Real-Time Demand-Driven Inventory and Fulfillment Decision System for Agile Retail Supply Chains

Jing Yang

doi:10.70393/6a6574626d.333333

Authors

Jing Yang Purdue University

DOI:

https://doi.org/10.70393/6a6574626d.333333

ARK:

https://n2t.net/ark:/40704/JETBM.v2n6a01

Disciplines:

Logistics

Subjects:

Inventory Management

References:

12

Keywords:

Demand-Driven Supply Chain, Real-Time Inventory Optimization, Model Predictive Control (MPC), Retail Forecasting (M5 Dataset)

Abstract

This paper presents a real-time demand-driven inventory and fulfillment decision system that couples high-granularity forecasting with rolling-horizon allocation under operational constraints. Using the public M5 retail dataset at the SKU–store–day level, we train a hybrid time-series forecaster and feed its multi-step predictions into a lightweight model-predictive control (MPC) allocator that respects capacity and lead-time limits. We evaluate accuracy using WRMSSE as the primary metric, and report RMSE and MAE as complementary scales to mitigate the instability of MAPE in sparse-demand series. Results show consistent accuracy gains over fixed safety-stock and (s, S) baselines, along with improved service levels without increasing total inventory. Robustness checks with demand noise and feature ablations suggest that promotion and calendar signals make the most significant contributions to forecast quality. The findings demonstrate that a closed-loop predictive–prescriptive pipeline can enhance agility and resilience for retail supply chains operating under uncertain demand.

Author Biography

Jing Yang, Purdue University

School of Business, Purdue University, Indiana, USA, 47907.

References

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