Information Updates and Transparency in Newsvendor-Supply-Chains : Values of Big Data and Block chain Technologies

In the Industry 4.0 era, emerging technologies such as big data analytics and blockchain 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 newsvendor 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 blockchain technology information sharing (BTIS) scheme can help achieve this by dampening knowledge uncertainty. We further extend the analysis to examine when blockchain technology incurs an operations cost and discuss the respective implications when blockchain is used for knowledge uncertainty reduction, as well as product information disclosures