How Forthcast measures the data it publishes

Source

Direct stockout dates, restock dates, order identifiers, and tracked store/SKU pairs come from the Forthcast product database. We do not relabel storage rows as business events: order lines are deduplicated to Shopify orders, and the stockout-duration denominator includes only completed post-install webhook/scan episodes. Forthcast urgency labels are model classifications, not observed outcomes.

Sample & refresh

Figures describe the retained Forthcast panel — not all Shopify stores. Test and quickstart domains are excluded. Stockout duration additionally requires a retained install date, direct webhook or inventory_scan detection after that install, and a valid stock-in date on or after the stock-out date. Open episodes remain in the database and are reported separately rather than assigned a duration. The snapshot was recomputed August 13, 2026.

Platform scale

Current denominators computed directly against the product database:

• 24,683 distinct active non-test store/SKU pairs across 25 stores.
• 342,969 distinct retained Shopify orders across 29 stores. The underlying 956,138 rows are order lines, not separate orders.
• 3,602 completed, directly detected post-install stockout episodes across 23 retained non-test stores; 5594 open episodes are excluded from duration.

These are panel counts with distinct grains, not lifetime traction claims or estimates for Shopify as a whole.

Definitions

• Stockout event — a tracked SKU's available inventory reaching zero, detected in real time via Shopify inventory webhooks or on a scheduled inventory scan.
• Resolution / duration — the elapsed time between a SKU stocking out and being restocked. Percentiles (p50/p75/p90/p95) describe the shape of that distribution.
• SKU tracked — a distinct product variant under active inventory tracking.
• Order analyzed — a Shopify order line processed into the demand history.
• Demand forecast — a generated SKU-level demand projection.
• Inventory anomaly — an unusual demand or stock movement the platform flags for attention.
• ABC class — SKUs segmented by their share of demand: A (top contributors), B (moderate), C (long-tail / slow-moving).

What we publish — and what we deliberately hold back

We publish observed counts, durations and distributions. To keep every number defensible, we hold back:

• Dollar cost / lost-revenue totals — a reliable sales-velocity baseline exists for only a small fraction of events, so any revenue figure would be mostly modeled, not observed.
• Month-by-month "seasonality" of detection — those counts reflect when detection was enabled across stores, not a real seasonal pattern.
• Forecast accuracy and the modeling approach — how well the underlying forecasting performs, and the methods that produce it, are proprietary and out of scope for the published datasets.

The 2026 Forthcast Shopify Merchant Study

Alongside our observed platform telemetry, we run our own primary research. In February–March 2026 we interviewed 13 people who actually manage inventory in real Shopify businesses — about forecasting, reordering, stockouts, and the tools they use and abandon. This is qualitative and directional; it is kept clearly separate from the observed platform figures above.

What the 13 operators told us

Across our 13 interviews, the most common pain was manual, spreadsheet-based inventory planning — cited by 11 of 13 operators. Nine of 13 described forecasting as guesswork or a constant struggle, 8 of 13 juggle inventory across multiple sales channels or warehouses, and 6 of 13 pointed to cash tied up in stock. Of 70 distinct pain points logged, 45 were rated high-severity. Counts describe this 13-operator panel only.

• 11 of 13 — plan and reorder inventory manually, in spreadsheets (Google Sheets / Excel)
• 9 of 13 — describe demand forecasting as guesswork or a constant struggle
• 8 of 13 — track inventory across multiple sales channels or warehouses
• 7 of 13 — wrestle with supplier lead times, minimum order quantities, or PO timing
• 7 of 13 — run on disconnected tools that need manual re-keying between them
• 6 of 13 — cite cash tied up in inventory / cash-flow visibility as a core problem

How we counted: each figure is the number of distinct interviews (of 13) whose logged pain points or summary named that theme. To cite: "Forthcast, 2026 Shopify Merchant Study — forthcast.io/data-methodology."

Read the full findings → State of Shopify Inventory Operations 2026

In their words

“Too little inventory stunts your growth; too much inventory kills you.”

“I would definitely love something automated. We're in 2026 and I can't believe I still have to do an export of my sales from Shopify and an export of the stock at the 3PL.”

“Supply chain has been an Achilles heel of ours. Every time we've started to gain real momentum, we've hit challenges around supply chain.”

At a glance

• 13 Shopify merchant operators interviewed, February–March 2026
• 7 are identity-verified in depth — 5 recruited and paid through an independent research panel (payment records on file) plus 2 we know directly. These are the operators our headline quotes come from.
• 9 interviews transcribed in full; all 13 documented with structured written analyses — retained on file
• Two full video calls were recorded (Operators 08 and 09), held privately and available for verification under NDA
• Participants are referred to by stable anonymized codes (Operator 01–13) to honour their privacy and our panel agreement

How participants were recruited

The operators were reached three ways, and we're transparent about the mix: 5 through Appstore Research, an independent panel that screens and compensates research participants (each applied, was identity-screened, and was paid — records on file); 1 through direct industry contact; 1 through a founder community on X; and 6 through freelance operator marketplaces. Our headline examples come from the verified core; marketplace-sourced interviews are used only as supporting, fully-anonymized colour, never as headline proof.

The panel (anonymized)

On file: "analysis" = structured written analysis; "transcript" = full verbatim transcript; Operators 08 and 09 additionally have recorded video calls held privately.

What we asked about

Every interview walked the same ground: background and role; how they forecast demand and plan reorders; purchase-order workflows and supplier lead times; a specific painful stockout and what a post-mortem changed; ready-to-ship vs pre-order split and QC; multi-channel and multi-warehouse complexity; and the tools they use, what those cost, and where they fall short. Representative verbatim questions include "Would you need any tool at all, or do you think you could get there by building it yourself?" and "Have you ever faced a painful stockout on your best-runners — and did you do a recap of what caused it?"

Why anonymized — and why you can still trust it

Participants took part as research subjects, not public endorsers, so we don't publish their names or brands. Each is a consistent, catalogued Operator NN — you can follow the same operator's views across our articles, the way a research paper cites "P7." The proof isn't a logo; it's the method: an independent panel recruited and screened the core participants, we paid them, and we keep the transcripts (and, for Operators 08 and 09, recordings) on file — available to journalists or partners for verification under NDA. Category descriptors (e.g. "decade-old plant-based supplements brand") are true and non-identifying.

Limitations

A sample of 13, SMB-weighted and self-reported; directional and qualitative, not a statistically representative survey. Where we cite counts (e.g. "9 of 13"), they describe this panel only.

Cite our data

Every figure is free to cite with a link back to its page. Questions about a specific number? info@forthcast.io

See the Stockout Ledger