Demand Forecasting + RELEX: Built for Head of Procurement
Most retailers find Demand problems too late. Ward delivers automated insight cards. What changed, why, and what to do. While there's still time to act. Your Procurement team has the data. What they don't have is bandwidth to find what's buried in it.
Demand Forecasting powered by RELEX Solutions
Demand Forecasting is the process of ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-sku-day level.
When connected to RELEX Solutions, Ward reads demand forecasts, space plans, workforce forecasts and enriches them with contextual signals to generate demand insight cards. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
How Ward delivers Demand insight cards: Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
Key capabilities
- Store-SKU-day level precision
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
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…
Dashboards
Pinned views built from saved data-lake queries.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
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 |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037 — basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211 — basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
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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
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher with no leverage and no benchmark
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 II, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing tied to scope and store count, not seats or tokens
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓Reference customers and a 850+ store live pilot operator you can interview
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
What a Ward insight card looks like
72-hour heat wave predicted for Dhaka region. Historical model suggests +18% on beverages, +12% on ice cream. Pre-position recommended.
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.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 II, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing tied to scope and store count, not seats or tokens.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust 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
Insights surface
Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action. Not just a chart to interpret.
Insights become actions
Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel: mobile push, text, or email. Not every insight needs a ticket. When one does, it has an owner.
Your team responds
Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal. Ward learns what worked, what missed, and why.
Outcomes measured
Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.
Agents get sharper
Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.
See what demand problems Ward catches.
Root causes, not just alerts. See it on your data.
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.