Methodology of Operationalization of KPIs for Shop-Floor

Methodology of Operationalization of KPIs for Shop-Floor

Mariana Raposo Oliveira (Universidade de Lisboa, Portugal), Diogo Jorge (Erising, Portugal) and Paulo Peças (Universidade de Lisboa, Portugal)
DOI: 10.4018/978-1-5225-5445-5.ch010

Abstract

Key performance indicators (KPIs) are a critical tool to support activities and results' monitoring in any industrial organization. The published literature and the available approaches on KPIs focus on the business and administrative level, being computed with information retrieved at the shop-floor level. Despite that, there is a scarcity of structured and comprehensive approaches to support the generation of KPIs to be used at the shop-floor level (the few existent approaches are empiric-based). In this chapter, a methodology to support the selection and organization of KPIs at the shop-floor level is proposed. Departing from the Hoshin Kanri strategy deployment, it identifies the levels of decision and control in the company regarding the production activities and derives the most adequate KPIs for each level based on universal questions about “what performance to assess.” The build-up of visual management boards for each level is also proposed.
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Introduction

The Industry 4.0 and the KPIs

Nowadays Big Data is a widely used term to refer the generation and communication of data associated with new technologies (Fanning, 2016). This communication flow is based on the data collected by the company itself and it is done with the final aim of allowing quick and informed decisions, with increased productivity in mind. This has become increasingly popular as a result of the Industry 4.0 concept, also known as fourth industrial revolution (Hermann, Pentek, & Otto, 2016). Industry 4.0 is associated with information technology, automation and control applied to production processes, i.e. through the “internet of things”, the cyber-physical systems communicates and collaborates with each other and with humans, throughout the entire value chain and in real time (Hermann et al., 2016). This concept was born in Germany in 2011, under a German Government strategic project which aimed to promote the digitalization of production systems and to allow its in-line monitoring and from afar (Henning, Wolfgang, & Johannes, 2013; Hermann, Pentek, & Otto, 2015).

As this type of systems is implemented, the amount of data generated is expected to increase. If this data is to be used in a useful and efficient way, it requires an appropriate treatment. As a result, there is the need to transform “big data” into “smart data”, nurturing to speed up the decision-making process, supplying vital and assertive information for management activities (Fanning, 2016; Hermann et al., 2016).

Taking into account such difficulties and the need to find how to filter the data (to make it useful) generated in companies, the concept of Key Performance Indicator (KPI) has become increasingly mentioned in the literature, as an essential instrument in the interface between data generated and decision makers (humans or decision algorithms) (Fanning, 2016; Hermann et al., 2016; Marr, 2015). A definition of KPIs can be given: a restricted group of indicators intended to gauge the performance of a system in a systematic and comprehensive manner (Kahn, 2013; Parmenter, 2010).

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