The bullwhip effect under different information-sharing settings: a perspective on price-sensitive demand that incorporates price dynamics

Abstract

Information sharing has been shown previously in the literature to be effective in reducing the magnitude of the bullwhip effect. Most of these studies have focused on a particular information-sharing setting that assumes demand follows an autoregressive process. In this paper, we contribute to the literature by presenting a price-sensitive demand model and a first-order autoregressive pricing process that is coupled to the optimal order-up-to inventory policy and the optimal minimum mean-squared error forecasting technique. We compare a no information-sharing settingin which only the first stage of the supply chain observes end-customer demands and market prices, and upstream echelons must base their forecasts on downstream incoming orderswith two information-sharing settings, end-demand and order information and end-demand information. In the case of end-demand and order information, upstream echelons develop their forecasts and plan their inventories based on the end-customer demand, price information, and downstream orders. With end-demand information, upstream echelons use only end-customer demands and market prices to conduct their forecasting and planning. We derive the analytical expressions of the bullwhip effect with and without information sharing, quantify the impact of information sharing on the reduction of the bullwhip effect associated with end-demand and order information and end-demand information, and explore the optimal information setting that could most significantly restrain the bullwhip effect. Our analysis suggests that the value of these two information-sharing settings can be high, especially when the pricing process is highly correlated over time or when the product price sensitivity coefficient is small. Moreover, we find that the value of adopting end-demand and order information is always greater than that of end-demand information.

The impact of the supply chain structure on bullwhip effect

“…Operational causes include demand forecasting (Syntetos et al, 2009;Trapero et al, 2012), order batching (Potter and Disney 2006), price fluctuation (Ma et al, 2013;Lu et al, 2012), rationing and shortage gaming, lead time, inventory policy, replenishment policy, improper control system Syntetos et al, 2011), lack of transparency (Cannella et al, 2014b;Hussain et al, 2012), number of echelons Paik and Bagchi, 2007), multiplier effect, lack of synchronization (Ciancimino et al, 2012), misperception of feedback (Gonçalvez et al, 2005), local optimization without global vision (Disney and Lambrecht, 2008), company processes (Holweg et al, 2005, Cannella et al 2014c) and capacity limits (Crespo-Marquez, 2010). The behavioral causes cover neglecting time delays in making ordering decisions (Wu and Katok, 2006), lack of learning and/or training (Akkerman andVoss, 2013, Bruccoleri et al, 2014), and fear of empty stock/customers' baulking behavior (Croson and Donohue, 2006;Lin et al, 2014a).…”

The impact of information sharing on ordering policies to improve supply chain performances

“…Collaboration starts with information sharing, the main coordination mechanism to mitigate the bullwhip effect and its impacts (Lee et al, 1997b;Chen et al, 2000;Cho & Lee, 2013;Ma, Wang, Che, Huang, & Xu, 2013). Many studies focused on this issue, investigating different configurations of supply chains:…”

Analysis of the Complexity Entropy and Chaos Control of the Bullwhip Effect Considering Price of Evolutionary Game between Two Retailers

“…Wang and Disney et al [27,28] discussed the stability and oscillatory dynamics of the inventory system. Similar to what Ma et al [20,22] have done, we also import in price-sensitive demand in the supply chain. First, we assume that the two retailers both employ the AR (1) demand process, and both use the MMSE method, which is easy to be used to forecast the lead-time demand.…”

“…Ma et al [20] investigated a two-level supply chain in which the demand is price sensitive, while the price follows a first-order autoregressive pricing process. Wang et al [21] further investigated the case with price-sensitive demand and information sharing to probe into the bullwhip effect.…”