My colleague at the ARC Advisory Group, David White, wrote an interesting strategic report in which he explained how machine learning is helping support more efficient supply chains. Machine learning is being discussed more often in supply chain contexts because of the buzz created by the Internet of Things (IoT). But I was not quite sure what machine learning was.
The strategic report goes into quite a bit of detail on use cases for machine learning and discussion of the technology. I’m only going to summarize a few key points from the report here.
First of all, in one of the more interesting use cases, “machine learning techniques were applied against multiple data streams collected from an oil rig during operation. Deviations in the pressure readings for a water injection pump indicated that a seal in the pump would fail within days. This early warning enabled the seal to be replaced before it failed, avoiding the associated downtime. The owner-operator estimated that this data-driven predictive approach avoided an estimated $7.5m in losses from unplanned downtime.”
Machine Learning Can Help Prevent Unplanned Downtime
So what is machine learning?
“Three things make machine learning different than predictive analytics. Machine learning applications are self-modifying, highly automated, and embedded. That is, machine learning algorithms are designed to continuously adapt and improve their performance with minimal human intervention. Machine learning algorithms are also embedded within a process or workflow. That is, they become seamlessly integrated into the process to the point where they are invisible to the user or operator. Machine learning algorithms are in their element solving problems that are too difficult or complicated for human programmers to code.”
Examples of this include the suggestions we get about what we might be interested in purchasing when we go on Amazon, or the suggestions that Netflix provides to customer on what programs they would be interested in watching. In both cases, these recommendations are driven largely by machine learning algorithms. On Netflix, for example, as each viewer watches shows they like, the algorithm learns more about what that particular viewer likes to see.
“So reviewing our key criteria for machine learning, the Netflix recommendation algorithm is:
- Self-modifying. Movie and TV recommendations change over time as the algorithm learns more about the viewer’s preferences.
- Highly automated. The learning happens with minimal active intervention by the viewer.
- Embedded. The algorithm is a seamless, unavoidable part of the over-all Netflix experience.”
Machine learning is not a magic wand technology, however. “Successful machine learning depends on data, lots of it, and the more the better.”
On the other hand, we are entering an era in which vast amounts of data will be generated by the Internet of Things. That, in turn, will usher in a raft of new machine learning applications, including new supply chain applications.