From Manual Chaos to Automated Efficiency: 75% Fewer Errors in Delivery Note Capture

Indexing, Separation and Classification

Solution

A major Manufacturing and Distribution company needed to modernize the way delivery notes were captured and processed across multiple sites. Drivers scanned documents using outdated technology, creating inconsistent image quality and slowing down downstream indexing and data entry.

The business faced several key challenges:

• Increasing the number of collection/scan points across all distribution sites.

• Replacing legacy capture technology that was unreliable and difficult to maintain.

• Ensuring adoption among distribution partners and truck drivers with minimal process disruption.

• Processing nearly 100,000 pages per year, many of them in low-quality scans, which reduced barcode readability and batch separation accuracy.

To scale operations efficiently, the company needed a decentralized, automated solution that could intelligently separate batches, extract barcode or preprinted field data, and output structured information directly into the Management System — all within an 8-week implementation window.

Aluma deployed an end-to-end automated scanning and indexing workflow built to support high-volume, decentralized capture.

The new system delivered:

• Decentralized scanning, enabling drivers across all sites to upload delivery notes instantly.

• Automated batch separation using intelligent rules, even with mixed or low-quality images.

• High-accuracy barcode extraction and fallback extraction of preprinted fields when barcodes were unreadable.

• Customized output formats that integrated seamlessly with the company's management system, ensuring smooth downstream processing.

• Modern, easy-to-use technology that improved adoption among partners and eliminated the inefficiencies of the legacy system.

This approach turned a scattered, manual process into a fast, standardized, and reliable document pipeline.

Impact

65% Reduction in Document Processing Costs

Automation eliminated manual indexing and rework, lowering per-document processing costs and reducing reliance on labor-intensive back-office functions.

75% Reduction in Errors, Exceptions & Rework

Higher confidence in captured data reduced downstream errors, delays, and exceptions—supporting smoother operations, faster reconciliation, and more reliable reporting.

Scalable Growth with Near-Zero Incremental Cost

The decentralized capture model enabled rapid onboarding of new sites and partners, allowing the business to scale distribution volumes without proportional increases in operational cost or complexity.