The demand for a product will change as it moves through its lifecycle. EazyStock classifies all items into nine different demand patterns depending on their characteristics and maturity. The system detects any changes in the patterns over time and adjusts it automatically. Calculations are based on statistical forecasting algorithms, resulting in high precision forecasts based on data that is always up-to-date.
In cases when demand history cannot be used, for example at new product introductions, the user can manually adjust the forecast to ensure overall accuracy.
When a new product is replacing an existing one, the history of the existing item can often be used to calculate the demand of the new item. This can be managed with the item supersession functionality, a feature that maps demand and forecast history between the old and new product.
Seasonal products, campaigns and promotions can be tricky to manage. If you base forecasts of a seasonal product on the past period, you will most likely be way off the actual demand. Likewise, if you can’t cover the peak in demand during promotions, or purchase way too much, you will end up with either low service levels or excess stock.
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