Bayesian Estimation of Supply Chain Innovation Path

With the transformation of the supply chain from factor driven to investment driven and to innovation driven, supply chain innovation has attracted more and more attention. This paper obtains the Bayesian prior probability of innovation and studies the supply chain innovation path from the perspective of strategy and behavior. The model divides the overall innovation capability of the supply chain into specific node tasks and assets (Capability Set), and the innovation demand of the supply chain is expressed in the form of the conditional probability of market demand. Under the conditions of minimum risk, minimum cost, and rapid market response, it determines who should lead the supply chain innovation first and what type of supply chain innovation should be carried out first. Finally, taking the supply chain of the professional market in Zhejiang Province as an example, this paper verifies the theoretical model of supply chain innovation decision-making. The innovation of the supply chain of Zhejiang's specialized market is decomposed into the capability set of each enterprise node of the supply chain. This paper transforms the group innovation ability of the supply chain in the professional market into the individual innovation ability of a single enterprise node and reveals the starting point and intensity of the demand innovation of products/services in the supply chain.