Grocery assortment without leaving Blue Yonder
Grocery retailers on Blue Yonder run assortment through Ward. 30,000+ SKUs across your stores, keeps a running read on around the clock.
How Ward turns Blue Yonder data into grocery assortment
Assortment Planning is a insight card type Ward runs continuously. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
For Grocery & Supermarket retailers, that means watching 30,000+ SKUs over stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
Under the hood. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Setup: Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.
Capabilities
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
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.
What Ward has eyes on.
Ward reads straight from demand forecasts, replenishment recommendations, allocation plans from Blue Yonder on a read-only connection. Nothing is written back, and your Supply Chain configuration does not change. Blue Yonder stays the system of record.
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.
Ward keeps a running read on 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.
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.
Why this combination
is its own problem.
Assortment Planning behaves differently in grocery retail than it does anywhere else. The fleet shape, the SKU count, and the speed of the category all change what counts as a real signal and what is noise. Ward is tuned to the grocery version.
- 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 the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Blue Yonder. Ward reads straight from demand forecasts, replenishment recommendations, allocation plans and starts building baselines. First 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 daily, each with root cause and a recommended move. 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 Blue Yonder
Ward layers on top of Blue Yonder demand planning and replenishment. Ward watches what Blue Yonder recommends and flags when actual diverges from plan.
Setup: Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.
Data Ward reads from Blue Yonder
Impact metrics with Blue Yonder
Data lake enrichment
Ward enriches Blue Yonder data with: Demand forecasts, POS actuals, Weather & events, Supplier fill rates, Competitor data
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 Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy. Data points include: Demand forecasts, Replenishment recommendations, Allocation plans, Exception alerts.
Yes. Ward reads Blue Yonder data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
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
See what Grocery assortment 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.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.