Planning and Forecasting Using Predictive Planning

 Predictive Planning can be used to predict future performance based on your historical data in your planning application. You can compare and validate plans and forecasts based on the predictions. For a more accurate and statistically-based forecast, you can copy the prediction values and paste them into a forecast scenario for your plan.

Predictive Planning works with EPM Standard and EPM Enterprise applications for Custom and Module application types. For legacy applications, Predictive Planning works with Standard, Enterprise, and Reporting application types. Predictive Planning is not available in FreeForm applications.

In following scenario, we will describe how you can enable predictive planning in your existing planning application:

Adjusting User Variables

In this section, you will be adding values to the Product Family user variable.

1. On the home page, click Tools, then User Variables.
2. In User Variables, for ProductFamily, click  (Member Selector).
3. In Select Members, click the arrow next to Total Product.
4. Under Total Product, select Computer Accessories and Computer Services.

When selected, the members are added to the Selections list on the left.

5. Click OK.
6. Verify that Computer Accessories and Computer Services were added to ProductFamily, and then click Save.
7. At the information message, click OK.
8. Return to the home page. On the upper-right, click  (Home).

Running Predictive Planning

1. On the home page, click the Data card.
2. In Data Entry, under Library, expand Forecast.
3. Scroll down and then click Sales Forecast - Products.
4. On the form, review sales forecast for each product under Computer Equipment for the upcoming planning time intervals.

5. On the top right of the form, click Actions and select Predictive Planning.
   
   

When you run Predictive Planning, the system retrieves all the historical data for each member on the form. It then uses sophisticated time series forecasting techniques to predict the future performance for these members. The prediction results are displayed at the bottom of the form.

6. In the Predictive Planning section, use the down arrow  to select Tablet Computer from the dropdown.
7. Review the prediction results for Tablet Computers.

The historical data for this product is shown as a green series on the left side of the chart. The base case prediction is shown in blue on the right. The prediction interval, which is bound by the Worst and Best cases, is shown as an orange band around the base case prediction.

8. From the dropdown, select Sentinal Standard Notebook.
9. Compare the forecast against the statistical prediction. The Forecast scenario appears on the right side of the chart as a light green series.
10. From the dropdown, select Envoy Standard Netbook.
11. Review the predictive results for this product.

On the right side, view the informational boxes that contain key metrics for each series.

The Growth Rate metric allows the planner to quickly compare any two series. Based on the growth rate shown, the forecast is much more aggressive than the statistical prediction. The gauge to the right reflects the elevated risk for meeting the sales target for this product.

Understanding the Components of Predictive Planning

Predictive Planning provides a statistically-robust mechanism to help planners create and validate their forecasts using time series forecasting methods on historical data. Most forecasts created by users are based on gut feel or simple growth rates from previous years. However, Predictive Planning allows users to leverage time series forecasting techniques to produce more accurate forecasts.

When you open a form and run Predictive Planning, it produces the following results for each member on the form:

When you maximize the predictive results, the section is displayed with additional data:

Tip:

On upper right of the predictive results pane, click  (Maximize) to expand the results view.

1. Member selection dropdown: Select any member on the form to display predictive planning results.
2. Chart area: Displays data for the selected member. Historical actual data is displayed on the left side of the chart. On the right side of the chart, partitioned by the vertical line, forecast and prediction data for the future time horizon is displayed. The chart area also contains data for the best case (optimistic) and worst case (pessimistic) scenarios.
3. Historical Data Details: Provides information on the historical data used for running the forecast algorithms. It includes the number of historical observations, missing values, outliers, presence of seasonality, etc.
4. Prediction Details: Provides details on the prediction output for the best-performing algorithm. Predictive Planning runs a set of time series forecasting algorithms on the historical data and picks the output from an algorithm that gives the best accuracy for the given member. It shows the name of the algorithm that has the highest accuracy compared to other algorithms and it provides RMSE and Accuracy metrics.
5. Information boxes: Provides a statistical summary of each series on the right side of the chart. It typically displays one box per series. The order of the boxes matches the order of the series in the legend.
• Growth Rate statistic is provided in each box as the key metric for comparing one series against another.
• Risk Gauge is added next to the growth rate to indicate the probability of the scenario occurring above or below the prediction.

How Predictive Planning Works

Predictive Planning is accessible from any form using the Actions menu.

Forecasting Algorithms

Two primary techniques of classic time-series forecasting are used in Predictive Planning:

• Classic Non-seasonal Forecasting Methods — Estimate a trend by removing extreme data and reducing data randomness
• Classic Seasonal Forecasting Methods — Combine forecasting data with an adjustment for seasonal behaviour

 

Method	Seasonal	Best Use
Simple Moving Average	No	Volatile data with no trend or seasonality
Double Moving Average	No	Data with trend but no seasonality
Single Exponential Smoothing	No	Volatile data with no trend or seasonality
Double Exponential Smoothing	No	Data with a trend but no seasonality
Damped Trend Smoothing non-seasonal method	No	Data with a trend but no seasonality
Seasonal Additive	Yes	Data without trend but with seasonality that does not increase over time
Seasonal Multiplicative	Yes	Data without trend but with seasonality that increases or decreases over time.
Holt-Winters’ Additive	Yes	Data with trend and seasonality that does not increase over time
Holt-Winters’ Multiplicative	Yes	Data with trend and with seasonality that increase over time
Damped Trend Additive Seasonal Method	Yes	Data with a trend and seasonality
Damped Trend Multiplicative Seasonal Method	Yes	Data with a trend and with seasonality
ARIMA	No	Data with minimum of 40 historical data points, limited number of outliers and no seasonality
SARIMA	Yes	Data with minimum of 40 historical data points, limited number of outliers and seasonality

All of the non-seasonal forecasting methods are run against the data. If the data is detected as being seasonal, the seasonal forecasting methods are run against the data.

Created by Mohit Jain and Megha Gupta