How AI is Revolutionising Procurement: Automating the Categorisation of Unstructured Data into a workable Taxonomy

Artificial intelligence revolutionising procurement

Procurement teams across industries, from food manufacturing to healthcare, grapple with the challenge of managing unstructured data. Purchase orders, invoices, and vendor information often come in varying formats, making it difficult to categorise and analyse procurement data effectively.

AI is stepping in to revolutionise procurement by automating the categorisation of unstructured data into industry-standard classifications like UNSPSC (United Nations Standard Products and Services Code). This page explores the benefits of AI-powered tools for procurement teams and why industries, particularly food manufacturing and healthcare, are adopting AI for efficient data categorisation.

The Importance of Categorising Procurement Data

What is Procurement Data?

Procurement data includes everything from supplier details, invoices, and purchase orders to product specifications. This data is crucial for tracking spending, managing vendors, and ensuring compliance. However, when this data is unstructured—stored in different formats, systems, or languages—it becomes difficult to manage efficiently.

The Role of UNSPSC Categories

The UNSPSC classification system is widely used for categorising products and services in procurement. It allows organisations to standardise procurement data, making it easier to analyse spending patterns and improve vendor management.

Challenges of Manual Categorisation

For industries like food manufacturing, manually categorising procurement data is time-consuming and error-prone. Procurement teams must wade through invoices and purchase orders that may include thousands of unique product lines. In healthcare, the challenge is even more complex due to the high volume of specialised medical supplies, equipment and medicines purchased across clinical departments. All this leaves less time to dedicate towards procurement team profiling their spend into meaningful direct and indirect categorisation, potentially missing out on the opportunity to make substantial savings by not looking at direct expenditure at the necessary granular level.

Manual processes often result in inconsistent or incorrect categorisation, leading to poor visibility into spend data, inaccurate reporting, and inefficiencies.

The Power of AI in Data Categorisation

What AI Brings to the Table

AI, particularly machine learning (ML) and natural language processing (NLP), transforms how procurement teams manage unstructured data. AI tools can automatically recognise patterns in data, classify products and services, and categorise them according to UNSPSC codes, all in real time. The system becomes smarter over time, learning from past transactions to improve categorisation accuracy

Benefits of AI Categorisation
	Increased Accuracy: AI reduces human error. In food manufacturing, where procurement teams deal with raw materials, ingredients, and packaging, AI ensures each item is categorised accurately, helping to track spending on specific categories like "dairy products" or "packaging materials."
	Speed and Efficiency: AI processes large volumes of data in minutes, a task that could take humans days. In healthcare, where hospitals handle thousands of supplier transactions daily, AI significantly cuts down on time spent categorising high-value purchases like medical devices or pharmaceuticals.
	Scalability: AI can scale to handle growing procurement needs, whether it’s categorising millions of transactions in a global food manufacturing supply chain or tracking specialised medical equipment purchases across multiple hospital locations in healthcare.
	Cost Reduction: By automating categorisation, AI frees up time and resources, reducing the need for large teams dedicated to data management.

Business Case for AI-Powered Categorisation

Efficiency and Time Savings

Automating procurement data categorisation results in substantial time savings. For example, a food manufacturer that manages a supply chain with thousands of ingredients and packaging materials could significantly cut the time spent on manual categorisation, allowing procurement professionals to focus on more strategic tasks like negotiating supplier contracts. Similarly, in healthcare, AI tools have reduced categorisation time for medical supply orders, speeding up purchase approvals and delivery of critical equipment.

Accuracy and Data Quality

AI significantly improves the accuracy of procurement data. A healthcare provider using AI tools to categorise medical equipment purchases can achieve significant reductions in errors compared to manual processes. In food manufacturing, automated categorisation improved data quality by ensuring consistent categorisation of raw materials, leading to more accurate spend analysis and forecasting.

