In part 1 of our “Digital Trends and how they are affecting Procurement” blog series we tried to paint a picture of where technology is today and the way in which procurement is making use of this new technology; and we started off the conversation with Artificial Intelligence.
When talking about artificial intelligence you can’t help but bring into the conversation machine learning (ML) as well. There are many opinions on the relationship between these 2 digital trends. Some may say they are the same thing, while others will say they are 2 different things. We see machine learning as a branch of AI, that needs to be treated separately. ML has different applications in different fields and industries and we feel that when it comes to procurement these technologies are at different stages of development and can be used in very different ways.
If you are planning to incorporate one of these technologies into your business it’s important to know their meaning and the differences between them. Although they are similar, most times they are not suited for the same tasks. Making proper use of both technologies effectively can really take your business to the next level.
Before we dive into how procurement can benefit from ML, let’s try to define it.
What is machine learning?
“ML uses neural networks to analyse the characteristics of a situation and based on what it ‘sees’ makes a probability-based decision or action. But it needs to be taught, usually by processing volumes of data in combination with human intervention when it is unsure of a correct answer.”
One of the differences between ML and AI for procurement is that ML can learn and scale quicker than rules-based AI. Knowing the differences between these 2 technologies and being aware of their applications is key in deciding if suitable and understanding which one best fits your business needs.
Source to contract is one of the key components of every procurement organisation. It is a complex function that can bring significant value to companies, from spend consolidation, identifying contract leakage and non-compliance, supplier rationalisation etc. So, how can machine learning improve this process?
When it comes to source to contract we need to have a look at:
So far, machine learning has had limited impact on strategic sourcing. However, there are some developments in the area. As a rule, ML handles activities that need complex rules and pattern recognition. By using a basic level of human judgement ML can assign transactions to formal spend categories and subcategories. This process can help uncover sourcing opportunities, converting the whole process from a time-consuming, manual task into a real time, automatic activity.
Contract management has the most to profit from ML. Companies need to have visibility over the status of their purchasing contracts, supplier requirements, materials needed and consumption rates. These are all vital parts that ensure business continuity and improve supplier relationship.
ML can help speed up these processes based on the fact that these technologies can literally “learn” to do all the thinking and work for you. Let me break it down a little further for you.
When it comes to contract management ML can assist with:
There are still many procurement areas that machine learning will change in the future, both in terms of accuracy and efficiency but also speed. It’s fascinating to see, year by year, how many activities intelligent technology can change and ultimately improve. Stay tuned for the 3rd part of this blog series as we explore robotic process automation and how it’s changing the procure to pay area.