Stockout · RELEX · Head of IT

Stockout Prediction on RELEX, for technology

Know before the shelf empties. Ward runs it off RELEX, scoped to what a Head of IT owns.

Stockout Prediction on RELEX, for technology

The business wants AI. You sign off on the architecture. Ward writes the finding at the altitude a Head of IT works at.

Stockout Prediction. Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.

What Ward does with that: 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 RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.

Capabilities

  • Priority ranking by revenue impact
  • Reduce lost sales by catching gaps early
  • Automated replenishment recommendations
  • Supplier-aware lead time modeling
app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did Store 37 miss target last week?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83%, backroom replenishment lag at 2–4p
promo.liftBOGO 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.

8 parallel queries 3 sources cited confidence 0.92
Show me how to fix the staffing mismatch.
You · 9:43 AM
Labor Agent · drafting schedule diff
Querying labor_scheduling
Ask anything, Ward routes to the right agent. Cmd+K

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"
Stockout on RELEX data, live product demo.

Why this combination
is its own problem.

A Head of IT rarely logs into RELEX. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.

What Ward has eyes on.

Ward reads demand forecasts, space plans, workforce forecasts from RELEX Solutions on a read-only connection. Nothing is written back, and your Supply Chain configuration does not change. RELEX stays the system of record.

The stockout model runs on a daily cycle, not on a reporting calendar. It flags the pattern, attributes the cause, and attaches the next step before the number reaches a review deck.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward sits on top of RELEX Solutions with read-only credentials and begins ingesting demand forecasts and space plans. No config changes on your side. First findings arrive in two days.

  2. 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 findings are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: operating rhythm

    Daily cards arrive on a daily cycle and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.

How Ward connects to RELEX Solutions

Ward integrates with RELEX for unified retail planning. Ward monitors RELEX forecasts against actuals and surfaces planning exceptions as insight cards.

Setup: Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.

Data Ward reads from RELEX

Demand forecasts
Space plans
Workforce forecasts
Promotion plans
Replenishment orders

Impact metrics with RELEX

Forecast Accuracy
Drift flagged per store
Accuracy decay caught before it compounds across locations.
Space Productivity
Planogram gaps exposed
Space plans checked against actual sell-through per fixture.
Replenishment Efficiency
Manual triage reduced
Exceptions ranked by revenue impact and urgency.
Fresh Waste
Perishable ordering tightened
Hyperlocal weather and events sharpen fresh item forecasts.

Data lake enrichment

Ward enriches RELEX data with: RELEX forecasts, POS actuals, Weather & events, Demographic data, Supplier performance

The business wants AI. You sign off on the architecture.

Pain points
  • ×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
How Ward helps
  • 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 RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound. Data points include: Demand forecasts, Space plans, Workforce forecasts, Promotion plans, Replenishment orders.

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.

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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info