Advanced Analytics Forecasting

WHITE PAPER

The time of Artificial Intelligence and Machine Learning is now. But how can these tech developments be used in a real-world planning environment?

We’d like to take a moment to describe how AI is used in the Vanguard Predictive Planning platform, and what benefits it brings to the supply chain that uses it.

Forecast Accuracy

If you do not know the full benefits of a more accurate forecast, first read the whitepaper, “Why it All Comes Back to Forecast Accuracy.”  The bottom line is that forecasts that are more accurate lead to reduced inventory costs, improved fulfillment rates, better customer satisfaction, and more profit.

The benefits are apparent, but forecast accuracy can be tricky.  Choosing the right forecast method is an important step toward getting it right.

AI-Enabled “Best-Fit” Forecast Method Selection

The forecasting method you select is a function of multiple qualities about your item.  Is demand steady, cyclical, or sporadic?  Are there seasonal trends?  Are trends strong or limited?  Is the item new?  Each item being forecast has a unique history (and future), and therefore an optimal method.  A method that accurately forecasts one data set might prove inaccurate for another.

Vanguard Predictive Planning uses AI to run a “Best-Fit” analysis on forecast records at the beginning of each forecast cycle.  The AI engine automatically uses the most appropriate forecasting method for your record.  The best-fit analysis looks at the most recent demand data available and ensures the most accurate forecast for every item as it progresses through its lifecycle.  Forecasts that need attention are flagged for review by a planner. 

 

Advanced Analytics Forecasting

WHITE PAPER

About Vanguard Software

Vanguard Software introduced its first product for decision support analysis in 1995. Today, companies across every major industry and more than 60 countries rely on the Vanguard Predictive Planning platform. Vanguard Software is based in Cary, North Carolina.

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