A grocery Head of IT running pricing on RELEX
Price on elasticity you can measure. Ward runs it on RELEX data across your grocery fleet, scoped to technology.
How a grocery Head of IT runs pricing on RELEX Solutions
Grocery & Supermarket changes the scale of the problem: 30,000+ SKUs, every one of your stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
The business wants AI. You sign off on the architecture. Ward filters to what a Head of IT can act on and drops the rest.
What price optimization does: Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
How Ward delivers Pricing cards: Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
Getting the data in. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
Capabilities
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
- Category-level price sensitivity
- Competitive price monitoring
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 |
relex_replenishment_plan | import | 1h ago |
blue_yonder_forecast_daily | import | 1h ago |
retail_fresh_waste_daily | import | 1h ago |
retail_promo_calendar | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fresh-team-west | permit | Model::"fresh_waste_daily" |
Why Pricing matters for Grocery retail
Grocery pricing walks a razor's edge, a small error on staples like milk or eggs shifts store-level traffic patterns. Ward monitors price elasticity at the category-store level, distinguishing KVIs where sensitivity is acute from margin categories with headroom, so you know which SKUs can absorb a change.
What Ward has eyes on.
The pricing model runs every morning, not on a reporting calendar. It detects the pattern, attributes the driver, and attaches what to do about it before the number reaches a review deck.
Every one of your stores gets its own baseline. Ward monitors fill rate, shrinkage %, fresh waste % against it and raises only the deviations that hold up. The two that show up most in grocery retail are fresh waste & spoilage and on-shelf availability gaps, and both are baseline problems before they are P&L problems.
Ward reads straight from 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.
At the metric level. Ward tracks item-level elasticity by store cluster, competitive KVI price gaps, cross-category basket effects, and promotional cannibalization rates. The critical distinction is between price-sensitive traffic drivers and margin-accretive tail categories.
Why this combination
is its own problem.
Price Optimization behaves differently in grocery retail than it does anywhere else. The estate shape, the SKU count, and the speed of the category all change what counts as a real indicator and what is noise. Ward is tuned to the grocery version.
- 01 Beer and tobacco price changes ripple through unrelated baskets; treating them as standalone categories misses the traffic effect.
- 02 Cost-plus pricing on private label leaves 200-400 bps of margin on the table because the elasticity is below the assumed threshold.
Benchmarks. Grocery KVIs (milk, eggs, bread, bananas, gas) carry elasticity in the -1.5 to -2.5 range; tail categories run -0.3 to -0.8. A 1% list price change on KVIs shifts category volume 1.5-2.5% within a week. Most operators have 200-400 KVIs they actively manage; Ward typically finds another 50-150 hidden ones.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward plugs into RELEX Solutions with read-only credentials and begins ingesting demand forecasts and space plans. No config changes on your side. First cards arrive within 48 hours.
-
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: steady state
Ward hands you cards daily, each with root cause and the next step. Volume settles at a level a single person can read over coffee. The measure of success is not how many cards arrive, it is how many get acted on.
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
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
Grocery KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. For Grocery retail specifically, Ward monitors 30,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Fill rate, Shrinkage %, Fresh waste %, Promo lift, Basket size at the store-category level. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
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.
Yes. Ward reads RELEX data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
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.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Technology decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks item-level elasticity by store cluster, competitive KVI price gaps, cross-category basket effects, and promotional cannibalization rates. The critical distinction is between price-sensitive traffic drivers and margin-accretive tail categories.
A national chain drops private-label bread prices in your market. Ward detects the shift within 24 hours and models impact: nearby stores show a traffic decline among bread buyers who also carry full baskets. Ward recommends matching on the highest-velocity bread SKUs while raising prices on complementary deli items where elasticity is low, recovering traffic with a net-positive margin result.
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 Grocery pricing problems Ward catches.
Root causes, not just alerts. See it on your data.
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