Beyond Complements and Substitutes: A Graph Neural Network Approach for Collaborative Retail Sales Forecasting

Practice-Oriented Abstract This paper proposes a novel approach for enhanced sales forecasting by leveraging multifaceted product relations, disentangled on the ground of the cross-category choice dependence theory. With superior forecasting performance over state-of-the-art alternatives and a deep understanding of product relations, the proposed approach has significant practical implications for various stakeholders (e.g., retail store managers, inventory department, purchasing department, operational staff, marketers, and retail platforms). On the one hand, improved forecasting could provide solid data-driven decision support for supply chain management, resource planning, inventory control, and purchasing planning. The semblance of predictive power in sales forecasting demonstrates operational utility. On the other hand, derived insights on product relations could facilitate reasonable pricing and promotion strategies, enhance the relevance and diversity of recommendation systems, and provide benefits for assortment planning, cross-selling, and shelf space allocation.