Aftermarket demands forecasting with a Regression-Bayesian-BPNN model

The rapid development of automobile industry in China promotes the stable growth of the automotive aftermarket. For optimizing supply chain operations and reducing costs, it is critical for a company to forecast the demands for auto spare parts in the future. This paper proposes an improved Regression-Bayesian-BBNN (RBBPNN) based model to realize the demands forecasting. Compared with a classic ARMA model, the proposed RBBPNN model has higher accuracy and better robustness. These advantages are illustrated through the case study with the real sales data of a 4s shop in Shanghai.