XING SHENG YOU XUAN

The sale of fresh products is highly time-sensitive. Sales terminals need to provide the warehouse with the required quantity, after which the warehouse transports the fresh products to the sales terminals via a cold chain logistics system. When the predicted demand at the sales terminal is too low, multiple requests must be made to the warehouse, leading to increased transportation costs. Conversely, if the predicted demand is too high, unsold products may need to be sold at a discount or disposed of entirely, resulting in additional losses and waste. Therefore, accurate forecasting is very important.

The Pangu Large model achieves an accuracy rate of 97% in fresh product forecasting, significantly reducing logistics transportation costs and terminal waste, thereby saving costs for fresh product sales enterprises.