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Ontology development plays a vital role as it provides a structured way to represent and organize knowledge. It has the potential to connect and integrate data from different sources, enabling a new class of AI-based services and systems such as decision support systems and recommender systems. However, in large manufacturing industries, the development of such ontology can be challenging. This paper presents a use case of an application ontology development based on machine breakdown work orders coming from a Computerized Maintenance Management System (CMMS). Here, the ontology is developed using a Knowledge Meta Process: Methodology for Ontology-based Knowledge Management. This ontology development methodology involves steps such as feasibility study, requirement specification, identifying relevant concepts and relationships, selecting appropriate ontology languages and tools, and evaluating the resulting ontology. Additionally, this ontology is developed using an iterative process and in close collaboration with domain experts, which can help to ensure that the resulting ontology is accurate, complete, and useful for the intended application. The developed ontology can be shared and reused across different AI systems within the organization, facilitating interoperability and collaboration between them. Overall, having a well-defined ontology is critical for enabling AI systems to effectively process and understand information.

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.

Manufacturing maintenance has always undergone change and development. With Industry 4.0-related technological development, increasingly more complex machining equipment, and an increased focus on sustainability, maybe more so today than ever. This has led to an increased difficulty in finding competent maintenance employees to recruit. Simultaneously, it increases the need for continuous competence development to retain the existing work force up to date with the challenges of future development. The introduction of these new technologies and demands does not reduce the need of competence in basic maintenance skills though, but rather adds new areas of needed competence, making the maintenance profession increasingly more complex. This paper will, through an interview study of maintenance managers in an international manufacturing company located in nine countries, delve into the issues and present both challenges and enablers in how to work with recruitment and competence development within maintenance.

Companies must enhance total maintenance effectiveness to stay competitive, focusing on both digitalization and basic maintenance procedures. Digitalization offers technologies for data-driven decision-making, but many maintenance decisions still lack a factual basis. Prioritizing efficiency and effectiveness require analyzing equipment history, facilitated by using Computerized Maintenance Management Systems (CMMS). However, CMMS data often contains unstructured free-text, leading to manual analysis, which is resource-intensive and reactive, focusing on short time periods and specific equipment. Two approaches are available to solve the issue: minimizing free-text entries or using advanced methods for processing them. Free-text allows detailed descriptions but may lack completeness, while structured reporting aids automated analysis but may limit fault description richness. As knowledge and experience are vital assets for companies this research uses a hybrid approach by combining Natural Language Processing with domain specific ontology and Large Language Models to extract information from free-text entries, enabling the possibility of real-time analysis e.g., identifying recurring failure and knowledge sharing across global sites.

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.

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.

Purpose The purpose of the study is to test if it, by the use of a survey methodology, is possible to measure managers' awareness on, and specifically if there exist preconceived beliefs on, overall equipment effectiveness (OEE) results. The paper presents the design of the survey methodology as well as a test of the survey in one case company. Design/methodology/approach Actual OEE logs from a case company are collected and a survey on the data is designed and managers at the same case company are asked to answer the survey. The survey results are followed-up by an interview study in order to get deeper insights to both the results of the survey as well as the OEE strategy at the case company. Findings The findings show that the managers at this particular case company, on a general level, does not suffer too much from preconceived beliefs. However, it is clear that the managers have a preconceived belief that lack of material is logged as a loss much more often than what it actually is. Research limitations/implications The test has only been performed with data from one case company within the automotive manufacturing industry and only the managers at that case company has been active in the test. Practical implications The survey methodology can be replicated and used by other companies to find out how aware their employees are on their OEE results and if possible preconceived beliefs exists. Originality/value To the authors' knowledge, this is the first attempt at measuring if preconceived beliefs on OEE results exist.

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.