Improving Supply Chain and Logistics: Predicting Demand and Optimizing Inventory with Historical Sales Data

Wadahfred Kudi; Dongxiao Ren

doi:10.36948/ijfmr.2026.v08i05.89078

Improving Supply Chain and Logistics: Predicting Demand and Optimizing Inventory with Historical Sales Data

Author(s)	Mr. Wadahfred Kudi, Dr. Dongxiao Ren
Country	China
Abstract	Abstract Supply chains increasingly operate under volatile demand, uncertain replenishment lead times, promotional shocks, and disruptions that make point-forecast-based inventory policies difficult to manage. This article develops and empirically evaluates an integrated framework that links historical point-of-sale demand forecasting with probabilistic, multi-echelon inventory optimization. The study compares six forecasting approaches Simple Moving Average, Holt-Winters Exponential Smoothing, ARIMA, XGBoost, LSTM, and a stacked ensemble using 156 weeks of weekly sales data covering 450 stock-keeping units and three distribution nodes. Models are evaluated on a 13-week holdout using MAE, RMSE, MAPE, and R², with formal Diebold–Mariano tests used to assess predictive differences. The best-performing ensemble combines XGBoost, LSTM, and Holt-Winters outputs through a ridge-regularized meta-learner. Its aggregate MAPE is 11.6%, compared with 24.1% for ARIMA, while its MAE and RMSE are also lowest. The study then converts forecast uncertainty into lead-time demand distributions through residual bootstrapping and uses these distributions to parameterize a dynamic continuous-review (s, S) policy. A discrete-event simulation with 500 Monte Carlo replications shows that the ensemble-driven policy reduces total simulated supply-chain cost from $4.286 million to $2.918 million, improves realized fill rate from 89.4% to 96.7%, and reduces warehouse-to-supplier bullwhip amplification from 3.02 to 1.78.
Keywords	Supply Chain Analytics; Demand Forecasting; LSTM; Inventory Optimization;
Field	Computer > Data / Information
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-10-24
DOI	https://doi.org/10.36948/ijfmr.2026.v08i05.89078

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Improving Supply Chain and Logistics: Predicting Demand and Optimizing Inventory with Historical Sales Data

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