IFAC PapersOnLine 51-11 (2018) 7–12 ScienceDirect ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2018.08.226 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. 1 INTRODUCTION A performance measurement system (PMS) consists of a set of procedures and indicators that precisely and constantly measure the performance of activities, processes and the whole organization, and is a vital aspect in regard to the management of companies (Neely et, 2005). A PMS should be able to provide data for monitoring the past and the future performance, to strengthen the strategies to avoid introducing the conflicting indicators and support in providing data for benchmarking. PMSs do not only focus on financial procedures and indicators, they also often relate to consumers’ aspects or internal processes (Lohman, 2004). Key performance indicators (KPIs) are considered the core of the PMS: they are defined as a set of measures that focus on the main critical activities (Parmenter, 2007). With the help of indicators, companies can prove an existing gap between the actual and desired performance. KPIs allow managers to identify the progress in activities and those to be improved, support the setting of new goals, help decision-making in order to reach the desired performance and improvement, allow the translation of a company’s missions into concrete actions, and to evaluate how well the company is pursuing its objectives (Weber, 2005). The growing competition and complexity results in an ever-increasing demand for more accurate performance monitoring and controlling, especially in manufacturing firms (Hwang et al., 2016). In this context, KPIs are used to evaluate the efficiency and effectiveness of the actions in the production process, part of the processes, or also the entire production system (Braz et al., 2011). KPIs in production lead, judge and assist the decision process (Neely et al., 1996). Although performance measures in the manufacturing context have been widely studied for many years, further enhancements are required to help companies pursue their goals. Indeed, Lindberg et al. in 2015 state that many industries still don’t have proper indications on how their performance should be measured and improved (Lindberg et al., 2015). Moreover, the problem in sharing information between different factories, in order to be able to benchmark, is critical to compete (Fukuda & Patzke, 2010). KPIs should be properly selected to adapt the industry specificity, but general enough to be able to compare different operations. The actual technologies allow the collection of massive amount of data, and the sharing of this data between different sources. What data the sources should share is a critical decision. The International Organization for Standardization (ISO) has recently dealt with this topic in ISO 22400 standard “Automation systems and integration — Key Performance Indicators (KPIs) for manufacturing operations management”. The scope of ISO 22400 standard (henceforth, just “ISO 22400”) is ambitious, as it proposes a set of KPIs that aims to be industry and process independent. The ISO 22400 aims at defining the most important and commonly used measures for a manufacturing industry, and therefore it has been recognized for its potential contribution on manufacturing automation system development (Fukuda & Patzke, 2010). Nevertheless, in its current version, the standard shows some weaknesses. The understanding and interpretation of some concepts are difficult, resulting in inconsistences and imprecisions. As a result, there is an improvement potential to deal with before the standard can leverage its full potential. Keywords: ISO 22400; enterprise system engineering; Key Performance Indicators (KPIs); performance evaluation; manufacturing, operations management. Abstract: A set of key performance indicators (KPIs) for manufacturing operation management is introduced in the ISO 22400 standard. However, KPIs are only defined at a high abstraction level and this hampers the standard’s practical adoption. In this paper, a framework is introduced in order to solve this weakness. Starting from the analysis of the relevant measurements used in the KPIs’ formulas, a classification model is introduced defining three possible application scopes - work orders, work units and production orders - to analyze each basic element of the standard. As a result, the KPIs defined in the ISO 22400 standard are more precisely specified and, as a consequence, also suitable to be straightforwardly implemented in performance measurement systems. 1 Dept. of Enterprise Engineering, "Tor Vergata" University of Rome, Rome, Italy (e-mail: martina.varisco@uniroma2.it) 2 Dept. of Automatic Control, Lund University, Lund, Sweden (e-mail: charlotta.johnsson@control.lth.se) 3 Dept. of Automatic Control, Lund University, Lund, Sweden (e-mail: jacob.mejvik@control.lth.se) 4 Dept. of Enterprise Engineering, "Tor Vergata" University of Rome, Rome, Italy (e-mail: schiraldi @uniroma2.it) 5 Dept. of Control Science and Engineering, Dalian University of Technology, Dalian, China, guest researcher at Department of Automatic Control, Lund University, Sweden (e-mail: li.zhu@control.lth.se) Martina Varisco 1 , Charlotta Johnsson 2 , Jacob Mejvik 3 , Massimiliano M. Schiraldi 4 , Li Zhu 5 KPIs for Manufacturing Operations Management: driving the ISO22400 standard towards practical applicability