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This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM. [ABSTRACT FROM AUTHOR]

In the industrial field, one of the most widespread KPIs is represented by the Overall Equipment Effectiveness (OEE), first introduced by Seiichi Nakajima within the Total Productive Maintenance (TPM) theory and aimed at identifying the inefficiencies of industrial assets. While OEE has been objective of several studies, the relationship between the Overall Equipment Effectiveness and the role of the human factor in achieving its high levels of values has not been extensively investigated. In recent years few scientific studies have investigated the relationship, showing that there is a link between OEE and human factors, even significant, but not clearly identified yet. In order to examine this relationship, our study proposes a framework to clarify the links between human factors, OEE parameters, the industrial sector, and the degree of automation. This framework is then validated through the application of the Analytic Hierarchy Process (AHP) methodology. As a result, 13 aspects related to the human factor were identified. Finally, the study provides practical guidance and implications for maximizing the outcomes of the investigation, with the goal of improving an organization’s overall manufacturing performance. By understanding the impact of the human factor on OEE, organizations can make informed decisions to optimize their operations and achieve higher levels of productivity. [ABSTRACT FROM AUTHOR]