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.

Source: Global CPG organization, SAS paper Using Machine Learning and Demand Sensing to Enhance Short-Term Forecasting

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.

Source: SAS Paper Assisted Demand Planning Using Machine Learning for CPG and Retail