How Data Analytics is Empowering Procurement Operations

Predictive analysis for procurement is now an essential tool for elevating the process. The tool can be used for a range of vital elements through the supply and value chain, from improving supplier management to mitigating risks and managing sustainability goals. Organisations can now use this tool to drive the business forward – achieving better outcomes and supporting long-term goals.

With AI being implemented more and more across supply chains globally, the landscape of harnessing analytics is constantly changing, so how are companies using the tool to advance the procurement process?

How does predictive analytics transform procurement?

Predictive analytics plays a pivotal role in transforming procurement processes by providing deep insights into future trends and potential outcomes. By analysing historical data, predictive analytics aids in enhancing demand forecasting, optimising costs, reducing spending and managing supplier risks. Jack Macfarlane, Founder and CEO of DeepStream, says the way predictive analytics has transformed procurement is by streamlining procurement processes. "It has the power to enhance demand forecasting, optimise costs, reduce spending, analyse and manage supplier risk," he said. "It can also detect fraud before it happens, saving a business on costly legal ramifications."

Keith Hausmann, Chief Customer Officer, Globality, adds: “Predictive analytics are of tremendous help in eliminating traditional research and analysis of procurement professionals, ultimately allowing them to be more efficient, productive and valuable to their business partners.

“Algorithms and other types of AI can predict the highest quality, best fit and most competitive suppliers for specific demands or sourcing needs, based on a wide variety of predictive signals harvested from a vast array of proprietary and public information.

“This reduces human bias, often leading to sole sourcing, and eliminates dozens of hours of imprecise supplier research typically done in the sourcing process.

“Furthermore, predictive technology can suggest and guide the person executing sourcing activity towards optimal commercial structures or ideally suited scoping parameters, ensuring that demand/specifications are neither too little nor too much.”

Its wide range of tools has had a huge impact on procurement.

Olivier Berrouiguet, Chief Executive Officer at Synertrade, highlights its ability to automate tasks, enhance data-driven insights and improve operational efficiency.

He adds: “It helps identify new business opportunities, such as entering new markets, refining processes to eliminate redundancies and discovering additional revenue pathways.” Olivier highlights its ability to forecast demand to optimise scalability of inventory, negotiate supplier contacts and, most importantly, reduce costs.

The role of AI and machine learning in enhancing procurement data analysis

AI and machine learning significantly enhance procurement data analysis by automating processes and providing advanced insights.

Heather Dawe, Chief Data Scientist, UK, and Head of Responsible AI at UST, is a well-known AI thought leader with over 25 years working in the field.

She says the tool is increasingly used to plan and manage supplier engagement, the contract and ensuring there are smooth and efficient supply chain operations.

She highlights consumer packaged goods (CPG) companies which use the technology: “CPG companies use machine learning to predict customer demand for different products and manage the supply of the product's raw materials based on this predicted demand. Procurement teams within retail companies use generative AI to identify potential new niche suppliers which can help the given retailer improve their sustainability rating.”

While Jack was keen to point out how AI, like its many implementations across procurement, is there to remove human error in analysis, he adds that it is “providing the advantage of predictive analysis to inform decision-making.

“AI can also detect patterns humans might miss and provide actionable insights. This data informs their supply chain strategies, optimising procurement and minimising waste and costs associated with overstock.”

Keith elaborates, saying: “In the past, most procurement data analysis, especially concerning sourcing or spend analysis, was done through expensive, time-consuming and ultimately inaccurate data cleansing, followed by traditional use of spreadsheet technologies or BI tools.

“Today, AI can glean insights and provide perspectives from vast arrays of unstructured data, or at the very least, perform analyses that were previously done through complex pivot tables and bespoke spreadsheet models.

“For instance, during a typical strategic sourcing event where multiple suppliers provide commercial and qualitative responses, AI can now instantly combine all responses, draw conclusions, highlight anomalies, run scenarios and provide actionable insights. This shift from manual data handling to AI-driven analysis saves time and enhances decision-making accuracy.”

