MACHINE LEARNING AND PROCUREMENT
Machine learning (ML) is widely talked about in today’s time and rightly so. In this ever-changing world of technological advancements, machine learning has gained a great deal of importance. The advent of ML has proven crucial because of the time and cost savings that it brings along with it. It has the potential to dramatically impact the future of any organization.
At times, the terms Artificial Intelligence (AI) and Machine Learning (ML) are used interchangeably. Here’s how they are different from one another. Machine learning is essentially a subset of AI. It allows the machines or computers to make data-driven decisions without any human intervention or explicitly functioning the machine to do so. Whereas AI is any task carried out by a machine or program which otherwise would have been performed by a human intelligently. Briefly explained, it is training machines to do things which humans can do but, better.
In the same way, the concept of Predictive Analysis is widely talked about in ML. While these two terms are not the same, they are sometimes used interchangeably. Predictive Analysis is a variety of statistical techniques which are used to estimate or predict future outcomes. ML is one of these techniques.
For example, ML is used regularly by top companies like Amazon, Google and Facebook. The shopping recommendations you get while shopping on Amazon is based on past shopping experiences and that is Amazon learning what the user wants through ML. Now Amazon did not program its computers to behave a certain way. Computers or machines learn automatically and adjust actions accordingly.
Importance of Machine Learning in Procurement
Procurement, on the other hand, has been famously known to increase efficiencies and time and cost savings. Digital procurement has automated the processes and has abolished the tedious methods of traditional procurement. Therefore, procurement is already familiar with such technological advancements and adopting machine learning could be a welcome change.
One of the most eminent aspects of procurement is managing supplier relationships. While we have spoken in previous blogs about how this is a crucial aspect, it is also necessary that we negotiate better with them and develop long term relationships.
ML can be used in the procurement environment to suggest a supplier, for item predictions, eliminating redundancy, allocation of an account code in line with expense type, contract renewal, task formation, and reminder, etc.
Improved Supplier Relationships and Increased Savings
ML plays an important part in maintaining supplier relationships as it predicts patterns within suppliers and the rates that they have been quoting in the past. This will help in negotiating better and comparing prices the suppliers have to offer. A lot of error is eliminated in this process and it helps in easier and faster decision-making.
Faster and effective negotiations lead to an increase in savings. Through efficiency in the selection process of suppliers, we can easily rate the suppliers based on their performance. This is just one instance highlighting the importance of ML in procurement.
Efficiency in Supply Chains
At times, managing of supply chains gets ignored in procurement. Efficient supply chains speak volumes about an organization and the results show in the bottom line profits. Introducing machine learning in the supply chain will help in monitoring the efficiency and competency. Every entity in the supply chain holds its own weight and ML can keep track of each of these entities.
How we view Machine Learning at Eyvo?
At Eyvo, we try to predict the patterns of our users by tracking their previous activity and suggesting them what steps to take next. We even send out notifications if there is an activity that is due to be performed.
One of the analytical modules we use at Eyvo is a combination of Product, Time and Quantity. We have tools analyzing the product user’s order, time of the day, week, month, year and what quantity they had ordered. Our algorithm uses this data and outputs the pattern that helps our application in predictive analysis.
A simple example would be: A user using our eProcurement application places an order for office supplies periodically i.e. 1st of every month. Our tool will analyze this pattern and suggest to the user to create the order following the previous pattern. This tool will also analyze the inventory of the items and create a sample order for the user. We call this related user pattern.
Eyvo has already started taking steps towards ML which we are keen on improving. We understand that this will shape the future of procurement in the coming years. As we mentioned earlier, procurement is not new to changes and adoption of ML would be welcome. Are you ready for these changes in your purchasing cycle?
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