Sorting Demand Signals

Using ABC-XYZ Segmentation to Improve Forecasting Decisions

code
analysis
S&OP
forecasting
Author

Richard Maestas

Published

December 1, 2025

Align forecasting effort with what matters most

Summary

ABC-XYZ segmentation is one of the most practical ways to align forecasting methods with actual demand behavior. Rather than applying a single model across all products, this approach segments items based on two key dimensions:

  • Volume contribution (ABC) — how important an item is to the business

  • Demand variability (XYZ) — how predictable that item is over time

In practice, this segmentation becomes a foundation for forecast model selection, planner prioritization, and S&OP decision-making.

In this example, we walk through a simplified, real-world approach using a gardening equipment dataset. The goal is not to build a complex model, but to demonstrate how planners can quickly classify demand and use that classification to drive better forecasting outcomes.

Data Setup & Preparation

To keep the example simple and broadly applicable, the dataset is structured at a common planning level:

  • Territory

  • Product Line

  • Customer

  • Month (period)

  • Units Sold (qty)

This reflects a typical demand planning grain used in many organizations.

After loading the data, the first step is straightforward cleanup:

  • Standardize column names

  • Ensure dates are properly formatted

  • Confirm quantities are numeric

The focus here is not data engineering, but creating a clean dataset that supports repeatable analysis.

territory product_line customer period qty
West Gas Mowers Walk-In Customer 2024-11-01 369
West Gas Mowers Walk-In Customer 2024-12-01 59
West Gas Mowers Walk-In Customer 2025-01-01 68
West Gas Mowers Walk-In Customer 2025-02-01 956
West Gas Mowers Walk-In Customer 2025-03-01 32
West Gas Mowers Walk-In Customer 2025-04-01 81

Step 1: Understanding Volume and Variability

The foundation of ABC-XYZ segmentation is built on two simple metrics:

  • Percent of Total Volume

  • Coefficient of Variation (CoV)

Percent of total helps answer:

"Which items actually drive the business?"

CoV helps answer:

"How predictable is demand for each item?"

This is where most of the value comes from — not the classification itself, but the visibility into demand behavior.

territory customer product_line tot_qty cov pct_tot
West Select Seeds Gas Mowers 319860 1.049 0.162
West Woles Mulch Bags 270175 1.299 0.137
West Woles Gas Mowers 194107 0.892 0.098
West House Store Mulch Bags 96892 0.910 0.049
West House Store Gas Mowers 90254 0.979 0.046
West Woles Seed Spreaders 78506 1.630 0.040

Visualization

Plotting CoV against percent of total volume creates a simple but powerful view:

  • High volume + low variability → stable, high-impact items

  • Low volume + high variability → unpredictable, low-impact items

  • Everything else falls somewhere in between

This visualization is often where planners start to recognize:

  • which items can be automated

  • which require attention

  • where forecasting errors are most likely to occur

Many organizations have a long tail of items with zero or intermittent demand. While these items contribute little to total volume, they can distort CoV and lead to misleading XYZ classifications if not handled appropriately.

Step 2: ABC-XYZ Segmentation

Using standard thresholds:

  • ABC (Volume Contribution)

    • A: Top ~80% of volume

    • B: Next ~15%

    • C: Remaining ~5%

  • XYZ (Variability)

    • X: Low variability

    • Y: Moderate variability

    • Z: High variability

Each combination (e.g., AX, BY, CZ) represents a different type of demand pattern.

This creates a simple framework:

Segment Interpretation
AX High volume, stable — ideal for automation
BY Moderate importance, some variability
CZ Low volume, highly unpredictable — often not worth heavy modeling

At this stage, the goal is not perfection — it's directional clarity.

territory customer product_line tot_qty cov pct_tot cum_pct abc_class xyz_class ABC_XYZ
West Select Seeds Gas Mowers 319860 1.049 0.162 0.162 A Z A Z
West Woles Mulch Bags 270175 1.299 0.137 0.299 A Z A Z
West Woles Gas Mowers 194107 0.892 0.098 0.397 A Z A Z
West House Store Mulch Bags 96892 0.910 0.049 0.446 A Z A Z
West House Store Gas Mowers 90254 0.979 0.046 0.492 A Z A Z
West Woles Seed Spreaders 78506 1.630 0.040 0.532 A Z A Z

Plot ABC-XYZ Assignments

Step 3: Improving the Variability Segmentation

One of the limitations of traditional XYZ segmentation is the use of fixed thresholds.

In real datasets (especially retail or seasonal categories like gardening equipment), demand variability is rarely evenly distributed.

To address this, we test a quantile-based approach, where:

  • thresholds are derived from the actual data distribution

  • segmentation adapts to the business context

This results in:

  • more balanced classification

  • better separation between stable and volatile items

  • improved alignment with real demand patterns

Most of the zero values of CoV do not have any volume. These need to be treated a little differently than percent total used in the ABC segmentation.

Step 4: Handling Edge Cases

Two practical adjustments improve the segmentation:

  1. Zero or near-zero demand

    • These items can distort variability calculations

    • Assigning them to "X" avoids unnecessary noise

  2. Negative or undefined CoV

    • Reset to zero for stability

These small decisions matter — they make the segmentation usable in real planning environments.

Comparing Approaches

Here are the thresholds for segmentation for each method respectively:

ABC & XYZ Classification Thresholds
method a b c x y z
percentile 80% 15% 5% 0.25 0.5 >0.50
quantile_1 80% 15% 5% 0 .393 >.393
quantile_2 80% 15% 5% .37 1.879 >1.879

Recommendation

In this example, the quantile-based approach provides a more realistic segmentation than fixed thresholds.

It adapts to:

  • skewed demand distributions

  • uneven product portfolios

  • real-world variability patterns

For most organizations, this approach is a strong starting point when building:

  • forecast model selection logic

  • planner workflows

  • segmentation-driven reporting

Plot Updated Classifications

The plot below illustrates the updated ABC-XYZ combinations under this method.

Conclusion

ABC-XYZ segmentation is often introduced as a classification exercise, but its real value lies in how it shapes decision-making.

By combining volume importance with demand variability, planners gain a clear view of where to:

  • automate forecasting

  • apply more advanced models

  • focus human judgment

In this example, we kept the approach intentionally simple — but the same framework scales across more complex environments and planning tools.

For organizations looking to improve forecast accuracy and efficiency, ABC-XYZ segmentation is not just a technique — it is a foundation for building a more structured, data-driven planning process.

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