How to Understand Error Checking Variable Forecast Planning Buckets

Executive Summary

  • Why is there a lack of questioning regarding the planning book?
  • The importance of testing the planning bucket aggregation.
  • Improving the ability of best fit procedures to find patterns.
  • Indicators of too small of a forecasting planning bucket.


Some companies forecast using a weekly planning bucket, others monthly.

Why the Lack of Questioning Related to the Planning Bucket?

We find it peculiar that companies typically do not question their planning bucket. Even in situations where it makes sense to do so and will drive better forecast accuracy.

Demand sensing, which is not a forecasting method but a way of adjusting replenishment, has prompted companies to think that using very frequent information inputs, combined with smaller planning buckets improves forecast accuracy.

The Importance of Testing the Planning Bucket Aggregation

When we perform forecast testing, we usually find the opposite. But either way, the planning bucket interval should not be assumed to be any one particular value without testing.

Secondly, it should not be assumed that all product location combinations should use the same planning bucket. It is natural that product locations with lower demand histories and more intermittent demand will benefit from larger forecast planning buckets and vice versa.

For instance, with intermittent product histories, longer planning buckets can allow for improved forecast method selection. Usually, the smaller the planning bucket, the more the pattern is “cut up” making any best fit procedure miss patterns that can be quickly picked up with a larger planning bucket.

Indicators of Too Small of a Forecasting Planning Bucket

This can be determined by finding the type of match between the product locations and the forecasting methods. If a large percentage of the product location database is assigned to lower quality forecast methods, this is a good indicator that the planning bucket selected for many of the product location combinations is too small.


Of course, to compare like for like, the forecast accuracy measurement must be disaggregated to a consistent planning bucket. Brightwork Explorer can be used to test what the right planning bucket should be. It can also be used to apply different planning buckets for different groupings of the product location database.

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