Statistical Forecasting processes

The Classical decomposition analysis page displays data to help you gain insight into underlying patterns and make more accurate forecasts. If you understand trends, seasonality, and cyclical variations, you can better analyze historical data.

This page enables you to manage your ABC XYZ segmentation criteria.

This page provides C-VaR throughput and accuracy analysis. C-VaR analysis helps you assess the potential losses or risks associated with disruptions or uncertainties in the supply chain.

Displays the relationship between various datasets and historical PlanIQ data.

Visualize PlanIQ results including forecasts, seasonality, and trend components.

This page enables you to manage seasonal profiles for your product and customer arrangements. Here you can create, edit, and delete profiles.

This page enables you to select a product-customer combination and display an overview of the respective System Optimization Results for the main optimization methods: Simple Exponential Smoothing, Double Exponential Smoothing, Crostons Method, and Modified Crostons.

This page enables you to display statistical forecast values for both system-optimized and manually-specified values to provide easy comparison of the results, with the simple exponential smoothing method.

This page enables you to display statistical forecast values for both system-optimized and manually-specified values to provide easy comparison of the results, with the Croston's method.

This page enables you to display statistical forecast values for both system-optimized and manually-specified values to provide easy comparison of the results, with the modified Croston's method.

This page enables you to display statistical forecast values for both system-optimized and manually-specified values to provide easy comparison of the results, with the double exponential smoothing method.

This page enables you to review all statistical forecasts at all levels of product and customer hierarchy for specified customer/product combinations.

The Basic Methods page displays all statistical forecasts for the basic forecast methods.

The Intermittent Methods page displays all statistical forecasts for the intermittent forecast methods.

The Moving Average Methods page displays all statistical forecasts for the moving average forecast methods.

The Trend Methods page displays all statistical forecasts for the trend forecast methods.

The Trend Seasonal Methods page displays all statistical forecasts for the trend seasonal forecast methods.

Use the time series graphs to compare the performance of multiple PlainIQ forecasting methods against sales data.

This page allows you to review C-VaR values by product and customer, and identify combinations which have high demand variability.

You can use this page to disable specific forecast methods required at both a global level and product level.

This page provides an overview of which statistical methods are most commonly used across product-customer combinations.

With Edit Final Forecasts, you can edit the forecast when you adjust parameters that relate to disaggregation and the forecast method used. Or you can edit the forecast when you apply a time series uplift adjustment.

The Final Forecast MAPE / Accuracy and Bias page displays the forecast accuracy and BIAS data for each product-customer combination. You can change the key performance indicator (KPI) on display by using the context selector in the top-right of the page.

This page enables you to manage global parameter settings. These global settings become the default parameter values used throughout the application.

Import History for Stat allows you to import a corrected history from the History Correction page in Demand Planning.

This is a downstream process from the History Correction function. Once you complete history corrections on that page, you must then import the corrected history data to the Stat Forecasting app. The function in the Import History for Stat page enables that process.

This page displays the planning periods, defined time intervals that are used for all planning data, and their descriptive names, used in the app. It also allows you to adjust the periods to match your business calendar.

Manage the related datasets and their attributes used in PlanIQ.

Import related data and attributes to use for PlanIQ forecasts.

View the data for all related data imported from or exported to PlanIQ.

View imported attributes for a selected product and customer, and select those to export to PlanIQ.

Run PlanIQ forecasts for related datasets.

Manage PlanIQ forecasts to ensure a best-fit analysis.

The Statistical Forecast Settings page provides a worksheet view for key configuration parameters.

This worksheet provides an overview of the current method for each product, as determined by best fit. You can also change the forecast method, overlay and seasonality.

The Update Current Period page enables you to update the current period within the Stat Forecasting application, manually or through an import action.

You can manage the Alpha and Beta values for all your statistical forecast methods on this page.

This page evaluates the test results for each forecast method and identifies the best fit method.