Demand Prediction-Based Lateral Transshipment Location–Allocation–Inventory Model for Two-Echelon Multi-Product Supply Chain

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Abstract
Manufacturing firms increasingly face challenges in inventory allocation due to rising logistics costs, regional demand heterogeneity, and demand fluctuations. To address these issues, this study proposes a demand prediction-based multi-product lateral transshipment location–allocation–inventory (LAI) model for a two-echelon supply chain under a vendor-managed inventory environment. An integrated framework combining an improved grey demand prediction model with the success history-based adaptive differential evolution algorithm (IGDP-SHADE) is developed. The IGDP model is employed to forecast product demand under small-sample and highly volatile conditions, while the SHADE algorithm optimizes facility location, inventory allocation, replenishment, and lateral transshipment decisions with the objective of minimizing total system costs. A case study based on the excavator supply chain of Sany Heavy Industry is conducted to evaluate the proposed approach. The results demonstrate that the IGDP model achieves a mean absolute percentage error (MAPE) of 13.86% and a root mean squared logarithmic error (RMSLE) of 0.1806, outperforming benchmark models including ARIMA and Holt–Winters in forecasting seasonal small-sample demand data. Compared with Gurobi, the proposed IGDP-SHADE algorithm obtains the same optimal solution while reducing computational time by 17.47%. Sensitivity results indicate that introducing the lateral transshipment mechanism reduces total system costs by 2.97% compared with the scenario without transshipment. Furthermore, the optimal number of centralized storage points is identified as six, enabling an effective balance between transportation, inventory holding, and transshipment costs.
Funding Information
  • National Natural Science Foundation of China (72474231)
  • National Natural Science Foundation of China (72074073)
  • Natural Science Foundation of Hunan Province (2024JJ5450)

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