No matter what business you are in, there is always a need for future estimates in order to plan. It may be critical to take a closer examination of how precision and accuracy of a forecasting strategy affect other parts of your planning process. This perspective reveals observations of how different forecasting strategies affect simulated safety inventory levels and replenishment. By examining how your own planning system responds to similar tests, you might expose some deficiencies that are limiting your ability to meet inventory objectives.
In most areas of business, the forecast carries a common expectation concerning revenue growth. It’s easy to see why precision is relevant in such case. For the purpose of supply planning, the need for absolute precision may be slightly divergent.
The critical difference for supply planning is that a forecast serves as one of several inputs for making decisions like how much to make or buy. Additional inputs like lot sizes, safety inventory, and lead time, help to stabilize and buffer supply plans against rapid changes caused by fluctuating customer demands. Without these additional inputs, manufacturing may experience significant challenges in meeting customer expectations on time. Trying to match supply with demand may result in operating less efficiently if a company lacks the flexibility to respond rapidly.
Of these additional inputs, safety inventory is the most conveniently manageable. To overcome the challenge of not being able to exactly match supply with the rate, magnitude, and timing of demand, companies will often “decouple” or “buffer” the relationship between demand and supply with safety stock. Safety inventory is used to minimize the knee-jerking effect that might otherwise result when sales and lead-times are inconsistent. However, a key assumption is that the more accurate you are able to predict sales, the less safety inventory you require. When you put this assumption to the test, you may discover surprising results. In our dataset, we found that a forecast model that systematically chases demand may actually produce a more nervous demand signal and potentially higher safety inventory.
Although it should be noted that the simulation was limited to less than 10 examples, and by no means constitutes an academic observation, we would highly recommend testing this assumption yourself on your planning systems. In the simulation, different methods to determine safety stock levels were used. While one method used the difference between forecast and actual sales to determine safety stock, the other method compared actual sales to normalized actual sales. Both methods were used in the simulation respective to a given forecast strategy.