Demand Forecasting on NCR, for technology
The business wants AI. You sign off on the architecture. Ward flags demand movement in your NCR data and accounts for what caused it.
How a Head of IT runs demand off NCR Voyix
The business wants AI. You sign off on the architecture. Ward hands you daily cards scoped to technology decision-making.
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.
Setup: Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
Capabilities
- Automatic reorder point recalculation
- Store-SKU-day level precision
- Weather-driven adjustment
- Event and holiday modeling
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 NCR Voyix is read-only and runs on your schedule. Ward pulls from POS transactions, item-level sales, and the rest of the feed, then cross-references it with external context your POS does not carry: weather, local events, competitor pricing.
The demand model runs on a daily cycle, not on a reporting calendar. It picks up the pattern, traces the driver, and attaches a recommended move before the number reaches a review deck.
Why this combination
is its own problem.
A Head of IT does not need the demand model explained. They need to know which stores moved, why, and what to do by end of day. Ward writes the finding at that altitude.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to NCR Voyix. Ward reads POS transactions, item-level sales, tender data and starts building baselines. First daily cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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: steady state
Ward delivers daily cards every morning, each with the driver and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.
How Ward connects to NCR Voyix
Ward integrates with NCR Voyix POS and Aloha for convenience and restaurant retail. Transaction-level data powers daypart analysis and impulse optimization.
Setup: Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
Data Ward reads from NCR
Impact metrics with NCR
Data lake enrichment
Ward enriches NCR data with: POS transactions, Weather & events, Loyalty data, Competitor proximity, Demographic 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 NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration. Data points include: POS transactions, Item-level sales, Tender data, Daypart summaries, Loyalty data.
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 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.
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