Price Optimization: Grocery retail, RELEX data, Loss Prevention decisions
A grocery Head of LP on RELEX should not be hunting for pricing problems. Ward brings up them daily.
How a grocery Head of LP runs pricing on RELEX Solutions
Price Optimization, in one sentence. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
A Grocery & Supermarket operator is tracking 30,000+ SKUs throughout stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
Shrinkage costs you more than you think. Ward finds out where. Ward pulls forward the indicators that change a loss prevention decision.
What Ward does with that: Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
The connection itself. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
Capabilities
- Category-level price sensitivity
- Competitive price monitoring
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
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.
Why this combination
is its own problem.
RELEX Solutions was built for transactions, not for loss prevention decisions. The data is right there and it is in the wrong shape. Ward pulls from demand forecasts and space plans and rewrites them as a decision.
- 01 Beer and tobacco price changes ripple through unrelated baskets; treating them as standalone categories misses the traffic effect.
- 02 Chain-level KVI lists are stale within a quarter; the items customers actually compare drift with promo cycles and competitor activity.
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 Ward has eyes on.
The pricing model runs each day, not on a reporting calendar. It flags the pattern, accounts for what caused it, and attaches a recommended move before the number reaches a review deck.
Ward tracks 30,000+ SKUs over your stores, at the store-category level rather than the chain roll-up. The metrics under watch include fill rate, shrinkage %, fresh waste %. A roll-up hides a single-store problem inside a healthy average, which is how fresh waste & spoilage stays invisible for a quarter.
The connection to RELEX Solutions is read-only and runs on your schedule. Ward ingests demand forecasts, space plans, and the rest of the feed, then stitches together it with external context your Supply Chain does not carry: weather, local events, competitor pricing.
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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads from RELEX Solutions with read-only credentials and begins ingesting demand forecasts and space plans. No config changes on your side. First findings arrive inside the first 48 hours.
-
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
Daily 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
Shrinkage costs you more than you think. Ward finds out where.
- ×Shrinkage lands as a year-end surprise
- ×Cannot distinguish theft from spoilage from admin error
- ×High-shrinkage stores only identified during audits
- ×No correlation between operational changes and loss patterns
- ×Exception-based reporting misses slow-bleed patterns
- ✓Store-level shrinkage tracking with cause attribution
- ✓Anomaly detection flags stores deviating from estate average
- ✓Receiving dock discrepancy patterns identified automatically
- ✓Correlation analysis links operational changes to loss shifts
- ✓Trend analysis catches slow-bleed patterns audits miss
US retail shrinkage hit $112.1 billion in 2022. Up 19.4% year over year. Source: National Retail Federation
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.
Shrinkage costs you more than you think. Ward finds out where. Ward solves this with automated insight cards: Store-level shrinkage tracking with cause attribution. Anomaly detection flags stores deviating from estate average. Receiving dock discrepancy patterns identified automatically.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Loss Prevention 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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