SAS® Retail Forecasting predicts consumer response to price changes and promotional activity in order to generate a demand forecast at the store level. It's specifically designed to forecast demand for long- and short-lifecycle retail products by considering critical causal factors – price, promotions and marketing activity – and analyzing the effects across the whole category. It's for use by trading and supply chain divisions affected by price and promotional activity.
With the help of SAS we have been able to reduce stockholding by at least eight percent and reduce wastage by three to four percent.
Manager, Branch Ordering Development
Maximize stock coverage while minimizing costs.
We help improve forecast accuracy by enabling the analysis of consumers' response to price, promotion, marketing and operational activities and their effect on demand. Price and promotions will affect demand more than any other single factor. Most retail forecasting solutions do not consider price a causal factor when generating their forecasts, but it's a core component of the SAS Retail Forecasting solution.
Increase inventory turns.
The net result of improving accuracy is more balanced inventory levels. If excess stock is removed from the supply chain, stock will move faster through it, increasing the number of times it is replenished. This means there is a shorter period between when the stock is ordered and sold, leading to fewer cash tie-ups. With a typical 30-day payment term, the stock could be sold before payment is due.
Model future prices and promotions.
If consumer response to a change in price or a promotion can be quantified, then future price and promotions can be planned and demand forecasted.
Forecast the effect on a whole category.
If you know how one item will react if a related item is promoted, you can calculate the net effect on category demand. We use advanced analytics to understand each item's relationship with other items in the category and consider "cross-effects" when generating a demand forecast for an item. SAS Retail Forecasting calculates the net change in demand of new items and creates a predicted demand curve.
Forecast space more accurately.
Having a better understanding of demand means that it can be used as an input to the space management function in order to calculate ROS, stock holding, turns, safety stock and, therefore, required space.
Create a quick return on investment.
Faster implementation due to the predefined retail-specific models and ETL data model and methodoloogy.
- Demand forecasting at store-SKU level
- Consumer response to price and promotion
- Cross-effects identification
- New product forecasting
- Intermittent or slow-moving item identification
- Lost sales forecasting
- Output to replenishment system
- Manage by exception
- Configuration workbench
- Retail data model