Demand forecasting augmented by machine learning helps you better meet customer expectations with reduced inventory investment.
Most companies view seasonality as a pattern of demand that has regular and predictable change occurring every calendar year, like beer sales rising in the midsummer months, and cold medicine in the winter.
This foundation has been the basis for automated replenishment systems for many years, but like everything in supply chain today, it’s more complex than that, and the traditional replenishment approach has a lot of “noise” in how it’s calculated.
Photo by Marina Ryazantseva
Weather-dependent seasons really are about how far from the normal temperatures each day is. Rain can impact consumer demand at retailers. These deviations from the norm can cause a lot of noise that makes its way into the seasonality index.
And to further complicate the approach, seasonality is not a global value, but applies to very specific locations and each location can have differing indexes for individual items or groups of items. Other causal factors are such things as media events that might focus attention on a specific product or category.
Challenges of Seasonal Demand
One difficulty of using a demand pattern from year to year is that the demand is a sum of variables occurring daily. Another difficulty is that, of course, not all seasons start and end on specific dates. Demand can shift over time to trend higher or lower based on other items, markets, and media events. Finding the basis of a proper seasonality index is further hampered by other causal effects such as patterns of weather. The ultimate impact of a good–or poor–understanding of seasonality is on finding optimal inventory levels to balance cost and service. The root cause of this anomaly is that most demand forecasting systems look at the historical aggregate demand instead of smoothed demand potential as their basis. Aggregate demand has the noise of causal events. One example is demand influenced by weather.
Photo by Marina Ryazantseva
Weather-dependent seasons really are about how far from the normal temperatures each day is. Rain can impact consumer demand at retailers. These deviations from the norm can cause a lot of noise that makes its way into the seasonality index.
And to further complicate the approach, seasonality is not a global value, but applies to very specific locations and each location can have differing indexes for individual items or groups of items. Other causal factors are such things as media events that might focus attention on a specific product or category.
