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Method

Seasonality Index for Inventory Planning

A practical inventory planning note on SKU-level seasonality, seasonal buckets, and simpler category-level planning logic.

Planning use

Feeds SKU-level signals back into a simpler category-level inventory model.

Inventory planning improves when seasonality is measured below the category level.

Problem

In inventory planning, it is tempting to assign one seasonal pattern to an entire category. In practice, that can be too coarse. Products in the same category may follow different demand curves across the year.

That creates a planning problem: category-level seasonality is simple, but product-level behavior is often what actually drives inventory risk.

Approach

The method starts by calculating a seasonality index at the SKU level rather than relying only on category averages. Products can then be grouped into seasonal buckets based on similar patterns.

The point is not to create the most complex model. It is to keep the planning logic simple while making the seasonal signal more realistic.

A structured way to turn noisy product behavior into usable planning signals.

Product-level SI

Estimate seasonality at the product level first so each SKU can reflect its own demand pattern across time.

Season buckets

Group products into a smaller set of seasonal buckets so the result stays usable for business planning instead of becoming too fragmented.

Planning linkage

Map those patterns back into category-level planning logic so teams can use a cleaner operational framework without ignoring product differences.

Simple planning improves when the seasonal structure is better.

This method is useful when you want a planning model that remains explainable but still captures real variation inside a category. It is a practical middle ground between one-size-fits-all averages and overly complex forecasting systems.

Operational use

Teams can keep a clean planning framework without pretending that every product in a category behaves the same way.

Broader pattern

I treat this as an example of a broader pattern in operations work: build a model that is simple enough to use, but structured enough to support better decisions.

Original note

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