Assessing the value of demand sharing in supply chains

Abstract

We consider the problem of assessing the value of demand sharing in a multistage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order‐up‐to policy. We demonstrate how a typical supply chain player can determine the extent of its available information in the presence of demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. The retailer's demand is driven by the random shocks appearing in the autoregressive moving average representation for its demand. Under the assumptions we will make in this article, to the retailer, knowing the shock information is equivalent to knowing the demand process (assuming that the model parameters are also known). Thus (in the event of sharing) the retailer's demand sequence and shock sequence would contain the same information to the retailer's supplier. We will show that, once we consider the dynamics of demand propagation further up the chain, it may be that a player's demand and shock sequences will contain different levels of information for an upstream player. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. Although it follows from existing literature that demand sharing is equivalent to full information shock sharing between a retailer and supplier, we demonstrate and characterize when this result does not generalize to upstream supply chain players. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full information shocks (FIS) with an adjacent upstream player as quasi‐ARMA in—quasi‐ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 515–531, 2014

Strategic information sharing in supply chain with value-perceived consumers

“…This finding is not without contradictory to some studies such as that of Giloni et al. (2014) and Kovtun et al. (2014).…”

“…By contrast, previous research concluded that information sharing is only useful in the setting of positively correlated demand. This finding is not without contradictory to some studies such as that of Giloni et al (2014) and Kovtun et al (2014). The authors provide some theoretical results, which suggest that information sharing in case of a pure AR(1) retail demand would hold no value when a manufacturer can recover the retailer's information in terms of demand shocks from its invertible ARMA (1,1) order demand.…”

“…Moreover, the study pointed out that the benefit of information sharing might be lower than expected if the manufacturer is sophisticated enough to obtain more information about demand variance by using historical data from the retailer's orders. Within the context of correlated demand, the value of information sharing has been explored under various supply chain configurations, demand specification and sharing mechanisms by numerous researchers, such as Yu et al (2002), Raghunathan (2003), Cheng and Wu (2005), Wu and Cheng (2008), Yao and Dresner (2008), Leng and Parlar (2009), So si c (2010), Cho and Lee (2013), Ganesh et al (2013), Kalpakam et al (2014), Kovtun et al (2014), Babai et al (2016), Huang et al (2016a, b), Sabitha et al (2016), Huang et al (2017), Dominguez et al (2018), Kovtun et al (2019), Lu (2021) and Lu et al (2021).…”