CASE 02

MANUFACTURING

Invoice extraction, entry and inventory matching with staff focused on exceptions.

A Hong Kong manufacturer processed around 3,000 invoices monthly, with significant time spent entering and matching data.

Client context

The client is a Hong Kong manufacturer processing around 3,000 invoices from different suppliers each month. Document formats, field names and product data were not fully consistent, so staff entered each invoice and matched it against inventory and existing records.

Monthly volumeAround 3,000 invoices
Document sourcesMultiple supplier formats
Matching requirementInventory and existing records

Original workflow

01

Staff read each invoice and entered it into the required system

02

They then matched it manually against inventory and related records

03

Format differences, duplicates and mismatches required individual checks

BOTTLENECK

Most time was spent on repetitive, rule-based entry and checking, while only a small number of exceptions required experienced judgment.

What was built?

01

Receive and classify invoice files automatically

02

Extract required fields and validate format and content

03

Match against inventory and existing records

04

Send low-confidence, duplicate or mismatched cases to a human queue

Results and measurement basis

Cases are anonymised and every metric includes its measurement basis rather than presenting a claim without context.

10×

faster invoice processing

Based on average handling falling from around 10 minutes to around 1 minute per invoice.

3,000

invoices processed monthly

Based on monthly system processing records.

99%

fewer inventory matching errors

Based on identified mismatches before and after launch; individual months vary.

What changed after launch

01

Invoices are received, classified and extracted automatically, reducing manual entry

02

The system matches data against inventory and existing records automatically, reducing matching errors

03

Duplicates, mismatches and low-confidence cases are flagged automatically so staff can focus on exceptions

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