The amount of data in the world is growing every second. What’s more is that this creation of data shows no sign of slowing down. Machine learning provides a solution to derive meaning from all this data. It’s one thing to create data, but it’s another to understand and leverage it. Machine learning is comprised of tools and technology that allow you to learn more about your data. With this technology, you can ask questions about your data and uncover helpful answers.
The potential of machine learning is vast. This means we are only just starting to understand the value it can have for people and companies. Data is created by people and generated by devices such as computers and phones. Historically, we used to gain knowledge about data by analysing systems and adapting our systems, as necessary, to understand data patterns.
However, the volume and generation of data daily is so expansive that humans are struggling to keep up. We can no longer write the adaptation rules and efficiently understand what the data is telling us. This is where automated systems shine – these new systems can learn from the data and seamlessly adapt to the changes in data patterns. So, how can machine learning be used in your supply chain? Let’s take a closer look.
Machine learning in supply chain
Supply chains can use machine learning to analyse historic data. They can use this to improve their pricing models. Suppliers can assess what the best price is for a shipment or a collective group of shipments. Businesses in the supply chain will often bid on a set shipping price for the year in order to set ordering cost in a contract and helps them determine a fixed price that they pay to send their goods onward.
Suppliers can use machine learning to understand information from the past, compile it with present data and extrapolate that to into the future. This algorithmic calculation allows suppliers to have a better gauge of what their ordering cost should be when pricing with freight companies or carriers.
Machine learning can uncover options for carriers much more efficiently than people can. It’s not that your staff wouldn’t have been able to uncover this information, but it would have taken them much longer. So not only can it help optimise ordering cost, but machine learning can provide this service in a much more efficient fashion.
The supply chain can also experience disruptions in their service. For example, bad weather can cause a significant amount of disruption in the shipment and delivery of goods. With machine learning, a supplier can look at historic data and understand how these situations were managed in the past. It will analyse the corrective actions that the supplier took and see what strategies were effective. Machine learning will make suggestions on how you should adapt. It also has the potential to automate this process and make decisions for you.
Overall, machine learning is capable of providing personalised and insightful information to achieve daily tasks in the supply chain in a much more effective and efficient manner.