Key Capabilities of JDA® Demand Decomposition, powered by Manugistics®:
- Separates base demand from promotional demand
- Splits sales history data into time series and marketing/special events components:
- Time series
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Trend
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Seasonality
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Marketing/special events:
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Promotions
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Price fluctuations
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Cross-product effects (halo/cannibalization)
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Market conditions (micro and macro)
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Special/unusual event
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Drives accurate baseline forecast
Historical sales data contains a mixture of both base (turn) demand and demand generated by promotions. Separating base demand from promoted demand is key to establishing a meaningful base forecast going forward, but this process is often challenging due to high volumes of promotions, products and tactics. As a result, companies commonly struggle to produce an accurate baseline forecast.
Forming the core algorithmic base for JDA’s Price & Promotion Management solutions, JDA Demand Decomposition provides you with a deeper understanding of demand history as well as promotional impact and activity. JDA Demand Decomposition utilizes a collection of advanced statistical algorithms that segment exogenous factors (i.e., price effects, promotions, etc.) from base demand history, and defines a better baseline forecast.
JDA Demand Decomposition splits sales history data into two components – time series and marketing/special events – and enables you to produce accurate statistical forecasting that drives bottom line benefits. Able to handle significant promotional activity across products, including varying promotional tactics and price variations, JDA Demand Decomposition separates the effect of seasonality and promotional lift during seasonal promotions. JDA Demand Decomposition also accounts for peaks and valleys in your history that are attributable to halo or cannibalization effects by other products, as well as pre- or post-promotional effects. With superior forecast accuracy, you will be able to accurately apply future promotions and establish a foundation for improved promotions evaluation and optimization.
