Predict consumer response to price changes and promotional activity in order to generate a demand forecast at the store level. You can 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.
Maximize stock coverage, minimize costs.
Improve forecast accuracy by analyzing 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 forecasts, but it's a core component of our 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.
Quantify consumer response to a change in price or a promotion so you can better plan and generate demand forecasts for future prices and promotions.
Forecast the effect on a whole category.
Advanced analytics helps you understand each item's relationship with other items in the category, and considers "cross-effects" when generating a demand forecast.
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.
Follow an easy upgrade path.
The system forecasts the impact of price and promotion changes, enables you to implement SAS Revenue Optimization Suite when you're ready without changing the underlying architecture.
- 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.
- Management by exception.
- Configuration workbench.
- Retail data model.