Leveraging Artificial Intelligence for Smart Retail Decision Support: A Proof-of-Concept Framework for Demand Forecasting, Inventory Simulation, and Synthetic Workflow Assignment

Source abstract

Introduction: This study presents a proof-of-concept smart-retail decision-support framework that links demand forecasting, fixed-threshold inventory simulation, and trigger-gated synthetic workflow assignment. Methodology: An exploratory ItemID 1 analysis reports in-sample RMSE and MAE for Prophet, ARIMA, XGBoost, and LSTM as model-fit diagnostics only. A separate Store 1–Item 1 LSTM, trained with a chronological 90/10 split and training-only scaling, produces 182 one-step-ahead outputs for 3 July–31 December 2017, which serve as demand inputs to the inventory simulation. Results: This demand path, a 30-unit reorder threshold gave a lower average inventory level (81.68 versus 98.71 units) and a lower simulated holding-cost component ($1,486.60 versus$1,796.60) than a 50-unit threshold. A deterministic synthetic workflow releases an officer assignment only when a procurement trigger occurs. Conclusion: The contribution is the explicit orchestration of interfaces from forecasting output to replenishment events and gated task assignment. The evidential scope of each component is reported separately, and the results are not evidence of forecasting superiority, optimal inventory policy, or operational performance.