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Bullwhip effect

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Form of distribution marketing

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Illustration of the bullwhip effect: the final customer places an order (whip), which increasingly distorts interpretations of demand as one proceeds upstream along the supply chain.<br>The bullwhip effect is a supply chain phenomenon where orders to suppliers tend to have a larger variability than sales to buyers, which results in an amplified demand variability upstream. In part, this results in increasing swings in inventory in response to shifts in consumer demand as one moves further up the supply chain. The concept first appeared in Jay Forrester's Industrial Dynamics (1961)[1] and thus it is also known as the Forrester effect . It has been described as "the observed propensity for material orders to be more variable than demand signals and for this variability to increase the further upstream a company is in a supply chain".[2]

Research 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 sell 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.[3]

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 - manufacturers' responses.[4]

Causes<br>[edit]

Bullwhip effect<br>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 expected, companies often carry an inventory buffer called "safety stock".

Moving up the supply chain from end-consumer to raw materials supplier, each supply chain participant has greater observed variation in demand and thus greater need for safety stock. 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 that was developed by MIT Sloan School of Management in the 1960s.

Disorganisation

Lack of communication

Free return policies

Order batching

Price variations

Demand information

Simply human greed and exaggeration

The causes can further be divided into behavioral and operational causes.

Behavioral causes<br>[edit]

The first theories focusing onto the bullwhip effect were mainly focusing on the irrational behavior of the human in the supply chain, highlighting them as the main cause of the bullwhip effect. Since the 90's, the studies evolved, placing the supply chain's misfunctioning at the heart of their studies abandoning the human factors.[5]

Previous control-theoretic models have identified as causes the tradeoff between stationary and dynamic performance[6] as well as the use of independent controllers.[7] In accordance with Dellaert et al. (2017),[8] one of the main behavioral causes that contribute to the bullwhip effect is the under-estimation of the pipeline.[9] In addition, the complementary bias, over-estimation of the pipeline, also has a negative effect under such conditions. Nevertheless, it has been shown that when the demand stream is stationary, the system is relatively robust to this bias. In such situations, it has been found that biased policies (both under-estimating and over-estimating the pipeline) perform just as well as unbiased policies.

Some others behavioral causes can be highlighted:

Misuse of base-stock policies

Mis-perceptions of feedback and time delays. In 1979, Buffa and Miller highlighted that in their example. If a retailer sees a permanent drop of 10% of the demand on day 1, he will not place a new order until day 10. That way, the wholesaler is going to notice the 10% drop at day 10 and will place his order on day 20. The longer the supply chain is, the bigger this delay...