Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/323301 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Volume:] 347 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 991-1030
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
In the rapidly evolving landscape of manufacturing, the ability to make accurate predictions is crucial for optimizing processes. This study introduces a novel framework that combines predictive uncertainty with explanatory mechanisms to enhance decision-making in complex systems. The approach leverages Quantile Regression Forests for reliable predictive process monitoring and incorporates Shapley Additive Explanations (SHAP) to identify the drivers of predictive uncertainty. This dual-faceted strategy serves as a valuable tool for domain experts engaged in process planning activities. Supported by a real-world case study involving a medium-sized German manufacturing firm, the article validates the model’s effectiveness through rigorous evaluations, including sensitivity analyses and tests for statistical significance. By seamlessly integrating uncertainty quantification with explainable artificial intelligence, this research makes a novel contribution to the evolving discourse on intelligent decision-making in complex systems.
Schlagwörter: 
Explainable artificial intelligence (XAI)
Uncertainty quantification (UQ)
Predictive process monitoring
Information systems (IS)
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