Fix this 1 forecasting practice and improve your supply chain immediately and forever!

Challenge

Forecast Bias unnecessarily causes either excess inventory or shortages, depending upon in which direction the bias exists. This is the worst type of forecast inaccuracy because it is induced by management practices. Bias is when the forecast is consistently over or under actual demand. The other type of forecast inaccuracy is random variation, in which actual demand fluctuates above or below the forecast each month. Random variation is natural and cannot be eliminated, but it can be reduced through process improvements. Positive Bias: when forecast is consistently above actual demand. For example, at a large pharmaceutical company, they hadn’t differentiated their internal forecast from the one given to Wall Street. The commercial department did not want to make necessary reductions to their forecasts in case the news caused the stock price to tumble. Another cause of positive forecast bias is when the sales team does not trust the supply team to consistently deliver, so they submit aggressive forecasts to increase the likelihood that product will be available when required. In these cases, forecast bias will create excess inventory, and ultimately obsolescence or expired inventory. Since scrapped inventory is typically an important metric within the operations environment, they understand the game that is being played, and may choose to disregard the sales forecast, and create their own. Negative Bias: otherwise known as “sand-bagging”. In this case, forecasts are consistently lower than actual demand. Management often inadvertently encourages this behavior by setting quarterly milestones and bonus plans based upon sales forecasts, which pressures the sales team to submit lower forecasts. In this case, constant inventory shortages will occur, thereby impacting deliveries to customers. As you see, forecast bias is very disruptive, and the worst part is that it is completely within management’s control. Therefore, the target for forecast bias should be ZERO!

Solution

Do you know if there is bias in your forecasts? I won’t bore you with the details on how to calculate whether your forecast is biased, but feel free to call me if you have questions. Basically, you compare the forecasts to actual demand over a period of time, say 12 months, and see if there is a significant difference. How do you know if the difference is significant? We set tracking signals, which are the “guard-rails”. In the example below, the blue line is the cumulative difference between forecast and demand, and the red line are the tracking signals. As you can see, the blue line is certainly outside the “guard rails” and so bias does exist. In addition, the blue line is above the upper tracking signal denoting there is a positive bias. In this case, you would expect excess inventory.

Results

At a pharmaceutical client, positive forecast bias did exist. This was because the forecast given to supply chain was the same one that was given to Wall Street, and the Commercial team did not want to lower expectations and reduce the stock price. It was agreed that the remedy for this problem was to create an internal and external forecast. This took some time to implement, so the “quick fix” was to reduce the sales forecast by the bias percentage. In this case, the positive bias was calculated to be 5.7%, so in the short term forecasts were just reduced by this amount. Once processes and system enhancements had been completed, then the “2 forecast” solution was implemented. We calculated that this and several other improvements helped to increase forecast accuracy by 15%.