Fill Rate · NetSuite · Director Store Ops

Fill Rate Monitoring for Director of Store Operations, read from Oracle NetSuite

Ward reads straight from sales orders, inventory, purchase orders from Oracle NetSuite, runs fill rate on it, and returns the store operations read daily.

How a Director Store Ops runs fill rate off Oracle NetSuite

Managing 800 stores from a spreadsheet is insane. Ward writes the finding at the altitude a Director Store Ops works at.

Fill Rate Monitoring, in one sentence. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.

How it runs. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.

How the connection works. Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.

Capabilities

  • Threshold-based alerting
  • Store-vs-estate benchmarking
  • Category-level drill-down
  • Estate-wide fill rate dashboard
app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did Store 37 miss target last week?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83%, backroom replenishment lag at 2–4p
promo.liftBOGO 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.

8 parallel queries 3 sources cited confidence 0.92
Show me how to fix the staffing mismatch.
You · 9:43 AM
Labor Agent · drafting schedule diff
Querying labor_scheduling
Ask anything, Ward routes to the right agent. Cmd+K

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"
Fill Rate on NetSuite data, live product demo.

What Ward has eyes on.

The fill rate model runs each day, not on a reporting calendar. It detects the pattern, accounts for the cause, and attaches what to do about it before the number reaches a review deck.

Ward reads NetSuite rather than replacing it. Sales orders, inventory, purchase orders come across on a read-only connection, get enriched with contextual data, and come back as cards. Your ERP is untouched.

Why this combination
is its own problem.

A Director Store Ops rarely logs into NetSuite. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward connects to Oracle NetSuite with read-only credentials and begins ingesting sales orders and inventory. No config changes on your side. First daily cards arrive in two days.

  2. 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.

  3. 03

    Weeks 4 to 12: steady state

    Ward delivers insight cards every morning, each with the driver and what to do about it. 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 Oracle NetSuite

Ward integrates with NetSuite SuiteCommerce, inventory management, and financials. Mid-market retailers get enterprise-grade insight cards.

Setup: Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.

Data Ward reads from NetSuite

Sales orders
Inventory
Purchase orders
Customer records
Financial summaries
Item fulfillment

Impact metrics with NetSuite

Inventory Accuracy
Discrepancies reconciled live
POS and fulfillment data cross-checked against NetSuite counts.
Order Fill Rate
Stockouts preempted
Demand forecasting layered onto NetSuite purchase orders.
Gross Margin
Margin erosion flagged
Pricing drift and vendor cost creep caught across financials.
Cash Conversion Cycle
Days of supply reduced
Demand-inventory alignment frees tied working capital.

Data lake enrichment

Ward enriches NetSuite data with: Sales orders, Weather & events, Customer segments, Vendor performance, Market pricing data

Managing 800 stores from a spreadsheet is insane.

Pain points
  • ×Morning check-ins rely on phone calls and email chains
  • ×No single view of which stores need attention today
  • ×Labor scheduling is disconnected from demand signals
  • ×Planogram compliance is checked manually, quarterly
  • ×Exception management is reactive and inconsistent
How Ward helps
  • Morning brief delivered at 06:47 with prioritized action list
  • Estate-wide heat map of store performance, updated hourly
  • Staffing recommendations correlated with predicted traffic
  • Planogram compliance anomalies detected and flagged
  • Consistent exception handling with recommended actions

Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research

Frequently asked questions

Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance. Data points include: Sales orders, Inventory, Purchase orders, Customer records, Financial summaries, Item fulfillment.

Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.

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.

See what fill rate 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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info