Product lifecycle optimization by application of process mining

Marco Meßner and Johannes Dirnberger

A chapter in Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain, 2020, pp 295-315 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: Purpose: Active product life cycle management contributes to supply chain optimization. However, in nowadays industry the high number of variants and backward loops complicate tracing the entire product lifecycle in an ERP system. Consequently, product lifecycle and corresponding process-organizational optimizations are difficult to implement using established analysis. The aim is to challenge process mining as an alternative to address these aspects. Methodology: This paper, therefore, applies process mining to the ERP data of a component manufacturer in the metalworking industry. For this purpose, optimization potentials are derived from a literature research and subsequently validated by the application of process mining. Thereby, the data sample comprises 202 products with 15,000 corresponding activities, which were accumulated in the period 2017 to 2019. Findings: Process mining reveals the product lifecycles and allows to take different analysis perspectives, such as a market or product category view. Firstly, potentials in a variant-driven business for PLM will be elaborated. Secondly, process-organizational recommendations for the product management are developed. Thus, this paper offers a concrete approach to mapping and analyzing the product lifecycle by application of process mining. Originality: On the one hand, current analysis tools used in ERP systems merely assess the products actual status. On the other hand, PLM systems are regarded as costly due to the complexity but also a continuous process view is not its main focus. Nevertheless, there is little literature on alternatively using process mining in this context.

Keywords: Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:228925

DOI: 10.15480/882.3127

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