A new model development for demand forecasting of supply chain from data fusion perspective

Abstract

In the increasingly competitive society, the supply chain, as the third profit center of the enterprise, makes scholars and industry-related with supply chain more and more interesting in the study of relevant issues. Exiting studies believed that an application of predictive analytics could be a tremendous impact on supply chain management. The uncertainty of supply chain demand and bullwhip effect is a challenge in the supply chain. Data fusion can effectively reduce the uncertainty of demand and the amplification effect. In this study, a new conceptual model was established on the traditional supply chain based on data fusion. Results show that the conceptual model refers to data fusion for solving the uncertain and inconsistent multi-source data by Bayesian estimation and to providing reasonable decision information for supply chain managers.