This session has ended. To view the content details and download a copy of the presentation deck, click the link below.
Be sure to register for each of the remaining sessions in our webinar series!
This session has ended. To view the content details and download a copy of the presentation deck, click the link below.
Be sure to register for each of the remaining sessions in our webinar series!
The first session in our webinar series discussed the basics of setting up and analyzing your data to get an approximation on the “forecastability” of your planning combinations and further stratifying those combinations into ABC/XYZ classifications.
This second session goes deeper into assessing the available historical demand and the rationale for how much history is sufficient in different scenarios.
Demand history, and how it is “bucketed,” makes a huge difference in both the robustness of your statistical model and its potential for delivering a “good” forecast ongoing. However, sometimes too much history is detrimental and 0’s are significant!
You will understand how forecasting in weekly or monthly buckets and how your supply agility can impact the setup of determining the amount of history is appropriate in any given situation. You will then be led through that detailed setup when pulling data from your planning system into Excel to do the analysis and sorting into historical horizon groupings. This second session sets the stage for statistical model assignment and execution that will be covered in the remaining two sessions in this series.
Some specifics that will be addressed in this webinar session include:
In this session you will see several forecasting scenarios at different aggregation levels to learn how weeks versus months can make a significant difference. You will also learn how to use the planning tool to show various algorithm results and select the best model to apply to your dataset.
In this session we will introduce the Forecast Value Added concept and explain how to enhance value in the forecasting process, or minimize the steps in your process that may be hampering value. Several real-world examples of common forecasting practices using FVA will help highlight opportunities for improvement.
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