Explainable AI in Demand Forecasting Machine Learning Models

Authors: Bharathram Nagaiah

DOI: https://doi.org/10.37082/IJIRMPS.v13.i5.232718

Short DOI: https://doi.org/g92nvj

Country: India

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Abstract: Demand forecasting is a crucial element of effective supply chain management, retail planning, and production scheduling. Although models of machine learning (ML) have been reinventing the way demands are predicted with consistently greater accuracy, the complexity can sometimes be a burden to interpret. Such a transparency deficit may impede trust, choices, and adoption in critical business circumstances. XAI methods are applicable in solving this problem because they help in rendering the process of decision-making of complex ML models comprehensible to human stakeholders. In the given article, the authors consider the use of XAI when demand is forecasted, presenting multiple explainability methods: SHAP, LIME, permutation importance, partial dependence plots, and counterfactual explanations. The paper highlights the role of these tools to reveal the motivators behind predictions, enhance user trust, and help users make well-informed decisions

Keywords: Explainable AI, demand forecasting, SHAP, LIME, time series, XGBoost, random forest, machine learning, interpretability, supply chain.

Paper Id: 232718

Published On: 2025-09-05

Published In: Volume 13, Issue 5, September-October 2025

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