Stockout Prediction for Head of IT / VP Technology, read from Blue Yonder
Ward pulls from demand forecasts, replenishment recommendations, allocation plans from Blue Yonder, runs stockout on it, and returns the technology read daily.
Stockout Prediction on Blue Yonder, for technology
The business wants AI. You sign off on the architecture. Ward surfaces the readings that change a technology decision.
Stockout Prediction, in one sentence. Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
How Ward delivers Stockout insight cards: 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 reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.
What you get
- 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 Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
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 |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | 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-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
What Ward has eyes on.
The connection to Blue Yonder is read-only and runs on your schedule. Ward pulls from demand forecasts, replenishment recommendations, and the rest of the feed, then stitches together it with external context your Supply Chain does not carry: weather, local events, competitor pricing.
Know before the shelf empties. 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.
Why this combination
is its own problem.
Blue Yonder was built for transactions, not for technology decisions. The data is right there and it is in the wrong shape. Ward pulls from demand forecasts and replenishment recommendations and rewrites them as a decision.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of Blue Yonder with read-only credentials and begins ingesting demand forecasts and replenishment recommendations. No config changes on your side. First findings arrive inside the first 48 hours.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward sends insight cards on a daily cycle, each with root cause and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many findings arrive, it is how many get acted on.
How Ward connects to Blue Yonder
Ward layers on top of Blue Yonder demand planning and replenishment. Ward watches what Blue Yonder recommends and flags when actual diverges from plan.
Setup: Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.
Data Ward reads from Blue Yonder
Impact metrics with Blue Yonder
Data lake enrichment
Ward enriches Blue Yonder data with: Demand forecasts, POS actuals, Weather & events, Supplier fill rates, Competitor data
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
Frequently asked questions
Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy. Data points include: Demand forecasts, Replenishment recommendations, Allocation plans, Exception alerts.
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.
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 stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.