Mobile Consumer Scanning Technology: A Replacement for Interorganizational Information Systems for Demand Information Learning in Supply Chains?

Upstream firms in supply chains have shown a growing interest in deploying consumer scanning technology (CST), which relies on consumers scanning the purchased products with their mobile devices, as a novel alternative to conventional interorganizational information technology (IOIT) to learn downstream consumer demand information. However, there is limited understanding in the literature on (i) how CST helps improve upstream firms’ performances and (ii) whether CST will replace IOIT. This paper develops a theoretical model to examine the value of CST to a supplier that bypasses a retailer and employs CST to directly collect scan information from consumers who are incentivized with a reward for their scanning effort. Our theoretical analysis demonstrates that CST offers both operational and strategic value to the supplier. On the operational level, recognizing that the scan information gathered by CST is a censored version of the realized demand, we develop a simple and effective approach for the supplier to learn the realized demand from the censored scan information. We then investigate the learning efficiency of our approach, the optimal reward decisions, and the savings in the supplier’s inventory overage and underage costs arising from CST. On the strategic level, we examine the choice of IOIT and CST within supply chains in equilibrium. Contrary to the conventional view, we find that the availability of CST may expand (instead of suppress) the use of IOIT within supply chains. Using an extensive simulation analysis based on real-world data from a manufacturer that has implemented a CST program, we show that the value of CST to a manufacturer can be substantial and provide insights into how market conditions affect the value.