Improving supplier risk assessment and management with data analytics

Data analytics significantly improves supplier risk assessment and management by monitoring key metrics such as financial health, delivery performance, sustainability and legal compliance. Often, supply chains are at the mercy of suppliers, both from a compliance standpoint and in providing the items needed to operate efficiently.

“Analytics can often be a crystal ball, giving teams the chance to be proactive rather than reactive.”

Olivier echoes that analytics can identify patterns indicating potential disruptions to the supply chain. He adds: “This allows procurement teams to proactively mitigate risk and ensure that high-quality products are reliably delivered on time to customers. The continual monitoring of metrics enables organisations to adapt processes quickly to changing market conditions, maximising efficiency and customer satisfaction.”

Heather explains how UST works with clients from across business sectors – typically global enterprises within the finance, retail, CPG, healthcare and technology verticals. “While procurement activities vary across these industries, all of course have procurement relationships and processes in place and supplier management is a core aspect of any procurement function,” she says.

Uncovering hidden costs and inefficiencies in procurement with data analytics

Data analytics helps uncover hidden costs and inefficiencies in procurement by scrutinising spending patterns, supplier performance and process inefficiencies.

With every penny so vital for a successful procurement operation, discovering those parts of the supply chain which are not working as effectively as possible is key - even more so if money is being left on the table, giving teams the power to reclaim it.

For Barath Narayanan, Global BFSI and Europe Geo Head at Persistent Systems, the analytics have pinpointed discrepancies in invoice processing, redundant purchases, underutilised contracts, overspending, supplier discrepancies and inefficient processes.

He says: “By uncovering these insights, organisations can negotiate better terms with suppliers, optimise inventory levels and streamline procurement workflows.” Barath also says how the cross-company data now available can work hand in hand to create datasets with the power to create an optimised end-to-end supply chain, a paramount benefit in today’s landscape.

Ensuring sustainability and regulatory compliance in procurement through data analytics

Sustainability is almost as vital to procurement teams as savings. The need to procure items with the least impact on the planet, as well as working with suppliers through codes of conduct to ensure their practices are not cancelling out the sustainable efforts, is crucial.

This also means making sure everything is done in the most ethical, responsible way, something which may be difficult to oversee with managing a large base.

Heather's role at UST saw her work with large global retailers and manufacturers, using data analytics, machine learning and AI to establish a means of measuring and monitoring their progress towards their sustainability goals.

“This has typically involved deriving processes that measure the carbon used by their internal processes and by their external suppliers,” she adds. “Tracking the intended reduction in this usage, alerting where this is not occurring and similar proactive approaches enable these enterprises to understand areas within their business and supply chain that are not progressing as required, showing them where they need to take further action.”

Olivier stressed that ESG practices are no longer a luxury and procurement teams must continually evaluate their sustainability strategies to fulfil their corporate responsibility. “Data analytics provides procurement teams with greater transparency and visibility over their environmental impact and regulatory compliance, allowing them to take further action based on the results,” he adds.

On enforcing the regulatory work, Heather says: “Government regulations for sustainable sourcing are taking effect, we are increasingly baselining carbon usage to that required by the regulator. Likewise with ethical sourcing – taking a data-driven approach to the ways in which business processes can be changed to meet ethical and regulatory standards.”

The importance of real-time data for agile procurement decisions

Real-time data is crucial for agile procurement decisions as it enables quick responses to market changes and supply chain disruptions. Jack called it crucial for agile procurement decisions, as “it enables quick responses to market changes and supply chain disruptions. Tools like supplier discovery use AI to analyse real-time data, provide rapid insights, identify suppliers based on current market requirements and enhance supply chain resilience, leading to better cost management and efficiency in procurement.”

Barath echoed this ability to stay agile in the evolving and sometimes volatile landscape, with the data offering insights into market fluctuations, supplier performance, inventory levels and demand variations. “It is indispensable across industries - in e-commerce, it tracks customer behaviour, inventory levels and sales trends to optimise stock levels, predict demand and create dynamic sourcing strategies,” he adds.

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