Information Sharing in Supply Chains: An Empirical and Theoretical Valuation

Abstract

We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from a CPG company, we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. While the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These "decision deviations" lead to information losses in the order process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of operations knowledge-the value of knowing the downstream replenishment policy.

Global Dual Sourcing and Order Smoothing: The Impact of Capacity and Lead Times

“…Bray and Mendelson (2012) study firm-level U.S. data and show that firms generally amplify lastminute shocks, yet smooth seasonal variations. Cui et al (2014) present strong empirical evidence of order smoothing. There is also a large economics literature preceding the work in operations management, which empirically investigates production smoothing-we refer to Cachon et al (2007) for an overview and discussion.…”

The Operational Value of Social Media Information

“…For example, Gaur et al (2007) use dispersion among experts' forecasts as a measure of demand uncertainty for new products; Bassamboo et al (2015) study how crowds can obtain better forecasts than can individuals; Kesavan et al (2010) show how using historical inventory and gross margin data can improve forecasting; Osadchiy et al (2013) show that a model combining analysts' forecasts and financial market returns improves forecast accuracy; and Kremer et al (2011) study the behavioral bias of forecasting and propose an intervention to improve the forecast accuracy. Downstream information, such as point-of-sales, has also been shown to add value to upstream order forecasting (see Gaur et al 2005 andCui et al 2015). These papers use financial market index data, accounting variables, or external operations information to improve sales forecasts.…”

Demand–Supply Mismatches and Stock Market Performance: A Retailing Perspective

“…Driven by the fact that demand and supply variability are the primary factors responsible for supply chain inefficiency, we develop a novel key performance indicator (KPI), entitled DSM (demand-supply mismatch), which measures the relative volatility of the inventory productivity of a firm. Our KPI is supported by the extensive body of literature on the "Bullwhip effect," which shows that unnecessarily amplified inventory volatility has negative consequences along various dimensions (e.g., Chen and Lee 2012, Cui et al 2015, Lee et al 1997, Warburton 2004. In contrast to prior studies that relate inventory productivity measures directly to financial metrics (Alan et al 2014, Cannon 2008, Gaur et al 2005 or that normalize inventory productivity by an industry peer's performance (Chen et al, 2005, Kesavan and Mani 2013, our DSM measure accounts for the volatility of inventory productivity over time.…”