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Discrete event simulation (DES) projects rely heavily on high input data quality. Therefore, the input data management process is very important and, thus, consumes an extensive amount of time. To secure quality and increase rapidity in DES projects, there are well structured methodologies to follow, but a detailed guideline for how to perform the crucial process of handling input data, is missing. This paper presents such a structured methodology, including description of 13 activities and their internal connections. Having this kind of methodology available, our hypothesis is that the structured way to work increases rapidity for input data management and, consequently, also for entire DES projects. The improvement is expected to be larger in companies with low or medium experience in DES.

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.

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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.

PurposeScholars 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueThe 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.