Information Updates in Newsvendor Supply Chains : Values of Big Data and Blockchain Technologies

Today, emerging technologies such as big data analytics and block-chain can improve supply chain operations by updating information to reduce demand uncertainty and improving data quality. We conduct a theoretical exploration of the values provided by such technologies in the context of a news-vendor supply chain facing a normally distributed demand with an unknown mean. We use Bayesian learning to reduce demand uncertainty based on the observation data, and obtain the expected value of big data (EVBD) as the number of data becomes large. Further, we obtain the expected value [resp. profit] of perfect information (EVPI [resp. EPPI]), and establish a relationship between EVBD and EVPI. We then define a concept of “asymptotically perfect coordination”, and propose how a block-chain technology information sharing (BTIS) scheme can help achieve this by dampening knowledge uncertainty. Finally, we extend the analysis to cover the case when there are operations costs associated with block-chain technology and discuss the respective implications when block-chain is used for knowledge uncertainty reduction and product information disclosures