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Find the overdue orders. Check the work behind the answer. Give your team a clear next step. Follow one analysis from start to finish.

Follow the walkthroughGet the SQL and data

A fixed example with fictional data. No live AI session or customer database connection.

KoleOrder review
Interactive example

The question

Which open orders have missed their promised date?

The sample answer

Start with East.

It has two of the four overdue orders. Check those records first, then investigate what caused the delay.

Read the handoff note
Overdue orders
4
Warehouses
3
Order value
$600
Overdue orders and order value by warehouse
WarehouseOrdersValue, USD
East2$360
South1$165
West1$75

Order value is not lost revenue. The records do not establish a cause.

12 fictional recordsSnapshot: September 21, 2026 · 00:00 UTC

01 / The answer

Four orders to look into.
A clear place to start.

East accounts for half of the overdue orders. That gives the operations lead a starting point, with the definition and source records close at hand.

Define the question.

Overdue means a promised date before September 21, 2026, with an order still processing or shipped. Delivered and cancelled orders are excluded.

Snapshot: September 21, 2026 · 00:00 UTC

Find the affected orders.

ORD-101 and ORD-102 are in East. ORD-103 is in West; ORD-104 is in South. These four orders have a combined order value of $600.

4 overdue orders across 3 warehouses

Keep the limits visible.

Order value is not lost revenue. The records do not establish a cause. Check inventory, dispatch events, and carrier updates before drawing a conclusion.

A starting point for investigation

02 / The evidence

The answer is useful.
The work is yours to check.

Read the query, inspect the records, or reproduce the result yourself. Everything you need is here, without an account.

order-review.sql

Read-only query
SELECT warehouse,
       COUNT(*) AS late_orders,
       SUM(order_value_usd) AS order_value_usd
FROM sample_orders
WHERE status IN ('processing', 'shipped')
  AND promised_date < '2026-09-21'
GROUP BY warehouse
ORDER BY late_orders DESC, warehouse;

Groups overdue orders by warehouse, then sorts by the number of orders.

Take the example with you.

The CSV contains all 12 orders. The SQL file creates a sample table, inserts the records, and runs the query. Use a scratch database.

Download source records / CSVDownload setup & query / SQL

Uses SQL compatible with DuckDB, PostgreSQL, and SQLite. This example was verified in SQLite.

Inspect all 12 source records
Fictional orders · September 21, 2026 · 00:00 UTC
OrderWarehouseStatusPromised dateValue, USD
ORD-101Eastprocessing2026-09-18$120
ORD-102Eastshipped2026-09-20$240
ORD-103Westprocessing2026-09-19$75
ORD-104Southshipped2026-09-20$165
ORD-105Eastprocessing2026-09-21$90
ORD-106Westshipped2026-09-22$210
ORD-107Eastdelivered2026-09-18$150
ORD-108Westdelivered2026-09-19$80
ORD-109Southdelivered2026-09-19$190
ORD-110Eastdelivered2026-09-20$110
ORD-111Southdelivered2026-09-20$130
ORD-112Westcancelled2026-09-17$300
Learn to check joins and data quality

03 / The handoff

Give the next person
more than a number.

A useful handoff explains what happened, where to look, and what still needs checking. Keep the question and the evidence with the next action.

In SyneHQ, use Quantum Lab and saved queries to keep the analysis accessible. Carry the findings into a report your team can edit and share.

Explore the analysis workspace

Example handoff note

Review East’s overdue orders first.

Operations review · September 21, 2026 · 00:00 UTC

EastStart here
ORD-101 · ORD-102
West
ORD-103
South
ORD-104

Check ORD-101 and ORD-102 with the East warehouse lead. Record the cause and next action for each affected order, then follow up with West and South.

The cause is still unconfirmed. Re-run the query against a fresh snapshot before deciding whether the backlog has improved.

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