NetSuite plus Ward: stockout prediction for fashion retail
Your NetSuite data already carries the stockout data point. Ward reads straight from it, attributes root cause, and attaches a recommended move.
How Ward turns NetSuite data into fashion stockout
Here is stockout prediction in plain terms. Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
In a Fashion & Apparel footprint the job is 15,000+ SKUs over locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Under the hood. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
The connection itself. Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.
What it does
- Automated replenishment recommendations
- Supplier-aware lead time modeling
- Priority ranking by revenue impact
- Reduce lost sales by catching gaps early
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 Stockout matters for Fashion retail
Fashion stockouts are invisible, they show up as "size not available," not "product missing," and the POS never records the lost sale. Ward monitors sell-through velocity by style-size-color-store and detects when popular size runs are depleting faster than replenishment can cover within the remaining selling window.
What Ward has eyes on.
Ward reads NetSuite rather than replacing it. Sales orders, inventory, purchase orders come across on a read-only connection, get enriched with contextual data, and come back as daily cards. Your ERP is untouched.
The stockout model runs every morning, not on a reporting calendar. It picks up the pattern, accounts for root cause, and attaches a recommended action before the number reaches a review deck.
Coverage is store by store, category by category. Ward monitors sell-through rate, markdown %, return rate throughout 15,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven locations are not.
At the metric level. Requires style-size-color velocity tracking, sell-through benchmarking against plan, inter-store inventory visibility, and time-remaining-in-season context. Ward also flags recurring size curve inaccuracies as a planning problem distinct from replenishment.
Why this combination
is its own problem.
Stockout Prediction needs sales orders and inventory at minimum. Oracle NetSuite carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
- 01 E-commerce inventory pools are counted at the chain level but allocated by warehouse, so "in stock" online routinely cancels because the assigned DC ran dry.
- 02 Pre-season size curves are set from prior-year history and rarely re-run mid-season, locking in a misread on emerging size demand.
Benchmarks. Fashion full-price sell-through targets: 60-75% by week 6, 75-85% by week 10. Broken size runs (a key size missing while others remain) typically affect 15-25% of styles in week 4 and 30-40% by week 8 without active rebalancing.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of Oracle NetSuite with read-only credentials and begins ingesting sales orders and inventory. No config changes on your side. First daily 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 returns findings every morning, each with the driver and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.
How Ward connects to Oracle NetSuite
Ward integrates with NetSuite SuiteCommerce, inventory management, and financials. Mid-market retailers get enterprise-grade insight cards.
Setup: Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.
Data Ward reads from NetSuite
Impact metrics with NetSuite
Data lake enrichment
Ward enriches NetSuite data with: Sales orders, Weather & events, Customer segments, Vendor performance, Market pricing data
Fashion KPI impact
Frequently asked questions
Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice. 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 analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance. Data points include: Sales orders, Inventory, Purchase orders, Customer records, Financial summaries, Item fulfillment.
Yes. Ward reads NetSuite data and combines it with contextual signals (weather, events, demographics) to generate Fashion-specific insight cards. No custom development required.
Requires style-size-color velocity tracking, sell-through benchmarking against plan, inter-store inventory visibility, and time-remaining-in-season context. Ward also flags recurring size curve inaccuracies as a planning problem distinct from replenishment.
Ward detects a spring jacket selling far above plan in key sizes at urban stores while sitting in suburban locations. At current velocity, the hot sizes will stock out well before end of season. Ward recommends inter-store transfers from underperforming locations to high-velocity stores, recovering full-price sales that would otherwise become end-of-season markdowns.
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 stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
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