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Purpose The maintenance function is crucial for maintaining competitiveness, safety and environmental responsibility. As demands for quality and production efficiency increase, optimized maintenance becomes more essential. Industry 4.0 and 5.0 introduce new generations of maintenance, highlighting technical and human-centered approaches. However, manufacturing companies still face many challenges in implementation and use. Prior research lacks studies that support the manufacturing industry and have not been practically connected to it. This research explores the implementation and use of smart maintenance technologies in large Swedish manufacturing companies, offering practical recommendations for industry practitioners and contributing to the field of smart maintenance research.Design/methodology/approach The research is based on 12 semi-structured interviews with respondents from 11 large manufacturing companies representing varying levels of experience and maturity in smart maintenance technologies. The empirical data were analyzed qualitatively to identify themes associated with such technologies.Findings This research identifies and describes three themes associated with smart maintenance technologies in large manufacturing companies: organizational, human and technical. These themes do not outline an implementation process, but rather synthesize the practical experiences of participating companies, increasing the understanding of how smart maintenance technologies are implemented and used in industrial practice.Originality/value This research highlights developments in maintenance for both practitioners and researchers. It compiles insights from participating companies using smart maintenance technologies to improve understanding of their practical application in industry.

Research within smart maintenance has become a popular research topic largely focused on how the nine technologies of Industry 4.0, such as Industrial Internet of Things (IIoT) and Augmented Reality (AR), as well as Artificial Intelligence (AI) and Cyber Physical System (CPS), can be used for, e.g., condition monitoring of equipment, remote services, modelling wear of components, calculating Remaining Useful Life (RUL) and prediction of failure. Due to the new generation of maintenance, new skills are required regarding the interaction between humans and technologies. Human-technology interaction in smart maintenance research is not highlighted in a structured way and according to any type of socio-technical system. The aim of the paper is to study the challenges and their associated theoretically grounded enablers regarding the implementation of and use of smart maintenance technologies, through the interplay between humans, technologies and organizations. The paper is based on empirical data from seven large manufacturing companies in Sweden as well as a literature review.

Industry 4.0 is usually presented as usage of technologies. Some of these play an important role in the development of smart maintenance technologies. However, although the subject of smart maintenance has been discussed for more than 10 years, the manufacturing industry still finds it challenging to implement smart maintenance technologies to add benefits to maintenance organizations in line with company’s goals. This study presents a conceptual process for implementing smart maintenance technologies, challenges and enablers to consider when implementing, and benefits. This article is based on an analysis of empirical findings from seven large manufacturing companies in Sweden, previous maintenance research, and authors’ three previous smart maintenance research articles. In the first article, the authors explored perspectives on smart maintenance technologies from 11 large companies within the manufacturing industry, while in the second one, perspectives on smart maintenance technologies from 15 manufacturing Small and medium-sized enterprises (SMEs) were presented. In the third and final one, the authors developed and presented a testbed for smart maintenance technologies.

Industry 4.0 presents nine technologies including Industrial Internet of Things (IIoT), Big Data and Analytics, Augmented Reality (AR), etc. Some of the technologies play an important role in the development of smart maintenance technologies. Previous research presents several technologies for smart maintenance. However, one problem is that the manufacturing industry still finds it challenging to implement smart maintenance technologies in a value-adding way. Open questionnaires and interviews have been used to collect information about the current needs of the manufacturing industry. Both the empirical findings of this paper, as well as previous research, show that knowledge is the most common challenge when implementing new technologies. Therefore, in this paper, we develop and present a testbed for how to approach smart maintenance technologies and to share technical knowledge to the manufacturing industry.

