Bullwhip effect

The bullwhip effect is a distribution channel phenomenon in which forecasts yield supply chain inefficiencies. It refers to increasing swings in inventory in response to shifts in customer demand as one moves further up the supply chain. The concept first appeared in Jay Forrester's Industrial Dynamics (1961) and thus it is also known as the Forrester effect. The bullwhip effect was named for the way the amplitude of a whip increases down its length. The further from the originating signal, the greater the distortion of the wave pattern. In a similar manner, forecast accuracy decreases as one moves upstream along the supply chain. For example, many consumer goods have fairly consistent consumption at retail. But this signal becomes more chaotic and unpredictable as you move away from consumer purchasing behavior.

In the 1990s, Hau Lee, a Professor of Engineering and Management Science at Stanford University, helped incorporate the concept into supply chain vernacular using a story about Volvo. Suffering a glut in green cars, sales and marketing developed a program to move the excess inventory. While successful in generating the desired market pull, manufacturing did not know about the promotional plans. Instead, they read the increase in sales as an indication of growing demand for green cars and ramped up production.

Research indicates a fluctuation in point-of-sale demand of +/- five percent will be interpreted by supply chain participants as a change in demand of up to +/- forty percent. Much like cracking a whip, a small flick of the wrist (a shift in point of sale demand) can cause a large motion at the end of the whip (manufacturer's response).

Because customer demand is rarely perfectly stable, businesses must forecast demand to properly position inventory and other resources. Forecasts are based on statistics, and they are rarely perfectly accurate. Because forecast errors are given, companies often carry an inventory buffer called "".

Moving up the supply chain from raw materials supplier to end-consumer, each supply chain participant has greater observed variation in demand and thus greater need for . In periods of rising demand, down-stream participants increase orders. In periods of falling demand, orders fall or stop, thereby not reducing inventory. The effect is that variations are amplified as one moves upstream in the supply chain (further from the customer). This sequence of events is well simulated by the beer distribution game which was developed by MIT Sloan School of Management in the 1960s.

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