Managing 800 stores from a spreadsheet is insane.
Your store operations team has the data. They don’t have the bandwidth to find what’s buried in it. Ward delivers the findings, with root causes attached. Click any number to see the SQL.
Pulled Store 37’s last 28 days against the chain baseline. Two compounding causes.
labor_efficiency | Revenue per labor hour −22%, staffing miss at the 11a–1p peak |
|---|---|
promo.lift | BOGO crackers cannibalised Brand Y −28%, net category only +6% |
Nine SKU-store pairs cross the predicted-zero threshold inside 48 hours. Store 22 produce is the most urgent.
forecast.daily | Lettuce at Store 22: 84 units/day against 38 on hand, zero by 11a Thursday |
|---|---|
supplier.lead | Regional DC lead time 18 hours, no sufficient PO already in transit |
Three stores in the ten-store cluster breach the 2% threshold on one SKU. The cause is upstream, not theft.
shrink.rate | Stores 4, 11, 19 at 2.4–3.1% against a 1.2% cluster baseline |
|---|---|
delivery.defect | All three take delivery from the same lane, defect rate 4.8% above goal |
Gross lift looked strong. Net of cannibalisation and pull-forward it destroyed value.
promo.gross_lift | Promoted SKU +41% against its own baseline |
|---|---|
promo.net | After category cannibalisation and pull-forward, net contribution −$14K |
What store operations finds out
too late.
Managing 800 stores from a spreadsheet is insane.
Morning check-ins rely on phone calls and email chains
No single view of which stores need attention today
Labor scheduling is disconnected from demand signals
Planogram compliance is checked manually, quarterly
Exception management is reactive and inconsistent
Source: RSR Research
Insight cards for
store ops directors.
- ×Morning check-ins rely on phone calls and email chains
- ×No single view of which stores need attention today
- ×Labor scheduling is disconnected from demand signals
- ×Planogram compliance is checked manually, quarterly
- ✓Morning brief delivered at 06:47 with prioritized action list
- ✓Estate-wide heat map of store performance, updated hourly
- ✓Staffing recommendations correlated with predicted traffic
- ✓Planogram compliance anomalies detected and flagged
Morning brief delivered at 06:47 with prioritized action list
Estate-wide heat map of store performance, updated hourly
Staffing recommendations correlated with predicted traffic
Planogram compliance anomalies detected and flagged
Consistent exception handling with recommended actions
From signature
to running insights.
A live pilot for store ops directors hits these milestones on real data, on a fixed-fee schedule.
This is what Ward
delivers to you.
Dashboards
Pinned views built from saved data-lake queries.
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" |
7 stores need your attention. 793 are clean. Priority: Stores 22 and 37, fresh availability below threshold. Replenishment already raised.
Read-only credentials, Cedar policy per agent, a full reasoning trail on every number. Your security review is short and your data team is not carrying the risk.
Read the security posture →The blind spots that cost
store ops directors the most.
KPIs that erode quietly when nobody’s watching, one from each vertical this role covers.
What Ward covers
for store ops directors.
Retail verticals
Insight typesThe first three are where this role usually starts.
Metrics Ward moves
Platform pieces this role leans on
Systems Ward connects toRead-only by default.
Operator stories
and the alternatives.
Two ways to start.
Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.
Playbooks for Store Operations.
The playbooks this team runs on Ward.
Control AI spend by department and user.
Without skimping on the outcome.
Give every department and every user a compute budget. Ward routes each question to the cheapest model that clears the quality bar, so finance caps the spend without capping what the business gets back.
Managing 800 stores from a spreadsheet is insane.
See what Ward finds for Store Operations leaders, with root causes and recommended actions.
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.