A home Head of Procurement running fill rate on SAP
Every empty shelf is a lost sale. Ward runs it on SAP data over your home footprint, scoped to procurement.
The full picture: home fill rate, SAP data, Procurement decisions
A Home Improvement operator is tracking 50,000+ SKUs throughout stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
Merchandising wants Ward. You sign the contract. Ward delivers daily cards scoped to procurement decision-making.
Here is fill rate monitoring in plain terms. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
The mechanism. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
The short list
- Estate-wide fill rate dashboard
- Threshold-based alerting
- Store-vs-estate benchmarking
- Category-level drill-down
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled the spring pre-build against the last three seasons and the weather signal for the 22 Southeast stores.
| Signal | Finding |
|---|---|
seasonal_prebuild | Mulch on-hand hit 61% of plan the week temps broke 70°F |
dc_push | DC push started 9 days after the first-warm-week trigger, LY it was 2 |
attach.project | Soil and edging attach fell 18% at stores that gapped on mulch |
Recommend: tie the push trigger to the 10-day forecast instead of the calendar week, pre-position two truckloads at the 8 stores that gapped, and re-set the attach endcap.
seasonal_prebuild…
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 |
|---|---|---|
epicor_pos_transactions | import | 2m ago |
epicor_special_orders | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
retail_seasonal_prebuild | import | 1h ago |
retail_sku_velocity | import | 1h ago |
retail_pro_account_sales | import | 1h ago |
retail_weather_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
pro-team-read-accounts | permit | Model::"pro_account_sales" |
vendor-blocked | forbid | Model::"labor_*" |
supply-read-orders | permit | Model::"special_orders" |
Why Fill Rate matters for Home retail
A store can report 96% fill rate while missing the one fastener that completes every deck project basket. Ward monitors fill rate through a project-basket lens, flagging when project-critical items drop below threshold even if aggregate availability looks healthy.
What Ward has eyes on.
Coverage is store by store, category by category. Ward scans continuously project basket value, seasonal accuracy, long-tail turn across 50,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven stores are not.
Ward reads POS transactions, inventory positions, purchase orders from SAP Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. SAP stays the system of record.
The fill rate model runs every morning, not on a reporting calendar. It flags the pattern, traces what caused it, and attaches what to do about it before the number reaches a review deck.
At the metric level. Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
Why this combination
is its own problem.
A Head of Procurement does not need the fill rate model explained. They need to know which stores moved, why, and what to do by end of day. Ward writes the finding at that altitude.
- 01 Fill rate measured at the SKU level masks project-basket completion; a deck-project basket that needs 12 SKUs has a much lower complete-basket availability than any individual SKU's availability suggests.
- 02 Endcap and seasonal positioning is tracked separately from fill rate, but the customer experience is shaped by both, a fully-stocked product hidden in a non-seasonal aisle still functions like a stockout.
Benchmarks. Home improvement project-basket completion: healthy chains run 88-94% on the top-100 project baskets; below 80% on a top basket cuts category revenue 3-7% over the affected weeks. Pro customer fill rate matters disproportionately, Pros typically defect after 2-3 stockouts on flagged SKUs.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of SAP Retail with read-only credentials and begins ingesting POS transactions and inventory positions. No config changes on your side. First cards 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: steady state
Ward hands you cards daily, each with what caused it and a recommended move. 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 SAP Retail
Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
Data Ward reads from SAP
Impact metrics with SAP
Data lake enrichment
Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates
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 room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
Home KPI impact
Frequently asked questions
Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. For Home retail specifically, Ward monitors 50,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Project basket value, Seasonal accuracy, Long-tail turn, Pro customer share, Attachment rate at the store-category level. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.
Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Home-specific insight cards. No custom development required.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
Estate-wide fill rate looks healthy, but Ward's project-basket analysis shows the "deck build" basket has far lower complete-basket availability because a single specialty fastener is out of stock. A standard fill rate report would bury this item among 50,000 others. Ward surfaces it through basket completion analysis, and the supply chain team expedites the item to restore project-level availability within days.
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 Home fill rate problems Ward catches.
Root causes, not just alerts. See it on your data.
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