Graph-Informed Forecasting and Coordinated Replenishment: A Cost-Sensitive Study of Multi-Store Retail Inventory

James Young; Beatrice Morris

doi:10.62051/ajmse.v1n7.10

Graph-Informed Forecasting and Coordinated Replenishment: A Cost-Sensitive Study of Multi-Store Retail Inventory

James Young, Beatrice Morris

Academic Journal of Management Science and Engineering, (2026), Vol.1, No.7, pp.96-106

Published: October 29, 2026

DOI: 10.62051/ajmse.v1n7.10

PDF: Download Full Text PDF

Abstract

Retail networks with correlated demand shocks raise two separate questions: whether shared information improves forecasts, and whether better forecasts reduce inventory costs under shared capacity. Building on the integrated forecasting–replenishment perspective of MARIOD, we address both questions with a transparent 12-store, 300-day network featuring cluster structure, a two-day lead time, lost sales and an aggregate daily replenishment budget. Local ridge forecasts are compared with graph-lag forecasts that add lagged neighbor and cluster demand summaries, under independent equal-share ordering, proportional budget pooling and a standardized-deficit risk allocator. Across 20 seeds and four scenarios, graph features reduce forecast error in correlated settings, and proportional pooling lowers default-scenario cost by 7.37 units/day relative to equal-share ordering, establishing a strong low-complexity coordination baseline. A 3×3 economic sensitivity analysis shows that the cost effect of graph forecasts depends jointly on scenario, inventory policy and holding-to-stockout prices, demonstrating that forecast accuracy and operational value must be evaluated together. The study provides a controlled, decision-oriented benchmark against which learned multi-agent coordination policies can be measured.

Keywords: Retail Supply Chain, Graph-informed Forecasting, Inventory Allocation, Decision-focused Evaluation, Synthetic Benchmarking, Multi-Agent Coordination

APA Citation: James Young, Beatrice Morris (2026). Graph-Informed Forecasting and Coordinated Replenishment: A Cost-Sensitive Study of Multi-Store Retail Inventory. Academic Journal of Management Science and Engineering, 1(7), 96-106. https://doi.org/10.62051/ajmse.v1n7.10

References

Clark, A. J., & Scarf, H. (1960). Optimal policies for a multi-echelon inventory problem. Management Science, 6(4), 475–490. https://doi.org/10.1287/mnsc.6.4.475.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 38(4), 1346–1364. https://doi.org/10.1016/j.ijforecast.2021.11.013.
Chen, J., Zhang, X., & Han, X. (2023). Explainable identification of tax-related financial reporting anomalies in U.S. public companies: Cross-fitted tax-consistency residuals and Owen-value Shapley propagation. World Journal of Economics and Business Research, 1(1), 33–45.
Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748–1764. https://doi.org/10.1016/j.ijforecast.2021.03.012.
Syntetos, A. A., Boylan, J. E., & Croston, J. D. (2005). On the categorization of demand patterns. Journal of the Operational Research Society, 56(5), 495–503. https://doi.org/10.1057/palgrave.jors.2601841.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014.
Yang, Y., Wang, M., Wang, J., Li, P., & Zhou, M. (2025). Multi-agent deep reinforcement learning for integrated demand forecasting and inventory optimization in sensor-enabled retail supply chains. Sensors, 25(8), 2428. https://doi.org/10.3390/s25082428.
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1609.02907.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. In International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1710.10903.
Wu, Z., Pan, S., Long, G., Jiang, J., & Zhang, C. (2019). Graph WaveNet for deep spatial-temporal graph modeling. In International Joint Conference on Artificial Intelligence. https://doi.org/10.48550/arXiv.1906.00121.
Teng, D. (2025). TRIAD: Deadline- and reliability-aware scheduling for post-earthquake structural assessment workflows. World Journal of Engineering Research, 3(6), 55–66.
Oreshkin, B. N., Carpov, D., Chapados, N., & Bengio, Y. (2020). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1905.10437.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. https://doi.org/10.48550/arXiv.1707.06347.
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems, 30. https://doi.org/10.48550/arXiv.1706.02275.
Zhou, F. (2026). Moment-constrained side-force allocation for wind-assisted merchant ships: Hydrodynamic performance of retractable anti-leeway fins and flap rudders. Innovation and Technology Studies, 3(1), 27–41.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386.
Elmachtoub, A. N., & Grigas, P. (2022). Smart "predict, then optimize". Management Science, 68(1), 9–26. https://doi.org/10.1287/mnsc.2020.3922.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://doi.org/10.48550/arXiv.1706.03762.
Li, P. (2024). Think4SCND: Reinforcement learning with thinking model for dynamic supply chain network design. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3521439.
Liu, J., Wang, J., & Lin, H. (2025). Coordinated physics-informed multi-agent reinforcement learning for risk-aware supply chain optimization. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3629716.