The digitalization of manufacturing industry, known as e.g., Industry 4.0 or smart production, has opened new opportunities for real-time optimization of production systems. Also, this technological leap has provided new possibilities for the maintenance of production equipment to become data driven and in many cases predictive. This fourth industrial revolution is changing the role of humans at the shop floor. Visions of the dark factory arises, meaning fully automated factories where humans are redundant, both for physical processing and for decision making. The research on Smart maintenance shows great advances in predictive diagnostics and prognostic techniques. However, in manufacturing industry, studies have shown that up to 50–60% of equipment breakdowns are due to human errors. Some of these errors are partly addressed through the development of improved information aid, such as e.g., instructions through Augmented Reality and training in Virtual Reality. Still, the root cause of human errors in manufacturing industry haven’t been properly categorized in terms of e.g., neglect, lack of competence, unclear processes, or poor leadership. In this paper the potential of data driven maintenance is discussed from a human centric perspective. Considering the large part of failures being due to human factors and the possibilities of improvement through implementation of smart technologies, this paper argues for exploring the root causes of human errors in discrete item manufacturing systems and address the proper human centric technologies as a means of reducing these failures.

The advanced technologies available in the development of Smart Maintenance within Industry 4.0 have the potential to significantly improve the efficiency of industrial maintenance. However, it is important to be careful when deciding which technologies to implement for a given application and when evaluating the quality of the data generated. Otherwise, what should be cost-effective solutions may end up being cost-driving. The use of domain knowledge in selecting, developing, implementing, setting up, and utilizing these technologies is increasingly important for achieving success. In this paper, we will elaborate on this topic by presenting and analyzing insights from industrial cases, drawing on the authors’ extensive experience in the field.

Industry 4.0 consists of nine technological pillars: IIoT, Cloud Computing, Big Data and Analytics, AR, etc. Some of the pillars play an essential role in maintenance development. Previous research presents many technologies for smart maintenance, but one prevailing problem is that there are still challenges to implementing smart maintenance technologies cost-effectively in the manufacturing industry. Therefore, we explore perspectives on smart maintenance technologies from respondents within 15 manufacturing SMEs. We start by investigating whether the companies had implemented smart maintenance technologies, if so, in what context. Then, we explore perspectives from the manufacturing SMEs on added values, challenges, opportunities, advantages, and disadvantages of smart maintenance technologies. However, as none of the case companies had implemented any Smart Maintenance Technologies, only implementation challenges could be investigated.

The ongoing transformation of manufacturing industry into digitalized production, Industry 4.0, has put new perspectives on the maintenance of production systems. The technologies offer an array of new possibilities in optimization of maintenance and data driven decision making. On the other hand, these new technologies offer a lot of challenges in form of investment costs, need for new competences, and how to handle the equipment legacy, i.e. upgrading old equipment. Many researchers associate data driven decision making with intelligent sensors, cloud computing and cyber physical systems, but are these technologies the most cost-effective way of achieving data driven maintenance? The aim of this paper is to discuss how manufacturing industry should approach smart maintenance in order to improve the industry’s competitiveness, rather than spending money on technology that doesn’t contribute. The basis for the discussion will mainly be a literature study but additional empirical data may be included.

Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.

The manufacturing industry faces significant technical challenges due to the industry 4.0 technologies, which play an essential role in maintenance development. Maintenance in industry 4.0, also named smart maintenance, maintenance 4.0, predictive maintenance, etc., is boosted using industry 4.0 technologies, such as Industrial Internet of Things (IIoT), Big Data and Analytics, Cloud Computing, Augmented Reality (AR), Additive Manufacturing (AM), etc. Previous research presents several smart maintenance technologies, but the manufacturing industry still finds it challenging to implement the technologies cost-effectively. One problem is that there is insufficient research on how smart maintenance technologies can be implemented cost-effectively and add value to the manufacturing industry. Therefore, this paper aims to explore perspectives on smart maintenance technologies: 1) if there are any implemented smart maintenance technologies, 2) in what context, 3) added values, 4) challenges, 5) opportunities, 6) advantages, and 7) disadvantages with the technologies. This paper presents the results of a case study based on an online open questionnaire with respondents working in maintenance organizations in large manufacturing companies.