Demand Sensing

Abstract

This thesis develops a mathematical and computational framework for demand sensing in supply chain management, addressing the dual challenges of forecast accuracy and hierarchical coherence. This work was carried out during a graduate internship at Dassault Systèmes. Modern retail demand is shaped by vertical aggregation constraints, horizontal product interactions, and temporal dependencies influenced by external drivers. Traditional approaches (classical time series, machine learning, and reconciliation heuristics) treat these aspects separately and struggle to deliver coherent and accurate forecasts at scale.

The thesis first establishes a theoretical foundation for multi-product demand forecasting. Forecasting is formalized as a supervised learning problem on probability spaces, introducing the multi-product demand process and a general forecast operator that unifies local versus global modeling, recursive versus direct horizons, and alternative loss functions. Hierarchical forecasting is expressed in compact linear algebraic form, showing how classical heuristics and optimal reconciliation (MinT/OLS) can be understood as projections onto the coherent subspace.

Building on this, the thesis introduces a graph-based extension of the framework. By embedding time series into relational graphs, Graph Neural Networks (GNNs) capture vertical, horizontal, and temporal dependencies within a unified model. An end-to-end forecasting pipeline is proposed, combining graph construction, feature extraction with external covariates, GNN encoding, and forecast generation, with coherence ensured through bottom-up construction, regularization penalties, or post-hoc reconciliation.

Empirical evaluation on the M5 dataset demonstrates the practical implications. Incorporating external drivers consistently improves accuracy across models and aggregation levels. Machine learning models, particularly gradient boosting, outperform classical baselines at granular levels, though the latter remain competitive at higher aggregations. The proposed Hierarchical Graph Network (HGN) achieves competitive results, with particular benefits at intermediate levels where both hierarchical and cross-series relations are most informative. A comparison of local versus global training highlights the trade-off between accuracy and computational efficiency.

The findings underscore three insights for practice: external drivers require robust data infrastructure, hybrid local–global modeling offers balanced accuracy and efficiency, and reconciliation remains essential to guarantee coherence. Overall, the thesis demonstrates how integrating hierarchical structure, external signals, and graph-based learning advances both the theory and practice of demand sensing in modern supply chains.