Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics

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Abstract
Optimizing costs and profits while meeting customer demand is a critical challenge in the development of perishable supply chains. Customer-centric demand forecasting addresses this challenge by considering customer characteristics when determining inventory levels. This study proposes a solution framework comprising two steps: (a) segmentation using customer characteristics and (b) demand forecasting for each segment using transparent and responsible artificial intelligence techniques. We employed k-means, hierarchical clustering, and explainable AI (XAI) to segment, model, and compare several machine-learning techniques for demand forecasting. The results showed that support vector regression outperformed the autoregressive models. The results also showed that the two-step segmentation and demand forecasting process using hierarchical clustering and LSTM outperforms (Weighted average RMSE across segments = 61.57) the conventional single-step unsegmented forecasting process (RMSE overall data = 238.18). The main implication of this study is the demonstration of XAI in enhancing transparency in machine learning and an improved method for reducing forecasting errors in practice, which can strengthen the supply chain resilience for perishable products.
Funding Information
  • University of Technology Sydney

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