Assortment Planning for Grocery on RELEX, built for merchandising
Your category managers are drowning in spreadsheets. Ward detects grocery assortment movement in your RELEX data early, with the cause and what to do about it attached.
How a grocery VP Merchandising runs assortment on RELEX Solutions
For Grocery & Supermarket retailers, that means continuous review 30,000+ SKUs across stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
Your category managers are drowning in spreadsheets. Ward filters to what a VP Merchandising can act on and drops the rest.
Assortment Planning is a finding type Ward runs continuously. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
The mechanism. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
How the connection works. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
Capabilities
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
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 Assortment matters for Grocery retail
Every assortment addition displaces something else, so the real question is incremental contribution after cannibalization and basket effects. Ward clusters stores by demographics, traffic, and competitive set, then benchmarks SKU performance at the cluster level to produce assortment recommendations that go beyond national planograms.
Why this combination
is its own problem.
RELEX Solutions was built for transactions, not for merchandising 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 Cluster definitions based on store size or banner miss the demographic signal; two same-size stores can have completely different shopper profiles.
- 02 New-item performance gets evaluated against chain averages instead of cluster benchmarks, so winners in one cluster are killed because they fail in another.
Benchmarks. A typical grocery store carries 30,000-45,000 SKUs; the bottom 20% generate under 2% of revenue. Cluster-aware assortment usually frees 5-12% of facings without a revenue drop, which translates to space for 1,500-3,000 better-fit SKUs per store cluster.
What Ward has eyes on.
Stock what sells. Cut what doesn't. 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.
Every one of your stores gets its own baseline. Ward monitors fill rate, shrinkage %, fresh waste % against it and brings up 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.
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 combines it with external context your Supply Chain does not carry: weather, local events, competitor pricing.
At the metric level. Ward tracks SKU productivity (revenue per facing), incremental contribution, substitution patterns, and cluster-level demand elasticity, all weighted against supplier fill rates and promotional obligations.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to RELEX Solutions. Ward ingests demand forecasts, space plans, workforce forecasts 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 returns daily cards every morning, each with what caused it and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many findings 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
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
Grocery KPI impact
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
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.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Merchandising decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks SKU productivity (revenue per facing), incremental contribution, substitution patterns, and cluster-level demand elasticity, all weighted against supplier fill rates and promotional obligations.
A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores that match the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram leaves revenue on the table in urban stores while it ties up slow-moving inventory in rural ones.
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
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Root causes, not just alerts. See it on your data.
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