintenance strategy (e.g. reshape the maintenance organisation towards Smart Maintenance). This paper suggests 13 categories of PIs to facilitate the selection of PIs for Smart Maintenance. The categories are based on 170 PIs, which were analysed according to the anticipated effects of Smart Maintenance.

The 13 suggested categories bring clarity to the measuring potential of the PIs and their relation to the Smart Maintenance concept. Thereby, this paper serves as a guide for industry practitioners to select PIs for measuring the effects of Smart Maintenance.

This is the first study evaluating how maintenance PIs measure the anticipated effects of maintenance in digitalised manufacturing. The methods intercoder reliability and negotiated agreement were used to ensure the trustworthiness of the categorisation of PIs. Such methods are rare in maintenance research.

Scholars and practitioners within industrial maintenance management are focused on understanding antecedents, correlates and consequences of the concept of “Smart Maintenance,” which consists of the four dimensions, namely, data-driven decision-making, human capital resource, internal integration and external integration. In order to facilitate this understanding, valid and reliable empirical measures need to be developed. Therefore, this paper aims to develop a psychometric instrument that measures the four dimensions of Smart Maintenance.

The results from two sequential empirical studies are presented, which include generating items to represent the constructs, assessment of content validity, as well as an empirical pilot test. With input from 50 industrial experts, a pool of 80 items that represent the constructs are generated. Thereafter, using data from 42 industrial and academic raters, the content validity of all items is assessed quantitatively. Finally, using data from 59 manufacturing plants, the dimensionality and factor structure of the instrument are tested.

The authors demonstrate content validity and provide evidence of good model fit and psychometric properties for one-factor models with 8–11 items for each of the four constructs, as well as a combined 24-item four-factor model.

The authors provide recommendations for scholarly use of the instrument in further theory-testing research, as well as its practical use to assess, benchmark and longitudinally evaluate Smart Maintenance within the manufacturing industry.

This paper presents a concept for digitalised maintenance (DM), maps the conceptualised DM to maintenance problems in industries and highlights challenges that might be faced when realizing this concept.

First, maintenance problems that are faced by the industry are presented, followed by a conceptualisation of DM. Next, a typical operational scenario is used as an exemplification to show system dynamics. The characteristics of this conceptualised DM are then mapped to the identified maintenance problems of industry. Then, interesting initiatives in this domain are highlighted, and finally, the challenges to realize this approach are discussed.

This paper identified a set of problems related to maintenance in industry. In order to solve current industrial problems, exploit emerging digital technologies and elevate future industries, it will be necessary to develop new maintenance approaches. The mapping between the criteria of DM and maintenance problems shows the potential of this concept and gives a reason to examine it empirically in future work.

This paper aims to help maintenance professionals from both academia and industry to understand and reflect on the problems related to maintenance, as well as to comprehend the requirements of a digitalised maintenance and challenges that may arise.

The purpose of this study is to ensure productive, robust and sustainable production systems by enabling future investments in maintenance. This study aims to provide a deeper understanding of the investment process and thereby facilitate future maintenance-related investments. The objectives are to describe the investment process, map the decision support and roles involved and identify factors influencing the process.

The study was designed as a multiple-case study, with three industrial cases of maintenance-related investments. A structured coding procedure was used to analyse the empirical data from the cases.

This paper provides a deeper understanding of the process of maintenance-related investments. Eleven factors influencing the investment process could be identified, three of which were seen in all three cases. These three factors are: fact-based decision-support, internal integration and foresight.

Investments in modern maintenance are needed to ensure productive, robust and sustainable production in the future. However, it is a challenge in manufacturing industry to justify maintenance-related investments. This challenge may be solved by developing a decision-support system, or a structured work procedure, that considers the findings of this study.

From this study, an extended view of the relation between quantifying effects of maintenance and maintenance-related investment is proposed, including surrounding factors influencing the investment process. The factors were identified using a structured and transparent coding procedure which is rarely used in maintenance research.

Resistance is expected to emerge with the implementation and use of new technologies in production systems. This work focuses on identifying sources of resistance to the use of Industry 4.0 technologies when managing production disturbances and suitable managerial approaches to deal with them.

