Seven methods that improve forecasting accuracy

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According to Gartner, companies that excel at demand forecasting average 15% less inventory, 17% stronger perfect order fulfilment , 35% shorter cash-to-cash cycle times, and one-tenth as many stock-outs as their peers.

According to Gartner, companies that excel at demand forecasting average 15% less inventory, 17% stronger perfect order fulfilment , 35% shorter cash-to-cash cycle times, and one-tenth as many stock-outs as their peers.

Logility's white paper, Seven Methods that Improve Forecasting Accuracy, outlines a variety of forecasting methods that can be tuned to optimize forecasts at different phases of the product life cycle by exploiting available historical data and market knowledge.

Advanced forecasting systems use a combination of qualitative and quantitative techniques. The key is to pick the most effective models, blend their best features, and shift between them as needed to keep forecast accuracy at its peak.

This paper highlights the most effective and flexible forecasting models, including best-fit statistical, derived, attribute-based, causal, and more.

See how a spectrum of forecasting methods can achieve better results. Download your copy of Seven Methods that Improve Forecast Accuracy

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