The BullWhip Effect

The Accelerated Analytics team published a white-paper recently titled “The Business Case for Supply Chain Collaboration.”  This white-paper included a discussion on the bullwhip effect which has received a great deal of positive feedback so we thought we would include an expert….

The basic premise of the demand driven collaborative supply chain model is that in order to achieve the highest possible in-stock and simultaneously minimize waste, all parties within the supply chain must have timely access to actual dales demand data and all parties must have a means by which they can work together to coordinate promotions and business planning.

A forecast by definition is an estimate made in advance of an event occurring and is therefore an educated guess.  Unfortunately, even sophisticated forecasting software can have an error rate of 50% on promoted items.  Most troubling of all, forecast accuracy decreases moving backward into the supply chain.

The reason forecast accuracy decreases moving backward in the supply chain can be illustrated by plotting sales over time for supply chain participants as depicted in chart 1.   Notice the actual sales demand recorded at the retail point of saleshad moderate variation.  This is because the retailer builds their forecast model using actual demand as tracked through their point of sales systems.  But notice how much more chaotic and unpredictable the demand curve becomes as you move away from the actual point of sale.  Demand as viewed by the supplier, wholesaler, and manufacturer is based on estimated sales which combined with latency and manually adjusted “safety stock” causes increasingly inaccurate and chaotic forecasts.  Research indicates fluctuation in actual customer demand of +/- 5% can be interpreted by supply chain participants as a change in demand of up to +/- 40%.  As depicted in chart 1, although actual demand has only changed +/-5% the reaction of supply chain participants is dramatically exaggerated and is known as the bullwhip effect.   Much like cracking a whip the user only needs a small motion in their wrist (point of sale) to cause a huge motion in the end of the whip (manufacturer).

The bullwhip effect causes inaccurate forecasts, inefficiency, and waste within the supply chain.  Anytime the forecast line of a supply chain participant (e.g. chart 1, wholesaler, manufacturer, or supplier) is above or below the actual demand line (e.g. chart 1; retail sales) inventory levels are not optimized and out of stocks or inventory build-up will occur.  The U.S. Department of Commerce estimates $3 trillion in excess inventory is locked in the U.S. and European supply chains.   The bullwhip effect is exacerbated by the parties in the supply chain who do not have an accurate understanding of actual demand.  In other words, they are forecasting but the inputs upon which their forecast model is built are inaccurate.

Accurate forecasting and close coordination between supply chain partners can help to eliminate the bullwhip effect and dramatically increase overall profitability.  The most effective way to improve forecast accuracy at each step in the supply chain is to base the forecast on actual sales demand data.  In this way, each point in the supply chain can be demand driven and the parties can collaborate on the same forecast inputs.  In addition, promotions can be coordinated and managed to maximize sell-thru without causing supply disruptions.  As information quality improves, cycle times are compressed through the entire supply chain process.

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