Meßner, MarcoMarcoMeßnerDirnberger, JohannesJohannesDirnberger2020-12-012020-12-012020-09-23Hamburg International Conference of Logistics (HICL) 29: 295-315 (2020)http://hdl.handle.net/11420/8017Purpose: Active product life cycle management contributes to supply chain optimi-zation. However, in nowadays industry the high number of variants and backward loops complicate tracing the entire product lifecycle in an ERP system. Conse-quently, 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, optimiza-tion 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-organiza-tional recommendations for the product management are developed. Thus, this pa-per 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 as-sess 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.enhttps://creativecommons.org/licenses/by-sa/4.0/LogisticsIndustry 4.0DigitalizationInnovationSupply Chain ManagementArtificial IntelligenceData ScienceWirtschaftHandel, Kommunikation, VerkehrConference Paper10.15480/882.3127https://www.epubli.de/shop/buch/Data-Science-and-Innovation-in-Supply-Chain-Management-Wolfgang-Kersten-9783753123462/10604710.15480/882.3127Kersten, WolfgangWolfgangKerstenBlecker, ThorstenThorstenBleckerRingle, Christian M.Christian M.RingleOther