This invention relates to the field of supply chain
demand forecasting technology. Specifically, it relates to a supply chain
demand forecasting method and
system based on multi-source heterogeneous data fusion. The aim is to address the problem in
community group-buying scenarios where sudden surges in localized orders easily generate
noise, leading to
distortion after the fusion of these surges with multi-
source data, affecting the accuracy of
demand forecasting and hindering automatic replenishment decisions in the supply chain. The method involves real-time collection of
community group-buying order locations and sales volumes, using
reverse geocoding to unify the format; after identifying surges in orders, density clustering is used to calculate the coverage
radius and classify the data, matching
noise intensity coefficients and
hysteresis correction amounts, and combining a negative exponential function to generate a dynamic
attenuation coefficient, thus doubly correcting surges in sales volume; the corrected data is then fused with e-commerce transaction and store
inventory data, standardized, and input into a
recurrent neural network model to output regional demand forecasts for the next seven days, triggering automatic replenishment decisions and improving forecast accuracy and supply chain responsiveness.