Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned imp...

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Veröffentlicht in:Supply Chain Analytics Jg. 8; S. 100087
Hauptverfasser: Radišić-Aberger, Ognjen, Burggräf, Peter, Steinberg, Fabian, Becher, Alexander, Weißer, Tim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2024
Elsevier
Schlagworte:
Earliness
Effectivity Date
Engineering Change
Machine Learning
Predictive Process Monitoring
Engineering Change
Predictive Process Monitoring
Effectivity Date
Earliness
Machine Learning
ISSN:2949-8635, 2949-8635
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Beschreibung
Zusammenfassung:By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.
ISSN:2949-8635
2949-8635
DOI:10.1016/j.sca.2024.100087