Supply Chain Demand Forecast Based on SSA-XGBoost Model

Abstract

Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts. To solve these problems, using artificial intelligence and big data technology to achieve market demand forecasting and intelligent decision-making is becoming a strategic technology trend of supply chain management in the future. Firstly, this paper makes a visual analysis of the historical data of the Stock Keeping Unit (SKU); Then, the characteristic factors affecting the future demand are constructed from the storage level, product level, historical usage of SKU, etc; Finally, a supply chain demand forecasting algorithm based on SSA-XGBoost model has proposed around three aspects of feature engineering, parameter optimization and model integration, and is compared with other machine learning models. The experiment shows that the forecasting result of SSA-XGBoost forecasting model is highly consistent with the actual value, so it is of practical significance to adopt this forecasting model to solve the supply chain demand forecasting problem.

A Comprehensive Analysis of Demand Prediction Models in Supply Chain Management

“…The literature emphasizes the importance of improving forecast accuracy using ERP. the enterprise resource planning (ERP) system has greatly improved the availability and accuracy of data (Ni, Peng, Peng, & Liu, 2022). Ghalehkhondabi et al identified common forecasting approaches like Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), as well as Mean Squared Error (MSE) (Ghalehkhondabi et al, 2017).…”