Abstract: A system and method for calculating demand forecasts is presented. Sales data for a set of SKUs is received. The sales data is filtered to contain only data for low-selling SKUs. A set of clusters of SKUs is created. A generalized dynamic linear model for use with each cluster in the set of clusters is generated. A set of random data points is generated. The dynamic linear model is fitted at each data point in the set of random data points using a Monte Carlo method. This fitting can be performed using an unscented Kalman filter method. Calculating a forecast for sales based on the fitting at each data point. Using the forecast for sales, inventory is ordered. Other embodiments are also disclosed herein.

Abstract: A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.

Abstract: A system and method for calculating demand forecasts is presented. Sales data for a set of SKUs is received. The sales data is filtered to contain only data for low-selling SKUs. A set of clusters of SKUs is created. A generalized dynamic linear model for use with each cluster in the set of clusters is generated. A set of random data points is generated. The dynamic linear model is fitted at each data point in the set of random data points using a Monte Carlo method. This fitting can be performed using an unscented Kalman filter method. Calculating a forecast for sales based on the fitting at each data point. Using the forecast for sales, inventory is ordered. Other embodiments are also disclosed herein.

Abstract: A system and method for post-processing demand forecasts is presented. A forecast for a set of stock keeping units (SKUs) is received. Thereafter, a series of adjustments is are performed on the forecast. One adjustment involves determining how often orders are fulfilled by a drop ship vendor and adjusting a forecast to adjust for the fact that a portion of the forecast will never need to be ordered and stored at the retailer's warehouses. Another adjustments involves determining if there is a parent SKU the contains multiple child SKUs and adjusting accordingly. Another adjustment involves determining in there are any bundle SKUs that could be added to an individual SKU's forecast. Another adjustment involves adjusting a forecast based on a special buy. Another adjustment involves removing possibly dead items. Other embodiments are also disclosed herein.

Abstract: A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.