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Demand Pattern Classification Reference: ADI, CV2 and Forecast Method

ABC and XYZ tell you value and variability, but they do not tell you whether demand is intermittent. The ADI and CV2 scheme separates smooth, erratic, intermittent and lumpy demand, and each needs a different stocking and forecasting approach.

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Item	Definition or threshold	Typical stocking approach	Suggested forecast method
ADI	Average demand interval: periods divided by number of periods with demand	Higher means demand is rarer	Used to classify
CV2	Squared coefficient of variation of non-zero demand sizes	Higher means sizes vary more	Used to classify
ADI cut-off	1.32 periods	Splits regular from intermittent	Syntetos and Boylan scheme
CV2 cut-off	0.49	Splits stable from variable sizes	Syntetos and Boylan scheme
Smooth	ADI below 1.32 and CV2 below 0.49	Reorder point or MRP with safety stock from normal distribution	Moving average, exponential smoothing
Erratic	ADI below 1.32 and CV2 at or above 0.49	Larger safety stock, review sizes of orders	Exponential smoothing with outlier control
Intermittent	ADI at or above 1.32 and CV2 below 0.49	Low min and max, order on demand where lead time allows	Croston or SBA
Lumpy	ADI at or above 1.32 and CV2 at or above 0.49	Avoid stocking by formula, consider make or buy to order, review by item	SBA or bootstrapping, judgment
Croston method	Smooths demand size and interval separately, forecast = size / interval	Applies to intermittent items	Known to be slightly biased upward
SBA	Syntetos Boylan Approximation: Croston multiplied by 1 - alpha / 2	Reduces Croston bias	Common default for spare parts
Normal safety stock	Z x sigma x sqrt(lead time)	Reasonable for smooth items only	Poor fit for lumpy items
Fill rate target	Share of units met from stock	Better target than cycle service level for slow items	n/a
Time period choice	Weekly buckets often make ADI larger than monthly buckets	Classify at the bucket you plan in	n/a
Minimum history	At least 24 periods is a common working rule	Fewer periods give unstable classes	n/a

Cut-offs of 1.32 and 0.49 come from published research by Syntetos, Boylan and Croston and are a working convention, not a law. Reclassify items regularly because patterns change.

Classify your whole item master

Optional: run the ADI and CV2 classification across your ERP history.

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How to use the reference

Compute ADI and CV2 for each item from at least two years of history, then look up the class here. ADI is the number of periods divided by the number of periods that had demand. CV2 is the squared coefficient of variation of the demand sizes, using only periods with demand. Match the result against the table and note the suggested approach.

ADI = total periods / periods with non-zero demand
CV2 = (std dev of non-zero demand / mean of non-zero demand) squared
Classify at the planning bucket you use in the ERP

Why the classes matter

Normal-distribution safety stock formulas assume demand is reasonably regular. For intermittent and lumpy items, many periods have zero demand and a few have big spikes, so the standard deviation overstates or understates risk depending on the history. Croston style methods and fill rate targets give more stable results than a mean and sigma approach.

Example

A bearing used in 6 of 36 months has ADI of 6. If the quantities in those months are 2, 3, 2, 4, 3 and 2 the mean is about 2.7 and the standard deviation about 0.82, so CV2 is about 0.09. That makes the item intermittent, so a SBA forecast and a small min and max are more appropriate than a reorder point from monthly sigma.

Common mistakes

Calculating CV2 with zero periods included mixes two effects and moves everything toward erratic. Using one class cut-off for all bucket sizes is another. Finally, do not turn the class into a rule without business judgment: a critical spare may deserve stock whatever the class says.

Frequently Asked Questions

What are the ADI and CV2 cut-off values?

The widely used values are 1.32 for ADI and 0.49 for CV2. They come from research on forecasting methods for intermittent demand and mark where Croston style methods start to beat simple smoothing.

How is this different from XYZ analysis?

XYZ ranks items by variability using coefficient of variation of all periods. ADI and CV2 separate how often demand occurs from how large it is, which matters for slow moving and spare parts.

Should lumpy items be stocked?

Only after a business review. Many lumpy items are better handled by ordering against a customer need, or by a small stock backed by supplier agreements, because formula-based safety stock is unreliable.

How much history do I need?

At least 24 periods is a practical minimum, and more for very slow items. With a short history, a single sale can change the class, so treat the result as provisional.

Netray can compute ADI and CV2 for every item in your ERP history and feed the results into planning parameters with human review.

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