Empirical Assessment of Bullwhip Effect in Supply Networks

Introduction

Supply chain management has been an area of intense inquiry in Operations Management in recent years. The benefits of efficient and effective supply chains are well documented by ample research (Cao & Zhang, 2011; Ralston, Blackhurst, Cantor, & Crum, 2015; Stank, Keller, & Closs, 2001) and these benefits can be summed up as improvements to lead time, customer services, cost, and quality (Kainuma & Tawara, 2006). Many studies have analyzed demand variability, motivated by the observations of the Bullwhip Effect, also known as “demand amplification”, due to its devastating effect to an organization (Hussain & Drake, 2011).

Bullwhip effect is the amplification of demand/order variability when we move upstream along a supply chain. Since Lee, Padmanabhan, and Whang (1997), extensive studies have been conducted to investigate bullwhip effect in supply chains (Giard & Sali, 2013; Miragliotta, 2006; Wang & Disney, 2016). While analytical studies have contributed useful theory to addressing supply chain Bullwhip Effect issues (e.g., Chen, Drezner, Ryan, and Simchi-Levi (2000), Cachon (1999)), a few researchers have analyzed demand variability and the existence of bullwhip effect empirically. In their seminal paper, Cachon, Randall, and Schmidt (2007) investigate the bullwhip effect in a panel data containing 75 industries in manufacturing, wholesale, and the retail sectors. According to their analysis, 61 industries exhibit bullwhip effects.

In another study, Bray and Mendelson (2012) look at the bullwhip effect at the firm level, by decomposing demand signal into a series of signals with different lead time. They determined that about 65% of firms exhibit bullwhip effects. Shan, Yang, Yang, and Zhang (2014) study bullwhip effect in China and they find that more than 2/3 of the firms in their sample exhibit bullwhip effect. Bray, Yao, Duan, and Huo (2019) show that rationing game can be triggered by a supply shock rather than a demand shock and contributes significantly to bullwhip effect.

This paper differs from previous studies in several ways. First, the goal in the paper is not to test whether bullwhip effect exists (Wang & Disney, 2016). Instead, we focus on how customer industries’ demand variability is transmitted upstream, and affects supplier industries’ demand/production variability, and how this transmission is influenced by the structure of supplier-customer network.

Second, in real world supplier-customer relationship can be best described by a network structure in which a focal firm or industry is connected to many suppliers and many customers. However, this network structure remains largely unexplored (Giard & Sali, 2013) due to its complexity and most of the studies in the literature either ignore supplier-customer relationship or model this relationship as a simple link between one supplier and one customer. For example, in Cachon et al. (2007) and Shan et al. (2014) an industry or a firm exhibits bullwhip effect if its own production variability is higher than its own demand variability. On the other hand, many analytical papers focus on a serial/chain structure in which each firm or industry is linked to one supplier and one customer (Giard & Sali, 2013; Teunter, Babai, Bokhorst, & Syntetos, 2018). In other words, the role of supply chain network is omitted in the vast majority of papers in the literature. However, in reality firms and industries are connected through supply chain networks, essentially a supply web (Fransoo & Wouters, 2000). Moreover, bullwhip effect itself is about the amplification of demand variance through supplier-customer network. Therefore, an empirical study that directly investigates how demand/production variability is transmitted from downstream to upstream along the supply chain network is desirable, which is the focus of this study.