A qualitative approach was chosen in this research. The authors conducted a literature review and a series of interviews. Thirty-one papers from the literature review were analysed, and 16 people from five different companies were interviewed.

The authors identified five different sources of resistance and three managerial approaches to dealing with them. The sources of resistance were based on (1) feelings of over-supervision, (2) unclear values, (3) feelings of inadequacy, (4) concerns about loss of power and jobs and (5) work overload. The three approaches to dealing with resistance are (1) communication, (2) participation and (3) training.

This work identifies the sources and strategies to deal with resistance to the use of Industry 4.0 technologies in the management of production disturbances. The managerial literature in this area is limited, and to the authors's knowledge, the specific sources for resistance and strategies to deal with that in this topic have not been systematically investigated before.

The purpose of this paper is to identify maintenance improvement potentials using an overall equipment effectiveness (OEE) assessment within the manufacturing industry.

The paper assesses empirical OEE data gathered from 98 Swedish companies between 2006 and 2012. Further analysis using Monte-Carlo simulations were performed in order to study how each OEE component impacts the overall OEE.

The paper quantifies the various equipment losses in OEE, as well as the factors availability, utilization, speed, quality, and planned stop time. From the empirical findings, operational efficiency losses are found to have the largest impact on OEE followed by availability losses. Based on the results, improvement potentials and future trends for maintenance are identified, including a systems view and an extended scope of maintenance.

The paper provides detailed insights about the state of equipment effectiveness in terms of OEE in the manufacturing industry. Further, the results show how individual OEE components impact overall productivity and efficiency of the production system. This paper contributes with the identification of improvement potentials that are necessary for both practitioners and academics to understand the new direction in which maintenance needs to move. The authors argue for a service-oriented organization.

A common understanding of what events to regard as production disturbances (PD) are essential for effective handling of PDs. Therefore, the purpose of this paper is to answer the two questions: how are individuals with production or maintenance management positions in industry classifying different PD factors? Which factors are being measured and registered as PDs in the companies monitoring systems?

A longitudinal approach using a repeated cross-sectional survey design was adopted. Empirical data were collected from 80 companies in 2001 using a paper-based questionnaire, and from 71 companies in 2014 using a web-based questionnaire.

A diverging view of 21 proposed PD factors is found between respondents in manufacturing industry, and there is also a lack of correspondence with existing literature. In particular, planned events are not classified and registered to the same extent as downtime losses. Moreover, the respondents are often prone to classify factors as PDs compared to what is actually registered. This diverging view has been consistent for over a decade, and hinders companies to develop systematic and effective strategies for handling of PDs.

There has been no in-depth investigation, especially not from a longitudinal perspective, of the personal interpretation of PDs from people who play a central role in achieving high reliability of production systems.

Manufacturing companies struggle to manage production disturbances. One step of such management deals with prioritising those disturbances which should undergo root cause analysis. The focus of this work is on two areas. First, investigating current challenges faced by manufacturing companies when prioritising root cause analysis of production disturbances. Second, identifying the stakeholders and factors impacted by production disturbances. Understanding the current challenges and identifying impacted stakeholders and factors allows the development of more efficient prioritisation strategies and, thus, contributes to the reduction of frequency and impact of disturbances.

To achieve the intended purpose of this research, a qualitative approach was chosen. A series of interviews was conducted with practitioners, to identify current challenges. A series of focus groups was also held, to identify the impacted stakeholders and factors by disturbances.

Various challenges were identified. These are faced by manufacturing companies in their prioritisation of production disturbances and relate to the time needed, criteria used, centralisation of the process, perspective considered and data support. It was also found that a wide range of stakeholders is impacted by production disturbances, surpassing the limits of production and maintenance departments. Furthermore, the most critical factors impacted are quality, work environment, safety, time, company results, customer satisfaction, productivity, deliverability, resource utilisation, profit, process flow, plannability, machine health and reputation.

The current situation regarding root cause analysis prioritisation has not been identified in previous works. Moreover, there has been no prior systematic identification of the various stakeholders and factors impacted by production disturbances.