Fashion shrinkage, briefed to technology
The business wants AI. You sign off on the architecture. Ward scans continuously shrinkage over 15,000+ fashion SKUs and hands back the technology read each day.
What a fashion Head of IT sees in shrinkage
Shrinkage Detection, in one sentence. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
Applied to Fashion & Apparel, the surface area is 15,000+ SKUs throughout locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
The business wants AI. You sign off on the architecture. Ward delivers insight cards scoped to technology decision-making.
What Ward does with that: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Key capabilities
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
- Cause-level shrinkage attribution
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled fall denim against last season’s curve at the same selling week. Two causes, both still fixable this week.
| Signal | Finding |
|---|---|
sell_through | Week 6 sell-through 38% vs. 52% plan, concentrated in 3 of 9 door clusters |
size_curve | Waist 30–32 sold out in 41 doors while 36–38 sits at 71% on hand |
markdown.ladder | First markdown is 3 weeks later than LY, weeks-of-supply now 11.4 |
Recommend: transfer 30–32 out of the 12 overstocked doors, hold the ladder on core indigo, and take the first markdown on light wash now while it still clears at 20%.
sell_through_weekly…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
shopify_orders_daily | import | 2m ago |
shopify_returns_reasons | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
cegid_store_sales | import | 1h ago |
retail_size_curve_actuals | import | 1h ago |
retail_markdown_ladder | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-markdown | permit | Model::"markdown_ladder" |
vendor-blocked | forbid | Model::"labor_*" |
ecom-read-returns | permit | Model::"returns_reasons" |
Why Shrinkage matters for Fashion retail
The biggest hidden source of fashion shrinkage isn't theft, it's administrative error in transfer-heavy operations where every handoff between stores, e-commerce, and returns is a reconciliation risk. Ward tracks inventory movements across all channels and distinguishes transfer discrepancies from return fraud and genuine theft.
What Ward has eyes on.
Find the leak before it drains you. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
Ward keeps a running read on 15,000+ SKUs throughout your locations, at the store-category level rather than the chain roll-up. The metrics under watch include sell-through rate, markdown %, return rate. A roll-up hides a single-store problem inside a healthy average, which is how markdown timing stays invisible for a quarter.
At the metric level. Ward tracks transfer accuracy rates, return-to-sale ratios, inter-store reconciliation gaps, and high-value item movement patterns. Separating operational shrinkage from intentional loss is essential because the interventions are completely different.
Why this combination
is its own problem.
A Head of IT in fashion retail owns numbers that move faster than the reporting cycle that covers them. Sell-through rate, markdown %, return rate shift store by store, every morning. A monthly pack cannot represent that. The business wants AI. You sign off on the architecture.
- 01 Return fraud and wardrobing get logged as legitimate returns because store associates lack the data to challenge them in real time.
- 02 Inter-store transfers get scanned as "received" without case opening, so vendor short-shipments only surface at the next physical inventory.
Benchmarks. Fashion shrink runs 1.4-2.5% of sales, with returns/wardrobing in premium tiers contributing 0.4-0.8% of that. High-value categories (handbags, outerwear, premium denim) account for 40-60% of total dollar shrink despite being 10-20% of unit volume.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward ingests your existing systems on a read-only connection. Nothing is written back. First cards arrive inside the first 48 hours.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward hands back cards every morning, each with what caused it and the next step. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
Fashion KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. For Fashion retail specifically, Ward monitors 15,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Sell-through rate, Markdown %, Return rate, Style velocity, Size accuracy at the store-category level. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Technology decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks transfer accuracy rates, return-to-sale ratios, inter-store reconciliation gaps, and high-value item movement patterns. Separating operational shrinkage from intentional loss is essential because the interventions are completely different.
Ward flags a cluster of stores where high-value item returns run well above estate average, most without original tags, with the same payment cards appearing across multiple locations. The pattern matches a wardrobing ring. LP adjusts the return policy for flagged categories and sees a significant drop in high-value returns within weeks.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
See what Fashion shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
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