Purpose The purpose of this paper is to identify maintenance improvement potentials using an overall equipment effectiveness (OEE) assessment within the manufacturing industry. Design/methodology/approach 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. Findings 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. Originality/value 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.

Purpose-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? Design/methodology/approach-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. Findings-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. Originality/value-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.

Industrial Product Service System (PSS) thinking can be applied to production system by considering it as a product. Prior studies show that strategic planning of the maintenance activities in manufacturing industries holds great potential to increase productivity. Planning of maintenance activities is therefore an integral decision making aspect for maintenance engineers and it is important to analyze how industries are currently working with planning of maintenance activities and what additional support is needed. This paper aims at mapping the current state of the work procedures for maintenance engineers and planners in the industry and analyzes the gap from current practices to the strategic planning which could increase productivity. The study specifically focuses on how industries work today with finding critical resource, performing criticality analysis, and planning maintenance. A descriptive research approach is followed, where empirical data is collected in Swedish industry through three different data collection methods. The results show the state-of-art industrial practices and the gaps in maintenance planning.

PurposeSeeks to present a methodology for working with bottle‐neck reduction by using a combination of automatic data collection and discrete‐event simulation (DES) for a manufacturing system.Design/methodology/approachIn the DES model, the bottle‐neck was identified by studying the simulation runs based on the collected automatic data from the different machines in the manufacturing system.FindingsA case study showed an improvement of the availability in one machine from 58.5 to 60.2 percent. This single alteration with a minimum of investment resulted in a 3 percent increase of the overall output in the manufacturing system consisting of 11 numerically controlled machines and six other stations. A new simulation run was performed one year after the first study in order to see how the improvement work has progressed with the suggested method. The method resulted in an increase of 6 percent in overall output.Originality/valueIt could be assumed that machines in future manufacturing systems will provide automatic data. The data can then be used for DES models when identifying bottle‐necks in a manufacturing system.

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