This guest post, exploring how to leverage procurement data, was written and contributed by technology expert Jenn Fulmer of TechnologyAdvice.
Over the last few years, supply chains experienced major disruption due to global challenges and fundamental market shifts. While no one could have predicted the specific circumstances and outcomes, we find ourselves now asking if we could have been more prepared. And, for many procurement teams, the answer is yes. As we learn, grow, adapt and evolve, it is clear procurement’s future success and security depends on making more proactive, informed decisions moving forward. And, procurement data is the key.
Consequently, organizations that were early adopters of a data-focused approach were more prepared and have doubled down to integrate systems and enhance their forecasting abilities. Now, more businesses want to use procurement data to forecast market trends.
But what exactly is procurement data, how can you capture it and subsequently leverage it for your organization? In this blog, we’ll answer key questions about procurement data. To start, we’ll offer key definitions and important background information about procurement data and analytics. Then we’ll explore the four types of procurement analysis. Finally, we’ll share ways to put your procurement data to work predicting market trends.
What is procurement data?
Procurement data is a collection of financial and transactional information that comes from both inside and outside of the organization. For example, procurement data can be gathered from procurement technology, vendor contracts, purchase orders, RFPs, enterprise resource planning (ERP) systems and vendor risk assessments.
Organizations use this data to make better decisions surrounding vendor selection, risk and compliance and resource management. Additionally, organizations can use procurement data to forecast demand, informing both their production and marketing efforts.
Analytics in procurement
By combining artificial intelligence (AI) with procurement data, businesses can get deeper insights and make more accurate predictions. Because any number of things can disrupt a supply chain (e.g. natural disasters, sudden spikes in demand, legislation, etc.), businesses have to be agile in their procurement processes. Analytics in procurement gives organizations more visibility into their resources and key performance indicators (KPIs), helping them make better business decisions.
For example, a tire manufacturer might have several vendors available for rubber. While they typically work with Vendor A, that vendor is currently running low on stock and can’t fill the manufacturer’s order. To meet demand, the manufacturer needs to pivot quickly to a new vendor — but which one? Procurement analytics would allow the manufacturer to see other vendors that have inventory, along with their typical delivery times and prices to help them make the best decision.
4 types of procurement analysis
Spend analytics focuses on the goal of decreasing procurement costs by providing visibility into what an organization currently spends. Additionally, the organization can assign KPIs to its suppliers and measure them using supplier performance evaluations to determine whether they’re meeting its needs. Not only can spend analytics help a business reduce both direct and indirect spend right away, but it can also help it make better decisions moving forward to lower expenses in the long run.
Contract analytics provides insights into a business’s agreements with suppliers. It shows whether the business is taking advantage of the correct volume discounts, how and when contracts renew and the contract terms that are most advantageous. This can help the organization formulate better contracts in the future and negotiate with suppliers to cut costs and improve supplier relationship management.
Supplier analytics tells a business which suppliers carry the goods they need, how often they change vendors and the last time a vendor submitted bids for services. Gaining visibility into these metrics can help organizations improve their relationships with existing suppliers, find new suppliers to meet their needs and improve their overall procurement performance, becoming more flexible while lowering costs.
Procurement benchmarking helps an organization compare its procurement performance against that of competitors or leading businesses in the market. It allows the business to quickly react to changes in the industry and identify gaps to provide a competitive edge.
Using data-driven procurement to forecast market trends
When businesses have procurement data, they get insights into the materials, products and services they’re buying the most. The data enables them to understand and predict trends across their business and the industry at large. They can then use this information to make more accurate predictions about future demand while navigating potential challenges. Subsequently, they can use the data to adapt accordingly.
For example, early in the pandemic when hand sanitizer was difficult to come by, many alcohol distilleries diverted some of their resources into making it because they already had many of the raw materials on hand. Not only was this a sound business strategy in terms of utilizing existing product, but they were also able to use the move in marketing campaigns to improve their relationship with the public.
Demand forecasting is the process of making data-driven predictions about how customer purchasing trends will change in the future. It helps keep procurement teams and manufacturers from overstocking materials, producing too much of a product or even over or under-staffing their production team.
Methods of demand forecasting
Historically, businesses handled demand forecasting manually, with analysts examining historical data and any other limited data available to make predictions. Unfortunately, this was not as accurate as it could have been and soon, AI was added to the equation. With AI incorporated into RFP automation, businesses can analyze more data faster and get deeper insights into trends to help them make more accurate predictions about future business needs and sales. This information empowers procurement managers to act more proactively and strategically.
Additionally, organizations may also opt to forecast demand by surveying current customers to see what their purchasing plans are for the future. This takes time and effort, but because the data is coming straight from customers, it’s typically valuable and worth the effort.
Calculating proactive procurement using software
Organizations that use procurement analysis software can improve demand forecasting accuracy by around 55 percent on average. Procurement analysis software typically includes AI to make more accurate forecasts and help organizations proactively procure materials, rather than reacting to market changes after the fact.
Businesses also need software that provides full visibility into their procurement process, helping them pivot quickly when vendors aren’t meeting their needs. It should integrate easily with the organization’s ERP system and any other procurement-related software they use either through native integrations or APIs. Then, the business can pull all of their procurement data into a single dashboard, giving them better insights and helping them improve processes.
Ways to improve predictive procurement accuracy
Adding AI to the procurement process is one of the best ways to improve the accuracy of predictions. Because AI can comb through much more data than human analysts can in a shorter amount of time, organizations will get deeper insights into the data they collect and can make more informed decisions.
Additionally, adding more data streams, especially from suppliers, can improve accuracy because there will be more data to base predictions on. First-party data is great, but third-party information can provide information on customers that aren’t buying from the company.
Predictive procurement is the key to a more adaptable and dynamic supply chain. Start gathering procurement data now and incorporating it into your demand forecasting to optimize your processes and make better decisions.