The Bullwhip Effect in Supply Chains Işıl Tuğrul

Outline zDefinition zLiterature Review zFuture Work

Definition yThe increase in demand variability as we move up in the supply chain is referred to as the bullwhip effect.

Why important? zIt is important to understand the effect and take necessary actions to reduce its detrimental impacts yexcessive inventory yinefficient utilization of capacity ypoor customer service yexcess raw materials cost yexcess manufacturing and warehousing expenses yadditional transportation costs

Literature Review zForrester (1961) initiated the analysis of the demand variability amplification and pointed out that it is a consequence of industrial dynamics or time varying behaviors of industrial organizations. zAccording to Forrester’s effect, or the “acceleration principle”, a 10 percent change in the rate of sale at the retail level can result in up to a 40 percent change in demand for the manufacturer. zRemedy for this effect is to understand the system as a whole and to make modifications in behavioral practice.

Literature Review zJohn Sterman (1989) described a classroom game known as the Beer Game where participants simulate a supply chain. zAs the game proceeds, a small change in consumer demand is turned into wild swings in both orders and inventory upstream. zSterman attributed this amplified order variability to players’ irrational behavior or misconceptions about inventory and demand information. The players in the supply chain completely ignore the pipeline inventory when they are making their ordering decisions. zThey failed to account for the long time lags between placing and receiving orders and end up with poor decisions.

Literature Review zRichard Metters (1997) conducted a study to determine the significance of the detrimental effect of the amplified demand variability on profitability. zTwo distinct experimental designs are considered: ya) seasonality is induced month by month on an annual basis caused by incorrect demand updating and forward buying yb) seasonality is induced week by week on a monthly basis caused by order batching zProfitability is examined under heavy, moderate and no demand seasonality. zIt is concluded that eliminating the bullwhip effect can increase product profitability by 10-30%, and the potential profit increases from dampening the monthly seasonal changes outweigh those that are associated with weekly seasonality.

Literature Review zLee et al. (1997) have proposed four sources of the bullwhip effect - demand signal processing, rationing game, order batching and price variations. zSimple mathematical models are developed to demonstrate that the amplified order variability is an outcome of the rational and optimizing behavior of the supply chain members. zStrategies that can be implemented to reduce the distortion are also discussed. (e.g. avoid multiple demand forecasts updates, eliminate gaming in shortage situations, break order batches, stabilize prices)

Literature Review zChen et al. (2000) focused on determining the impact of demand forecasting on the bullwhip effect and quantifying the increase in variability at each stage of the supply chain. zThe variance of the orders placed by the retailer relative to the variance of the demand faced by the retailer is determined. yThe smoother the demand forecasts, the smaller the increase in variability. yWith longer lead times, the increase in variability is larger. yFor   0, the larger , the smaller the increase in variability.

Literature Review zChen et al. (2000) also analyzed the impact of centralized customer demand information on the bullwhip effect. zIt is demonstrated that centralizing the demand information will certainly reduce the magnitude of the bullwhip effect, but it will not completely eliminate the increase in variability.

Literature Review zDejonckheere et al. (2002) analyzed the bullwhip effect induced by forecasting algorithms in order-up-to policies and suggested a new general replenishment rule that can reduce variance amplification significantly. zOrder-up-to policies whose order-up-to levels will be updated by means of exponential smoothing, moving averages and demand signal processing are compared. zIn order-up-to systems, the bullwhip effect is guaranteed when forecasting is necessary. zBullwhip generated by moving average forecasting in order- up-to model is much less than that generated by exponential forecasts and demand signal processing.

Literature Review zA general replenishment rule capable of smoothing ordering patterns, even when demand has to be forecasted is proposed. zThe crucial difference with the order-up-to policies is that net stock and on order inventory discrepancies are only fractionally taken into account.

Future Work zComparative analysis of proposed strategies to mitigate the impact of the bullwhip effect zThe possible problems in implementing the suggested solutions of the bullwhip effect zBenefits of the bullwhip reducing strategies for the retailer

Questions & Answers