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Supply & Demand Planning

Achieve profitability while meeting omnichannel customer demand, without over- or under-stocking.

How SAS Delivers Supply & Demand Planning

SAS provides a highly automated, fully integrated supply and demand planning solution that breaks down traditional barriers between planning steps, transforming planning into a continuous, flexible process.      

Small-, medium- or large-scale forecasting

  • Automatically generate statistically driven, weighted consensus forecasts.
  • Monitor forecast performance to understand value added or lost at each step.
  • Use time-series forecasting to build models that reflect your business realities.

Demand sensing & shaping

  • Visually analyze demand data to spot patterns and insights related to sales, shipments, pricing, promotions, etc.
  • Evaluate sales history and plan for future events – new products, locations, channel introduction – using what-if scenario analysis.
  • Measure the effect of sales and marketing strategies on consumer demand using multitiered causal analysis.   

Inventory optimization

  • Gain near-real-time insight into supply and demand dynamics so you can avoid under- or over-stocking.
  • Calculate optimal inventory policies using multiechelon optimization with state-of-the-art simulation.
  • Use predictive modeling and what-if analysis to find out how different variables will affect the supply/demand balance.

Why SAS for supply & demand planning?

Trust the accuracy of your forecasts at scale and in detail. Collaborate internally and with suppliers to plan promotions and price changes. Avoid lost sales, out-of-stocks and missed opportunities resulting from inaccurate forecasts.    

Improve forecasting accuracy

Model and forecast all your products and selling locations with a patented statistical forecasting engine that uses advanced forecasting methods.

Optimize inventory and reduce stockouts

Use analytics to drive product where it's needed most to meet customer demand while managing the overall costs of inventory.

Take the guesswork out of forecasting

Use retail forecasting models that include underlying trends, seasonality, promotions, etc. Automated, large-scale forecasting selects the best-fit model based on individual time series.

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