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Protecting service levels with forecasts

Forecasts provide essential data for operating in today’s volatile environments by offering valuable insights into future events. However, in a VUCA world, unforeseen events will inevitably alter our predictions. The question is not if disruptions will occur, but when they will happen. Companies must be prepared for these challenges and use their tools accordingly.

Bad practices

Many companies base their supply and operations planning on creating connections and dependencies. Holding large amounts of stock is detrimental due to the financial costs and the negative impact on overall organizational performance. Therefore, their goal is to order only what is needed, when it is needed.

However, the effectiveness of this planning approach is threatened by one factor: variability. This method intrinsically hinders the ability to adapt to demand and supply fluctuations that can, and eventually will, occur.

How to use forecasts wisely

The implementation of forecasts cannot be approached in the same manner, as they also face variability and its accuracy can be impacted by unforeseen events. Therefore, companies must be prepared for these scenarios. Instead of generating replenishment orders exactly to cover for the most probable forecasted demand number, it is a better practice to consider the range of possibilities forecasts provide - what is called "confidence interval" in statistics.

The range provided by predictions is placed around the most likely outcome (called "mean estimate"):

The lower limit indicates the worst-case scenario, suggesting that with all available data, real demand will not fall below this amount.

Conversely, the upper limit represents the best-case scenario, indicating that the forecasting model does not anticipate demand exceeding these values.

With that information, a stock buffer can be created. The inventory should contain enough units to avoid a stock breakage if the best-case scenario finally occurs. On the other extreme, if demand follows the expected minimum, the only remaining inventory will be the difference of the amounts between the best and worst-case scenarios.
These scenarios are extreme, since they represent the limits of the expected future. Usually, real demand will fall somewhere between them. Actually, if forecasts are accurately performed, the average inventory surplus should be the difference between the mean estimate and the worst-case scenario. By following these data, inventories will contain minimal stock amounts while avoiding stock-outs and ensuring service levels.

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