The last couple of months the topic that pops up in many of the conversations I have is forecasting. Last week a beer brewer, this week a mail company was asking about it. Recently the Dutch financial paper had a full page on the value of forecasting for DSM NeoResins. The article (in Dutch) explains why forecasting the demand and the resulting stock impact were crucial for DSM to understand the dynamics of their supply chain and helped to manage the impact of the economic downturn. DSM experienced the classic bullwhip effect, a well known phenomenon in supply chain management, and was looking for ways to tame it.
The bullwhip effect is the amplification of demand fluctuations as one moves up the supply chain from retailer to manufacturer. It can be measured as the variance of the orders divided by the variance of demand. A bullwhip measure larger that one implies that demand fluctuations will be amplified. The bullwhip has three main causes. First there is the supply chain structure itself. The longer the lead time in the supply chain the stronger the bullwhip effect, because a longer lead time will cause more pronounced orders as a reaction to demand increases. Second cause is local optimisation. Since placing an order will involve cost there is an incentive to hold orders back and only place aggregate orders. Last but not least is the lack of information of actual demand. Without actual demand data one has to rely on forecasted demand. When applied without thought, forecasting will aggravated the bullwhip effect, leading to forecasted chaos.
To illustrate the impact of a “nonoptimal” forecasting method picture a simple supply chain, for example the supply chain from the Beer Distribution Game. Each point in time the following sequence of events takes place in each part of the supply chain. Incoming shipments from an upstream decision maker are received and placed in inventory. Next incoming orders from the downstream decision maker are received and are either fulfilled (when stock levels suffice) or backlogged. Last, a new order is placed and passed to the upstream echelon. Inventory is reviewed each time period. In deciding the order quantity we have to estimate future demand. To be able to forecast we need to have a forecast method. The figure below shows the results of a Moving Average (MA) and Exponential Smoothing (ES) method to forecast future demand. Compared to the actual demand one can clearly see that ES forecaster gives better results.
To illustrate the impact of forecasting on stocks and order quantity, assume that it takes 3 periods before an order will be received. This needs to be taken into account when placing an order. The below figure shows actual demand compared with the order quantity based on MA and ES demand forecasts. As can be seen clearly the fluctuation in demand is amplified in the orders, illustrating the bullwhip effect.
The amplification for the ES forecasts is about twice the amplification from the MA demands. So although SE gives better forecasts it amplifies the fluctuations in the demand more than the simple MA forecasting method in this case. Not something you would expect. When comparing net stock with actual demand a similar picture arises.
So, when attempting to forecast be aware of the chaos it can create. Improper forecasting may have a devastating impact on the bull whip effect. As a consequence inventory cost will increase and customer service will be impacted significantly. Best way to start is by selecting a few forecasting methods and order policies and test the accuracy of the forecasts on past periods. Using the mean squared error of the forecasts as a goodness of forecast measure the best forecast method can be selected. One can also use the bullwhip effect measure in selecting the forecast method.
PS: If you would like to have the data from the above examples, just drop me a note.
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