Managing the Bullwhip Effect in Multi-Echelon Supply Chains

Abstract

The variability of replenishment orders tends to increase as one moves up the supply chain, causing the bullwhip effect (Figure 1). The presence of the bullwhip effect in supply chains leads to severe consequences such as stock outs, low service level, and extra transportation and capacity costs. Lee et al. [1] identified five operational causes of the bullwhip effect: demand signal processing, lead-time, order batching, price fluctuations and rationing and shortage gaming. Extensive modeling studies have quantified the impact of these causes utilizing three modeling approaches: simulation modeling [2-6], statistical modeling [7,8], and control theoretic [9-11]. The results of these modeling studies have shown that the bullwhip effect can be managed with selecting the proper forecasting method and ordering policy [2,7-10], reducing the lead-time [7-10] and increasing the collaboration level [2,5-7,11-14]. Specifically, collaboration in supply chains and smoothing replenishment rules have been the most suggested approaches for mitigating/avoiding the bullwhip effect (Figure 1). Smoothing Replenishment Rules Inventory replenishment policies have been recognized as a major cause of the bullwhip effect and thus it has received a significant attention in the bullwhip effect studies [9,11]. However, the majority of the bullwhip effect studies have been considering the periodic review order-up-to (R, S) policy (OUT policy) motivated by its popularity in practice and research [2,7-13]. In this policy, at the end of each review period, the replenishment order is generated to recover the entire gap between target and available levels of inventory position (net inventory + supply line inventory) [11]. Many studies have quantified the bullwhip effect in supply chain models employ the OUT ordering policy proving that the bullwhip effect is guaranteed when this policy is employed irrespective of the forecasting method used and without making any assumptions about the demand process [7-13]. For avoiding the bullwhip effect, extensive research has been focusing on developing and evaluating smoothing replenishment rules that are modified from the traditional periodic review OUT policy with adding proportional controllers to regulate the reaction to demand changes [11]. The proportional controllers of the smoothing OUT replenishment rule are smoothing terms for the gaps between target and current levels of net inventory and supply line inventory. Specifically, in traditional OUT, the order is generated to recover the entire gaps while in smoothing OUT only a fraction of each gap is Abstract This editorial article presents the bullwhip effect which is one of the major problems faced by supply chain management. The bullwhip effect represents the demand variability amplification as demand information travels upstream in the supply chain. The bullwhip effect research has been attempting to prove its existence, identify its causes, quantify its magnitude and propose mitigation and avoidance solutions. Previous research has relied on different modeling approaches to quantify the bullwhip effect and to investigate the proposed mitigation/avoidance solutions. Extensive research has shown that smoothing replenishment rules and collaboration in supply chain are the most powerful approaches to counteract the bullwhip effect. The objective of this article is to highlight the bullwhip effect avoidance approaches with providing some interesting directions for future research. recovered. This research stream has shown that the proper tuning of the proportional controllers can eliminate the bullwhip effect [9,11]. However, some studies have shown that dampening the bullwhip effect largely may increase the inventory variance and thus affecting customer service level [9,11,15]. The available smoothing replenishment rules in literature are mainly based on the traditional OUT replenishment rule. Most recently, Costantino et al. [9] have proposed an alternative smoothing inventory replenishment system that relies on control charts and compared to traditional and smoothing OUT ordering policies under different operational settings. This innovative smoothing approach has shown a promising performance in terms of bullwhip effect elimination and inventory stability. Furthermore, it can be applied easily by each supply chain echelon based on the available historical data of incoming orders to each echelon. However, further research is still required in this new research direction [9].