Background
A leading dairy processor in Australia sources milk from hundreds of farmers across the country. The company relies on timely and accurate information from its suppliers to manage milk collection, quality assurance, and logistics.
Traditionally, farmers submitted critical documents such as milk supply reports, pickup requests, and quality details via fax. These paper faxes were manually sorted, reviewed, and entered into the company’s backend systems by administrative staff, forming a labour-intensive and error-prone process.
The Challenge
The manual handling of faxed documents presented several operational challenges for the dairy processor. Staff spent significant time sorting through incoming faxes, deciphering handwritten notes, and entering data into digital systems. This process was not only slow but also susceptible to human error, leading to occasional data inaccuracies that could disrupt milk collection schedules or impact quality tracking.
The reliance on paper faxes also created an operational bottleneck, as documents could be misplaced or delayed, and staff were burdened with repetitive, low-value tasks. These inefficiencies increased operational costs and posed risks to data integrity and business continuity while limiting the company’s ability to scale operations.
Our Approach
Our team proposed leveraging Azure AI Document Intelligence, a cloud-based platform that combines advanced Optical Character Recognition (OCR) with machine learning, to automate the extraction and processing of data from faxed forms.
We focused on building a solution that could handle both printed and handwritten text, adapt to the company’s unique document layouts, and integrate with their existing backend systems for real-time data flow.
Discovery and Planning
We began with a comprehensive discovery phase, working closely with administrative staff and the IT team to understand existing document workflows.
We conducted onsite observations, stakeholder interviews, and document analysis to identify pain points and opportunities for automation.
Our team catalogued the various document types, noting their structures, common data fields, and the challenges staff faced when processing them. This research informed a detailed project roadmap that aligned with business objectives and technical requirements.
User-Centered Design
Armed with a deeper understanding of the current process, we developed user personas representing different stakeholders in the document workflow, from farmers submitting faxes to administrative staff and downstream data consumers.
We created journey maps to visualise the existing process and designed a future state that would streamline operations while maintaining necessary human touchpoints. Our design approach prioritised intuitive interfaces for document review and validation, ensuring that staff could easily interact with the new system and maintain oversight of critical data.
Development Process
We employed an agile development methodology, working in two-week sprints to deliver incremental insights and gather continuous feedback. We established a development environment that mirrored the client’s production systems and implemented Azure DevOps for version control and CI/CD pipelines.
The development process included regular demonstrations to stakeholders, allowing for real-time adjustments based on user feedback. We also conducted extensive testing with actual faxed documents to train and refine the AI models, ensuring they could handle the variability in document quality and format regularly encountered.
The Solution
Ample Tech designed and implemented a robust, cloud native automation pipeline, utilising both prebuilt and custom models within Azure AI Document Intelligence. Prebuilt models managed standard document types, while custom models trained on historical faxes handled unique layouts and handwritten annotations.
Intelligent Document Processing Engine
The core of our solution was an intelligent document processing engine built on Azure AI Document Intelligence. This engine could recognise and process multiple document types, including milk supply reports, quality test results, and pickup schedules. We trained custom models using historical documents, enabling the system to understand the specific layouts and data fields unique to their operations.
The engine could extract both typed and handwritten text with high accuracy, converting unstructured fax data into structured, actionable information.
Automated Workflow Management
We implemented an automated workflow system that managed the entire document lifecycle. When farmers sent faxes, they were automatically digitised and routed to Azure Blob Storage.
Azure Functions triggered the document processing pipeline, which extracted data, validated it against business rules, and routed it to the appropriate downstream systems.
The workflow included exception handling for documents that couldn’t be processed automatically, creating tasks for human review while maintaining an audit trail of all processing steps.
Integration and Data Management
Our solution seamlessly integrated with existing ERP and logistics systems through secure APIs. Extracted data was transformed to match the required formats and validated before being sent to backend systems. We implemented a centralised data repository that maintained the original documents alongside the extracted data, providing a single source of truth for all milk supply information.
User Interface and Reporting
For administrative staff, we developed an intuitive web interface that provided visibility into the document processing pipeline. Users could view incoming documents, monitor processing status, and review exceptions that required human intervention.
The interface included powerful search capabilities, allowing staff to quickly locate specific documents or data points. We also implemented comprehensive reporting dashboards that provided insights into processing volumes, accuracy rates, and operational efficiency, enabling continuous oversight of the system.
The Impact
The automation of faxed document processing delivered measurable benefits for the dairy processor:
- Processing time for milk supply documents was reduced by over 82%, with most faxes processed and integrated into backend systems within minutes instead of hours.
- Data entry errors dropped by 94%, thanks to the high accuracy of Azure’s OCR and custom extraction models, combined with targeted human validation for low confidence cases.
- Administrative costs related to document handling were reduced by 73%, freeing staff to focus on higher value activities such as supplier engagement and quality improvement.
- The solution enabled real-time visibility into milk supply and logistics data, supporting better decision making and more agile operations.
By modernising document processing operations with Ample Tech’s solution, the dairy processor improved data accuracy, reduced operational risk, and empowered staff to focus on strategic initiatives.
The project stands as a testament to the transformative power of intelligent automation in the agribusiness sector, setting a new benchmark for efficiency and reliability in supply chain management.
