Demand Forecasting on Power BI, for finance
Ward reads power BI REST API datasets, underlying SQL/Azure data, dataflow outputs from Microsoft Power BI, runs demand on it, and hands back the finance read daily.
Demand Forecasting on Power BI, for finance
Your P&L surprises are born on the store floor. Ward writes the finding at the altitude a CFO works at.
What demand forecasting does: Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
Under the hood. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
How the connection works. Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
What it does
- 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.
A CFO rarely logs into Power BI. 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 ingests Power BI rather than replacing it. Power BI REST API datasets, underlying SQL/Azure data, dataflow outputs come across on a read-only connection, get enriched with contextual data, and come back as daily cards. Your BI is untouched.
See demand before it arrives. 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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to Microsoft Power BI with read-only credentials and begins ingesting power BI REST API datasets and underlying SQL/Azure data. No config changes on your side. First daily cards arrive in two days.
-
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: operating rhythm
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 Microsoft Power BI
Ward sits alongside Power BI. Your dashboards visualize. Ward detects and explains what changed. No dashboard login needed for your morning brief.
Setup: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
Data Ward reads from Power BI
Impact metrics with Power BI
Data lake enrichment
Ward enriches Power BI data with: Power BI datasets, Underlying SQL/Azure data, Weather & events, Demographics, Custom feeds
Your P&L surprises are born on the store floor.
- ×Margin erosion only surfaces at month-end close
- ×Inventory carrying costs are a black box
- ×Working capital tied up in slow-moving stock nobody is watching
- ×Same-store sales comps lack decomposition into actionable drivers
- ×Capex decisions for store remodels lack unit-economics evidence
- ✓GMROI tracking by category with weekly insight cards
- ✓Inventory carrying cost alerts when capital efficiency drops
- ✓Working capital optimization recommendations based on turnover trends
- ✓SSS decomposition into traffic, conversion, and basket components
- ✓Store-level unit economics cards for capex prioritization
Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group
Frequently asked questions
Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched. Data points include: Power BI REST API datasets, Underlying SQL/Azure data, Dataflow outputs.
Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.
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.
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.