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.

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity.

Purpose The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity. Design/methodology/approach An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety. Findings The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization. Originality/value Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A challenge for these companies is identifying throughput bottlenecks using the realtime machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences.

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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.

Factories world-wide do not utilize their existing capacity to a satisfactory level. Several studies indicate an average Overall Equipment Efficiency (OEE) of around 55% in manufacturing industry. One major reason is machine downtime leading to substantial system losses culminating in production plans with unsatisfactory robustness. This paper discusses an approach to integrate maintenance strategies into a production planning approach using discrete event simulation. The aim is to investigate how and where in the planning process maintenance strategies can be integrated and how different maintenance strategies influence production performance and the overall robustness of production plans. The approach is exemplified in an automotive case study, integrating strategies for reactive maintenance in a simulation model to support decision making on how repair orders should be prioritized to increase production performance. The results show that introducing priority-based planning of maintenance activities has a potential to increase productivity by approximately 5%.

PurposeThe purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.Design/methodology/approachAn embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.FindingsThe studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.Practical implicationsThe results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.Originality/ValueFirst and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the state of the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners.