See clearly. No matter the conditions.

SAS® Forecasting

Whether it’s a time of sustained growth or widespread volatility, forecasts are essential to knowing where to go and how to get there.

Forecasting underpins operational efficiency, financial performance and customer understanding. From capacity planning to cash flow to risk management to capital expenditure – the need for leaders to predict the future has only increased.

And with the pressure to produce more accurate forecasts – and more of them – AI capabilities enable organizations to automate large-scale time series analyses. This leads to easier, faster and more effective forecasts.

Explore the resources below to learn how to improve your forecasting so that you’re ready for whatever comes your way.


Forecasting in Action

  • 71 fewer hours

    With SAS Forecasting, monthly forecasts took just one hour to produce – not three days.

    “Managing the needs of our market is a permanent challenge for our business, and SAS is instrumental in this.”

    Source: SAS customer, an African energy company

  • 20% reduction in trips

    With SAS Forecasting, one bank achieved a 20% reduction in trips to replenish its ATMs.

    “We are now able to convert valuable ATM and customer data into a daily execution plan, allowing optimal reloading at non-peak times.”

    Source: SAS customer, a multinational banking and financial services corporation

  • 35% more accuracy

    With SAS Forecasting, simulate the effect of price adjustments, marketing activity and changing economic conditions on future sales.

    “We are able to forecast demand at a monthly level for each product to bring a greater understanding of trends in our market.”

    Source: SAS customer, a global beverage brand

  • 20% fewer errors

    With SAS Forecasting, automatically choose the best model using a combination of traditional time series and machine learning methods.

    This approach demonstrated a significant improvement in forecast accuracy, with further incremental improvement using point-of-sale and inventory data.

  • 47% fewer manual overrides

    With SAS Forecasting, machine learning identifies which statistical forecasts should be manually adjusted.

    Demand planners are guided to the forecasts that need adjustment, leading to higher forecast accuracy and less time wasted.