Achieving Source-To-Pay Automation at Scale: How to Automate Unstructured Documents

A New Imperative for Procurement and Finance Leaders in a Digital Age

As we enter an era of digital transformation, procurement and finance leaders are faced with the challenge of embracing new technologies to improve the efficiency and effectiveness of their source-to-pay processes. The adoption of automation tools presents a unique opportunity for organisations to revolutionise their procurement and financial operations, ultimately increasing their competitive advantage and enhancing their ability to attract and retain top talent. To truly reap the benefits of this digital revolution, organisations must move beyond simple task-based automation and workflow systems, harnessing the power of advanced cognitive capabilities for document processing and management.

The Pervasiveness of Documents in Our Lives

Documents form a crucial aspect of both our personal and professional lives, providing a means for communication, record-keeping, and decision-making. They can be categorised into three main types: structured, unstructured, and semi-structured. In the context of procurement and finance, organisations striving for end-to-end automation often grapple with the high volume of unstructured documents, which significantly limits their ability to scale their operations successfully. Intelligent Document Processing (IDP) offers a promising solution to this challenge, enabling organisations to extract valuable data from unstructured documents and paving the way for automation at scale.

Introducing Intelligent Document Processing: A Solution for Unstructured Document Challenges

Intelligent Document Processing represents the next generation of automation, allowing organisations to capture, extract, and process data from a wide variety of document formats. IDP utilises advanced Artificial Intelligence (AI) technologies, such as natural language processing (NLP), computer vision, deep learning, and machine learning (ML), to classify, categorise, and extract relevant information from documents, as well as validate the accuracy of the extracted data.

IDP vs. OCR: A Comparative Analysis

Traditional Optical Character Recognition (OCR) technologies, as well as those integrated into Robotic Process Automation (RPA) platforms, often struggle with handling varied document formats, interpreting natural language, and dealing with distortion, ultimately resulting in lower accuracy and straight-through processing rates. IDP, on the other hand, combines the functionality of OCR with advanced techniques, such as NLP, ML, and AI, to analyse and comprehend the meaning of data contained within documents. This allows for a more accurate and complete understanding of the information, as compared to OCR alone.

Addressing the Limitations of Robotic Process Automation

Organisations that employ RPA as part of their automation journey may eventually find themselves facing a plateau in terms of the opportunities for further process improvement. While RPA is highly effective for executing task-based, rules-based processes, it has significant limitations when it comes to processing unstructured documents. IDP can help organisations overcome these limitations by extracting data from such documents and providing RPA bots with the necessary information to complete tasks. This is particularly relevant in the context of invoice processing, purchase order processing, and contract management within the source-to-pay process.

Use cases across Source-To-Pay

Intelligent Document Processing (IDP) has numerous applications in procurement, including:

1. Purchase Order Processing: IDP can be used to automate the processing of purchase orders, extracting key data such as order number, item description, quantity, and price from structured documents. This can reduce the manual effort required to process purchase orders, improve accuracy, and speed up the procurement cycle.
2. Invoice Processing: IDP can be used to extract data from invoices, such as vendor information, purchase order number, invoice number, and line item details. This can automate the matching of invoices to purchase orders, reducing the risk of errors and delays in payment.
3. Contract Management: IDP can be used to extract key data from contracts, such as contract start and end dates, payment terms, and termination clauses. This can help organisations to better manage their contracts, reduce the risk of non-compliance, and improve overall procurement efficiency.
4. Vendor Onboarding: IDP can be used to automate the processing of vendor documents, extracting key information and populating it into the vendor management system. This can reduce the manual effort required for onboarding vendors, improve accuracy, and speed up the process.
5. Supplier Performance Monitoring: IDP can be used to extract data from supplier performance reports, such as delivery times, quality metrics, and pricing information. This can help organisations to better monitor supplier performance, identify trends, and make data-driven decisions about supplier selection and contract renewals.
6. Request for Proposal (RFP) Analysis: IDP can be used to analyse RFPs, extracting key information such as required qualifications, deliverables, and evaluation criteria. This can help procurement teams to better understand the requirements of the RFP and make informed decisions about which vendors to select.
7. Supplier Risk Management: IDP can be used to extract data from supplier risk reports, such as financial data, credit ratings, and legal records. This can help organisations to better understand the risks associated with their suppliers and make data-driven decisions about supplier selection and risk mitigation strategies.
8. Contract Review and Negotiation: IDP can be used to extract key information from contracts, such as payment terms, termination clauses, and deliverables. This can help legal and procurement teams to more quickly identify key issues in the contract and negotiate more effectively.
9. Purchase Requisition Processing: IDP can be used to extract data from purchase requisitions, such as the item being requested, the quantity, and the requested delivery date. This can help procurement teams to more quickly process purchase requisitions and reduce the risk of errors.
10. Compliance Monitoring: IDP can be used to extract data from documents such as contracts, invoices, and purchase orders to ensure that they comply with relevant regulations and policies.

Implementing IDP 

Extracting data using Intelligent Document Processing (IDP) typically involves the following steps:

1. Document Scanning and Uploading: The initial step in the IDP process involves scanning or uploading the document that requires processing. The document can be in paper or digital format, and can be uploaded directly into an IDP platform or sent via email.
2. Document Classification: Once the document has been uploaded, the IDP system will automatically classify the document based on its content and structure. For example, the system may identify a document as an invoice, purchase order, or contract.
3. Data Extraction: Once the document has been classified, the IDP system will extract data from the document based on pre-defined rules and machine learning algorithms. The data extraction process may involve identifying specific fields, such as the invoice number, date, and total amount, and extracting the data into structured formats.
4. Data Validation: Once the data has been extracted, the IDP system will validate the accuracy of the extracted data against pre-defined business rules and algorithms. For example, the system may verify that the invoice number is valid and matches the vendor and purchase order.
5. Data Integration: Once the data has been validated, it can be integrated with other systems and processes, such as accounts payable, procurement, or contract management. The IDP system may use APIs, web services, or other integration methods to ensure that the extracted data is seamlessly integrated with other systems.
6. Reporting and Analytics: Once the data has been extracted and integrated, the IDP system can generate reports and provide analytics to help organizations better understand their document processes. For example, the system may generate reports on invoice processing times, contract compliance, or purchase order accuracy.

Leading vendors in the IDP space include Automation Anywhere, ABBYY, UiPath, Kofax, AntWorks and Blue Prism. These vendors offer comprehensive functionality and a wide range of features to help organizations implement IDP effectively. By utilizing solutions from these top vendors, businesses can achieve more efficient, accurate, and cost-effective document processing, which in turn leads to improved operational efficiency and better decision-making.

Taking a Holistic Approach

To fully capitalise on the advantages of IDP, it is essential to integrate the technology within a holistic strategy for process optimisation and intelligent automation. At Hudson&Hayes, we support our clients in identifying opportunities for IDP through our distinct process transformation methodology, called Elevate. Subsequently, we drive end-to-end implementation via our four-step intelligent automation approach, known as Harmonise.

In Summary

In summary, Chief Procurement Officers and Chief Finance Officers can gain substantial benefits by embracing IDP for Source to Pay processes, such as enhanced efficiency, accuracy, and cost savings. IDP facilitates the automation of routine tasks, minimises human errors, and accelerates decision-making. By adopting IDP, organisations can maintain a competitive edge, streamline processes, and bolster their financial performance.