The Commodity Sensitivity solution enables organizations to analyze the impact of commodities price fluctuations on company performance by merging financial, supply chain and external market data in a real-time predictive analytics and forecasting model. The solution also enables scenario planning to assess the impact of different purchase strategies and trigger corrective actions.
Commodity Sensitivity landing dashboard summarizes fiscal year impacts and provides ease of navigation through model.
Predictive Commodities Dashboard enables users to select the forecasting method with the method accuracy analysis, and to visualize commodity price forecast.
Lead Time Settings enables definition and visualization of lead-time policies.
Impacts Dashboard enables analysis of the various impact components across scenarios.
Enables users to forecast commodities and FX trends through various predictive methods (e.g. Holt-Winter, Moving Averages, Exponential Smoothing, Trend & Seasonality, Prior Year Projection, etc.) and what if scenarios.
Commodity Forecast Accuracy Analysis
Automated testing of the historical predictive methods and ranking of the predictive models according to MAPE (Mean Absolute Percentage Error), MAD (Mean Absolute Deviation) and RMSE (Root Mean Square Error) analysis enabling the user to apply the most predictive scenario.
Lead Time Settings
Selection of preferred lead time assumptions for the predictive scenarios which automatically trigger the lead times for Purchasing to Inventory, Inventory to Production and Production to Market periods which enables users to directly assess impact on company performance.
Commodity Impacts and Actions
Visibility of impacts on commodity margins and FX volatility in 4 main sub-components: Commodity Price, Commodity FX, Inventory FX, and Local FX. Based on the predictive insights, the user has the ability to change assumptions, perform simulations, and create what-if scenarios in order to reduce risks and define opportunities.
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