No matter what business you are in, there is always a need for future estimates to plan. It may be critical to examine how the precision and accuracy of a forecasting strategy affect other parts of your planning process. This perspective reveals how different forecasting strategies affect simulated safety inventory levels and replenishment. By examining how your planning system responds to similar tests, you might expose some deficiencies limiting your ability to meet inventory objectives.
The forecast carries a common expectation concerning revenue growth in most business areas. It’s easy to see why precision is relevant in such a case. For supply planning, the need for absolute precision may be slightly divergent.
The critical difference for supply planning is that a forecast is 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 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 operate 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 exactly matching 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 minimizes the knee-jerking effect that might otherwise result in inconsistent sales and lead times. However, a key assumption is that the more accurate you can predict sales, the less safety inventory you require. When you put
this assumption to the test, you may discover surprising results. Our dataset found that a forecast model that systematically chases demand may 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.