What challenges did Lion Parcel face before AI implementation?

Finance staff manually re-entered Delivery Order information from physical documents into Excel, which was time-consuming and labor-intensive. Manual transcription increased the risk of errors in shipment and financial data. Paper-based documents were difficult to organize, search, and retrieve, limiting traceability and scalability.

What was the impact of the AI solution?

Automated OCR reduced manual data entry and improved processing efficiency. Automated field extraction, user validation, duplicate detection, and confidence-based verification improved data accuracy. A centralized searchable repository improved traceability, audit readiness, and record management.

Source

AlibabaEvidence: Low40/100

Lion Parcel uses Qwen-VL-Plus OCR to automate finance document processing

Use case typeDocument processing automationUpdated 1 day ago

Lion Parcel is a logistics and delivery provider operating across Indonesia and more than 50 countries. Its Finance team previously manually transcribed Delivery Order documents from hardcopy into spreadsheets, creating a labor-intensive process with human-error risk and limited traceability as shipment volumes grew. Lion Parcel used Alibaba Cloud Model Studio with Qwen-VL-Plus OCR to extract structured document fields, validate them in a backend workflow, store them in a centralized database, and export standardized finance reports.

Organization: Lion Parcel
Industry: Logistics
Location: Indonesia
Published: July 2026

Reported outcomes

Strategic outcomes

Cost efficiencyReduced manual document entryOther strategic outcomeImproved financial data accuracyRisk & complianceImproved audit readiness and traceability

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Lion Parcel
Provider: Alibaba
Maturity: Unknown
Linked source: Alibaba Cloud Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 2 of 2

1Document processing automation
2Invoice processing

Finance staff manually re-entered Delivery Order information from physical documents into Excel, which was time-consuming and labor-intensive.
Manual transcription increased the risk of errors in shipment and financial data.
Paper-based documents were difficult to organize, search, and retrieve, limiting traceability and scalability.

Alibaba Cloud Model Studio running Qwen-VL-Plus performed OCR on printed and handwritten Delivery Order images and returned structured JSON.
A backend processing layer validated and normalized fields, applied duplicate detection and confidence-based verification, and stored results in a centralized database.
The workflow supported downstream export into standardized finance reporting formats.

Technologies

Alibaba Cloud Model Studio Qwen-VL-Plus Object Storage Service

Automated OCR reduced manual data entry and improved processing efficiency.
Automated field extraction, user validation, duplicate detection, and confidence-based verification improved data accuracy.
A centralized searchable repository improved traceability, audit readiness, and record management.

Architecture

API calls send parcel-label or Delivery Order images to Alibaba Cloud Model Studio, where Qwen-VL-Plus performs OCR and returns structured fields. A backend processing layer validates and normalizes the fields, stores them in a centralized database, and exports standardized finance outputs.

Sources & evidence1

Evidence: Low40/100Evidence strength