Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information

Abstract

An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple-stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.bullwhip effect, forecasting, information, inventory, lead time, supply chain, variability

Models for Supply Chains in E-Business

“…He shows that the owner of the firm can manage the divisions as cost centers without compromising the systemwide performance by using an incentive-compatible measurement scheme based on accounting inventory levels. Similar to Celikbas et al (1999), Chen (1999) shows that it is important for the upstream members of the supply chain to have access to accurate customer demand information. More recently, Cachon and Lariviere (2001) study forecast sharing in a two-stage supply chain between a manufacturer and a supplier, where the manufacturer provides an initial forecast and a contract to the supplier, who in turn invests to set up capacity in anticipation of demand.…”

“…The notion of lack of information in the supply chain and the resulting bullwhip effect was first studied by Lee et al (1997). Chen et al (2000) quantify the effect of forecasting and lead times on the bullwhip effect under stylized supply chain settings. A number of other papers have dealt with the flow of information in the supply chain and the effect of that on its performance.…”

A model predictive control framework for centralised management of a supply chain dynamical system

“…Assume that the upstream suppliers always have ample goods in stock (Chen, Drezner, Ryan, & Simchi-Levi, 2000;Lee, So, & Tang, 2000;Ouyang & Daganzo, 2006;Ouyang & Li, 2010) to meet their customers' demands, then this approximation leads to the following two relations:…”

Assessing the Impact of Prices Fluctuation on Demand Distortion Within a Multi-echelon Supply Chain

“…The EPOS (Electronic Point of Sales), structure, which, as several authors have already demonstrated, reduces the Bullwhip effect [14,7], is similar to the one used in this work to build the predictive model, although in the EPOS structure the sales information to the end customer is passed on to each of the chain members, thus improving the forecasts made by them and reducing the variability of the generated replenishment/ manufacturing orders. A model has been associated with each member of the simulated chain, enabling the calculation of the replenishment/manufacturing order variation which would be generated according to the information available.…”

“…The measurement of the Bullwhip effect proposed by Chen et al [14] has not been deemed appropriate in this case, because its sensitiveness to small values in demand might induce this indicator to produce wrong values (huge peaks) which distort that measurement. Furthermore, as this is a traditional multilevel supply chain, it is difficult for manufacturers or wholesalers to actually know what the final demand is.…”