Demand Forecasting on RELEX, for store operations
Managing 800 stores from a spreadsheet is insane. Ward spots demand movement in your RELEX data and attributes what caused it.
Demand Forecasting on RELEX, for store operations
Managing 800 stores from a spreadsheet is insane. Ward filters to what a Director Store Ops can act on and drops the rest.
Here is demand forecasting in plain terms. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
The mechanism. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
Getting the data in. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
The short list
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
- Store-SKU-day level precision
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" |
Why this combination
is its own problem.
Demand Forecasting produces a lot of output that is technically correct and operationally useless to store operations. Ward filters on whether the finding changes a decision a Director Store Ops can actually make.
What Ward has eyes on.
Ward ingests 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 demand model runs each day, not on a reporting calendar. It picks up the pattern, traces the cause, and attaches the next step before the number reaches a review deck.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to RELEX Solutions. Ward reads straight from demand forecasts, space plans, workforce forecasts and starts building baselines. First insight cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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.
-
03
Weeks 4 to 12: operating rhythm
Cards arrive each day 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
Impact metrics with RELEX
Data lake enrichment
Ward enriches RELEX data with: RELEX forecasts, POS actuals, Weather & events, Demographic data, Supplier performance
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
- ✓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
Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research
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.
Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: 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.
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 demand problems Ward catches.
Root causes, not just alerts. See it on your data.
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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.