Supply Chain Networks With Multiple Retailers: A Test of the Emerging Theory on Inventories, Stockouts, and Bullwhips Xiang Wan and Philip T. Evers University of Maryland W hile various approaches to mitigating the bullwhip effect have been proposed, the composition of the underlying supply chain is often taken for granted. This article develops a set of simulation models to investigate changes to the supply chain itself and their impact on the bullwhip effect, on-hand inventory, and stockouts. It is shown that particular supply chain networks have an impact on the bullwhip effect. Furthermore, the impact of supply chain networks on the bullwhip effect is moderated by the demand forecasting technique used. Finally, supply chain networks, forecasting techniques, and their interactions are found to influence on- hand inventory levels and stockout rates for firms within the supply chain. Results also suggest that no one particular type of supply chain network dominates in terms of dampening the bullwhip effect, lowering on-hand inventory levels, or reducing stockout rates. The optimal network depends on the forecasting technique used and other supply chain factors. Keywords: bullwhip effect; inventory management; simulation; stockouts; supply chain network INTRODUCTION A supply chain with one manufacturer, one distributor, one wholesaler, and one retailer is normally used in the ‘‘Beer Game’’ to demonstrate the bullwhip effect. A criticism of the Beer Game, however, is its lack of realism as most supply chains have more than one member per echelon. Indeed, a question often arises from participants when the ‘‘Beer Game’’ is played: does adding more retailers to the supply chain improve or worsen the bullwhip effect? Although the vast majority of supply chains include more than one retai- ler, existing studies of the bullwhip effect are based mainly on a single retailer (Lee et al. 1997a; Chen et al. 2000a,b; Chandra and Grabis 2005; Zarandi et al. 2008). This repre- sents an interesting, but as yet overlooked, omission. The bullwhip effect describes the amplification of demand variation upstream in a supply chain (Lee et al. 1997a,b) and ‘‘has been viewed as one of the forces that paralyze supply chains’’ (Lee et al. 2004, 1888). In a supply chain, costs asso- ciated with the bullwhip effect are mainly from the resulting high inventory levels and stockout rates. High on-hand inventory levels lead to excessive inventory holding costs. For example, Cisco Systems, a worldwide leading supplier of networking equipment, suffered a $2.25 billion inventory write-off in the third quarter of 2001 due to the bullwhip effect (Barrette 2001). Stockouts may bring both tangible and intangible losses to firms. Tangible losses include pur- chases abandoned by customers. Intangible losses involve customer defection to other suppliers for current and future transactions (Zinn and Liu 2001, 2008). Due to the potential losses which may result from the bull- whip effect, many approaches are employed in industry to reduce costs associated with the bullwhip effect. For exam- ple, Cisco started its eHub program, which aims to reduce overamplified orders by providing smooth information flows of inventory and orders between Cisco and its suppliers (Barrette 2001). Hewlett-Packard tracks the variability of orders from its downstream customers to measure and con- trol the bullwhip effect (Lee et al. 1997b). Academic studies also propose various methods for mitigating the bullwhip effect, such as information sharing, accurate forecasting, and effective ordering (Lee et al. 2000; Croson and Donohue 2003; Sohn and Lim 2008; Wright and Yuan 2008). Literature related to order splitting implies that the under- lying network of the supply chain, especially the number of firms within an echelon, might be one factor affecting inven- tory and the bullwhip effect (Eppen 1979; Cohen and Lee 1989). Inserting additional retailers into a supply chain splits orders from end customers at the retailer echelon. While order splitting results in both lower demand variation and lower on-hand inventory levels (Sculli and Shum 1990; Hill 1996; Evers 1999; Thomas and Tyworth 2007), the link between supply chain networks and the bullwhip effect remains unresolved. Does a particular supply chain network dampen the bullwhip effect and thereby reduce on-hand inventory levels and stockout rates? If so, are the benefits of adjusting the network consistent over all firms in the supply chain? As various operational factors (such as the number of retailers and alternative forecasting and ordering polices) and their interactions are involved, these questions are exam- ined using a supply chain simulation. The results show that the bullwhip effect varies across different supply chain net- works. Moreover, the optimal supply chain network for miti- gating the bullwhip effect is found to depend on the demand forecasting technique used. Supply chain networks are also determined to have differing impacts on on-hand inventories and stockout rates of firms in the supply chain. The remainder of this article is organized as follows. The next section provides a literature review of various Corresponding author: Philip T. Evers, University of Maryland, 3435 Van Munching Hall, R. H. Smith School of Business, College Park, MD 20742, USA; E-mail: pevers@rhsmith.umd.edu Journal of Business Logistics, 2011, 32(1): 27–39 Ó Council of Supply Chain Management Professionals