The bullwhip effect (BWE) describes a phenomenon that involves the increasing amplification of demand variability along a supply chain (SC) The BWE has been a subject that has received continuous attention from researchers over the past 15 years and is a concern for SC managers because it is a major cause of efficiency and effectiveness loss in SCs Information sharing between actors in an SC is usually considered to be one of the primary means to minimise the BWE Approximately 50 articles published in major journals on these topics are studied in this article An analytical framework is used to highlight the contingent character of the conclusions proposed by the authors In this review, we identify the existence of significant gaps in the literature, especially concerning the BWE when it occurs in the productive part of the SC

The Bullwhip Effect in the Supply Chain: a study of contingent and incomplete literature

Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.

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Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain

Bullwhip effect reduces the efficiency, responsiveness, and value of the supply chain. There are some indirect causes like lead time, the number of echelons, and some direct causes of bullwhip effect such as rationing or price variation. Due to capacity constraints, retailers are forced to experience rationing of their demands. Fear of rationing usually gives rise to manipulable demand and hence increases the bullwhip effect. Moreover, if the retailer’s demand is price sensitive then it will cause price variation. The offerings of premium payment by retailers due to unfulfilled demand lure the supplier to extend his existing capacity and to allocate them more supply. In this paper, an attempt has been made to mitigate the impact of the bullwhip effect using a premium payment scheme. A technique has been coined that will help in reducing the bullwhip effect. The increased value of the supply chain on using a premium payment scheme is proof of the reduction of the bullwhip effect. Results are validated through numerical analysis.

Impact of Price-Sensitive Demand and Premium Payment Scheme on Bullwhip Effect

This chapter is devoted to present the evolution of the supply chains as the main structure for the production and distribution of goods to the final customers. The evolution of supply chains, as well as the factors influencing their growth are discussed in the chapter, together with some definitions and examples. An informal definition of a supply chain and their elements is also given. Next, the bullwhip effect is introduced as the principal factor affecting the dynamic performance of the supply chain, together with some figures showing its influence in terms of the increase of the supply chain costs across sectors and countries. We then present some of the causes of the bullwhip effect are introduced, along with the main barriers encountered to overcome it. Next, we discuss the need of models capable to capture the dynamics of the supply chain to better understand and reduce the bullwhip effect. The chapter concludes with a summary of the concepts and ideas presented and with the suggestion of several references for further reading.

Introduction to Supply Chains

Previous empirical studies on bullwhip effects treat each industry or firm as isolated from its supply chain network. In this paper, the authors are interested in the role played by supply chain relational connection in moderating how demand variability signal is transmitted upstream. The paper conducts an empirical study based on a panel data of 55 manufacturing industries and 9 wholesale industries. The regression analysis shows that demand variability is propagated through supply chain upward and the transmission is influenced by the structural relationship between suppliers and customers, which is measured by customer-base concentration and customer interconnectedness. On the other hand, customer demand variability has a greater impact on industries with less concentrated customer base or with less interconnected customers.

Joe Teng

Papers

Empirical Assessment of Bullwhip Effect in Supply Networks