Name: Ghost stockout detection: When sales interruptions should raise eyebrows
Start: 2026-05-27T17:45:00+0200
End: 2026-05-27T18:15:00+0200

Ghost stockout detection: When sales interruptions should raise eyebrows

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Wednesday May 27, 2026 17:45 - 18:15 CEST

4 - MEDIUM STAGE

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We call ghost stockout the situation in which the stock is marked as available on digital stock management inventories, but not physically available in the store.

We present an unsupervised algorithm for detecting ghost stockouts using only daily sales data. We model each SKU's daily sales outcome as a time dependent Bernoulli process with a parameter representing the probability of selling one product on a given day, denoted as p₀(t). A ghost stockout manifests as an anomalous zero sales streak, whose probability

P(N,t) = ∏ from i=1 to N of (1 - p₀(tᵢ))

serves as a detection signal. In a ghost free environment this signal is uniformly distributed across SKUs; in the presence of ghost stockouts, the empirical distribution exhibits a relative abundance of low P(N,t) values far exceeding the theoretical uniform baseline, a signature that is both theoretically predicted and experimentally confirmed.

We estimate p₀(t) via an adaptive moving average procedure that self calibrates its window size to maintain a bounded relative estimation error across the full range of SKU sale rates and seasonal profiles. Crucially, the entire pipeline, including p₀(t) estimation, error bounding and signal computation, was implemented in pure SQL on BigQuery, eliminating the need for dedicated ML infrastructure and dramatically reducing deployment cost.

Validated across two retail stores over one month (3,220 physical inventory checks), the method achieved an overall true positive rate of 15.5%, rising to 17.8% on self service departments. In the top performing department store pairs the TPR reached 35%, demonstrating that when applied to structurally well defined availability contexts, this fully unsupervised algorithm delivers detection performance competitive with supervised approaches.

Speakers

Pierre Leder

ADEO Services

Cesare Paulin

Data Scientist, Tecnomat Italy

Cesare Paulin is a data scientist and physics-informed AI researcher based in Milan, Italy. He specializes in integrating machine learning, signal processing, and mathematical modeling to tackle complex industrial and scientific problems, with applications in time-series forecasting... Read More →

Wednesday May 27, 2026 17:45 - 18:15 CEST
4 - MEDIUM STAGE

Data / AI, Talk

Audience Advanced

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Dev Summit 2026

Pierre Leder

Cesare Paulin

Attendees (50)

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Success you have shared X tickets.

Dev Summit 2026

Pierre Leder

Cesare Paulin

Attendees (50)

Waitlist (4)

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