Leveraging Consumer Technology to Learn Demand Information in Lieu of Supply Chain Collaboration

Abstract

Recently, Chinese firms have begun to deploy popular mobile apps (e.g., WeChat) into their supply chain practices to improve demand visibility. These efforts rely on consumers to scan the products they purchase using these apps, which we refer to as consumer scanning technology (CST). CST can be an alternative to conventional inter-organizational information technology (IOIT) that relies on collaboration between supply chain firms. We develop a theoretical model to examine the value of CST to learn supply chain (demand) information. In this model, an upstream supplier bypasses the downstream retailer and employs CST to directly collect end scan information from consumers who are incentivized with a reward for participating. The theoretical analysis of the model demonstrates both operational and strategic values of CST. On the operational level, noticing that the scan information gathered by CST is a censored version of the true information, we develop a simple and effective approach for the supplier to learn the true information from the censored one, and then investigate the learning efficiency of our approach and the optimal reward decisions. On the strategic level, we examine the equilibrium choice of IOIT and CST within supply chains and investigate their interplay. Contrary to conventional view, we find that the availability of CST may expand (instead of suppressing) the use of IOIT within supply chains. Using real-life data from a manufacturer that has implemented a CST program for learning demand information, we show that the value of CST can be substantial.