Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323301 
Year of Publication: 
2024
Citation: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Volume:] 347 [Issue:] 2 [Publisher:] Springer US [Place:] New York [Year:] 2024 [Pages:] 991-1030
Publisher: 
Springer US, New York
Abstract: 
Abstract 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.
Subjects: 
Explainable artificial intelligence (XAI)
Uncertainty quantification (UQ)
Predictive process monitoring
Information systems (IS)
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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