Towards Trustworthy AI in Demand Planning: Defining Explainability for Supply Chain Management

Ruiqi Zhu, Cecilie Christensen, Bahram Zarrin, Per Bækgaard, Tommy Sonne Alstrøm

Abstract: Artificial intelligence is increasingly essential in supply chain management, where machine learning models improve demand forecasting accuracy. However, as AI usage expands, so does the complexity and opacity of predictive models. Given the significant impact on operations, it is crucial for demand planners to trust these forecasts and the decisions derived from them, highlighting the need for explainability. This paper reviews prominent definitions of explainability in AI and proposes a tailored definition of explainability for supply chain management. By using a user-centric approach, we address the practical needs of definitions of explainability for non-technical users. This domain-specific definition aims to support the future development of interpretable AI models that enhance user trust and usability in demand planning tools.

External IDs:dblp:conf/icaart/ZhuCZBA25