Intelligent Demand Forecasting for Sustainable Spare Parts Reuse in a Rational Consumption Environment - BIN, LIN XI (International Journal of Supply Chain Management, 2025)
This study explores the development of intelligent demand forecasting methods in the context of the rise of rational consumption awareness, to establish the optimal spare parts reuse plan. As consumers pay more attention to sustainability and environmental protection, price and quality are no longer the only considerations; global inflation has also made consumer behavior more conservative, emphasizing budget control and long-term value. In response to the sluggish market demand, this study proposes a intelligent demand forecasting method to help companies understand consumer demand and design product reuse plans that are in line with their values. This study adopts the concept of "integrated forecasting" and designs two methods: (1) Hybrid Stacking (HS) Method: Combining traditional time series and machine learning techniques to improve forecast accuracy; (2) External Information Integration (EI-HS) Method: Incorporating external information into the HS model as an improvement baseline to further reduce forecast errors. Finally, the results of MASE and RGRMSE indicate that the methods proposed in this study hold strong application potential. The research results emphasize that the development of intelligent demand forecasting methods in the era of rational consumption is of key significance and points out the direction of sustainable improvement and expansion in the future, providing companies with more accurate forecasting tools and spare parts reuse decision-making basis.