ROI and Cost Savings

The ROI of AI-based procurement tools is compelling. A food manufacturing company that implements AI to categorise procurement data will cut down on administrative costs, drastically. In healthcare, AI has helped hospitals reduce procurement processing costs, leading to savings that can be redirected toward patient care. Furthermore, compare the cost of procuring a tool for internal usage on data that you know better than any other organisation versus the cost of engaging in a consultancy who charge a premium to categorise data often with minimal industry knowledge or insights into what purchase orders are and what they’re used for.

Compliance and Reporting

Industries like healthcare are highly regulated, and accurate procurement data is crucial for compliance with standards like ISO regulations. AI ensures accurate categorisation of medical products, helping healthcare providers stay compliant with industry regulations. In food manufacturing, AI helps ensure that procurement data is accurately categorised, making reporting to regulatory bodies easier and more consistent.

Use Cases and Industry Examples

Industries Benefiting from AI Categorisation

Food Manufacturing

Rejeb, A. et al. (2022) wrote about the range of research that has been conducted which supports the application of Big Data (BD) technology to improve procurement efficiencies and in particular to respond to the exponential increase in the generation of complex unstructured data. The actual potential scale of operational cost savings in the industry using BD technologies like ML and NLP is subject to extensive research and assumed to be in the billions of dollars which can be redirected into cost savings and improvement of food safety for consumers.

Healthcare

At the jurisidictional level in Australia, aggregate procurement bodies that support discrete health provider organisations and networks know that transforming to a reactive to leading procurement operating model, which includes embedding BD technologies to resolve significant data integrity issues associated with unstructured procurement data will save the health systems to the scale of hundreds of millions in as short a term as five years.

Comparison: AI vs Manual Methods
Dimension	AI-powered categorisation	Manual categorisation
Speed	Processes thousands of transactions in minutes	Takes days or weeks for manual categorisation
Accuracy	Reduces errors by as much as 75% (Strickland 2024)	Prone to human error, resulting in inconsistent data
Cost	Lowers administrative costs by automating processes	High labor costs for manual data entry
Scalability	Scales with procurement data growth effortlessly	Difficult to scale with increasing data volumes

Artificial intelligence sorts through thousands of records of unstructured data in minutes — AI is the smart conveyor belt system designed to sort through unstructured data quickly and more accurately than a human.

Key Features of an AI-Driven Procurement Tool

Automation: The tool's ability to automate and handle large volumes of unstructured data.
Customisation: How the AI can be tailored to specific procurement categories, including UNSPSC standards.
Integration: Seamless integration with existing procurement and ERP systems.

How to Implement AI Categorisation

Steps to Get Started:

Assess Current Procurement Data: Evaluate the state of your procurement data and the challenges of manual categorisation.
Select the Right AI Tool: Choose an AI tool designed to handle unstructured procurement data, such as one that supports UNSPSC categorisation.
Train the AI: Feed existing procurement data into the AI to teach it how to categorise transactions.
Integrate with Existing Systems: Ensure the AI tool integrates with your current procurement or ERP systems.

Overcoming Implementation Challenges

Both food manufacturers and healthcare providers often face challenges in migrating from manual to AI-driven categorisation. Common issues include data silos and integrating AI tools with legacy systems. However, with the right strategy, businesses can overcome these challenges and quickly realise the benefits of AI-powered procurement tools.

Long-Term Strategy:

Discuss the long-term benefits of adopting AI for procurement and how to scale it as part of a broader digital transformation strategy.

Conclusion

AI is transforming procurement processes across industries like food manufacturing and healthcare by automating the categorisation of unstructured data. Businesses that adopt AI-powered procurement tools experience faster, more accurate, and scalable solutions, leading to significant cost savings and improved operational efficiency.

Contact us for a demo and see how our AI-powered tool can automatically categorise your unstructured procurement data.

References

Rejeb, A., Keogh, J.G. & Rejeb, K. Big data in the food supply chain: a literature review. J. of Data, Inf. and Manag. 4, 33-47 (2022). https://doi.org/10.1007/s42488-021-00064-0

Strickland B (1 March 2024) The key to reducing errors with AI: Technology acceptance', accessed 15 September 2024. https://www.journalofaccountancy.com/news/2024/feb/the-key-to-reducing-errors-with-ai-technology-acceptance.html

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