The Introduction of Machine Learning in Procurement

Machine Learning in Procurement

Last Updated on March 21, 2024 by Team Experts

It is not surprising that new advancements in technology are being slowly introduced to every industry. Those who have already implemented these technologies or software in their businesses are already gaining benefits. 

In the procurement industry, many online tools and software are being integrated to make the processes faster and more effective. Thus, it is not surprising that more and more advancements are going to be implemented such as AI and machine learning. 

Nonetheless, the shifting from the traditional procurement process to a digital process is still in its early stages and machine learning is currently being integrated into procurement systems. 

Defining Machine Learning in Procurement

Many processes in procurement have been automated already. From payroll to material tracking, many things are now integrated to make things faster to boost profit and satisfy customers. 

However, many companies are having difficulty in handling this as they all try to get a better handle on their partners and suppliers to gain a competitive advantage over the others. 

Also, procurement professionals are being tested to their limits as they are constantly bombarded with requests which require a lot of document analysis. 

This is where machine learning comes into the picture. It is a form of AI that has a lot of applications within procurement. When it comes to procurement, it is the application of self-learning automated technology that is used to solve problems in procurement and improve operational efficiency. 

We can call machine learning the successor of robotic process automation. However, machine learning has the capability to learn and improve over time. 

The Types of Machine Learning 

1. Supervised learning

Supervised learning is a type of machine learning that is taught with patterns using the data acquired in the past. The patterns taught are detected automatically in new data. 

It is called supervised learning as you train the algorithm to find the patterns in the data with supervision through providing the correct answers. 

2. Unsupervised learning

Unsupervised learning is an algorithm that is programmed to find new patterns in the new data. Instead of providing it with supervision through correct answers, it looks for patterns within the data. 

3. Deep learning

Deep learning is an algorithm that is the advanced class of machine learning. This algorithm is inspired by the human brain wherein neural networks work to enhance its ability to perform tasks. 

4. Reinforcement learning

This machine learning is where it decides to act on its own in certain situations and the behavior is either punished or rewarded depending on the outcome. 

The Role of Machine in Procurement Today

1. Spend classifications

Spend classification is defined as the process of grouping related spend data into specific categories. This process is important as it prepares the data for spending analysis. 

By using machine learning, it is possible to gather more data from multiple sources and classify those data rapidly and accurately. 

2. Creating purchase recommendations

Machine learning can help the company to make quick purchasing decisions by establishing systems that act like a search engine that filters all your potential purchases. 

Additionally, the search can be refined more to show the list of suppliers who meet your requirements for the products you want to source. 

3. Predictive capabilities

It is a big problem for supply chain professionals when there are delays in shipping as it could mean high opportunity costs. Thus, prices can be affected by the shipping problem. 

Machine learning can help to ease the pressure on supply chain experts by spotting delays before people can see them. By using real-time data, the algorithm can predict an interruption or delay that may arise and make decisions that can mitigate the emergency. 

4. Enhanced supplier’s risk management

Companies have major concerns when their suppliers have unhealthy financial health due to the fact that they may not be able to do their obligation to the company anymore. 

However, procurement software suppliers are creating powerful cloud-based platforms that are powered by machine learning to help solve the problem with suppliers. 

When you know that your supplier has good financial health, you can be assured that it has the ability to fulfill the contract you have negotiated with it. 

5. Aids in reviewing purchases

Many e-procurement tools or software have already automated the process of creating purchase orders once a purchase request is approved. 

However, the purchasing process can be further improved by using machine learning techniques in the organization. 

The machine learning algorithm can calculate the confidence level of the requested materials or products based on historical data and budgetary requirements. By using the basis, it is capable of providing the approval authority to approve or disapprove the request. 

Once the request is approved, a purchase order is automatically generated and sent out to the supplier. 

6. Assess the company’s performance

There is so much data available outside of the company which it cannot handle manually. It is important for companies to compare their performance with other businesses to identify where it lacks and opportunities in the market. 

Many e-procurement tools are integrated with machine learning to classify related data from different sources. Thus, it led companies to use key performance indicators (KPIs) to assess their procurement performance. 

Read more: Why You Should Never Trust Your Machine Learning Model

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Anil is an enthusiastic, self-motivated, reliable person who is a Technology evangelist. He's always been fascinated at work especially at innovation that causes benefit to the students, working professionals or the companies. Being unique and thinking Innovative is what he loves the most, supporting his thoughts he will be ahead for any change valuing social responsibility with a reprising innovation. His interest in various fields and the urge to explore, led him to find places to put himself to work and design things than just learning. Follow him on LinkedIn

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