Does that mean supply chain professionals need to find other areas of employment? Probably not, but those who are intent on developing in their professions need to consider machine learning as another (and a very powerful) tool they can leverage to advance both their company’s business goals and their own career goals.
Here are three things that experienced supply chain professionals are responsible for and that machine learning can be engaged to help with.
Find things you don’t know to look for
Discovering answers to questions you didn’t ask has been an important part of analytics processing for a while and AI is bringing advanced abilities to bear. For supply chain application AI (or machine learning if you prefer) is able to direct itself to areas you haven’t thought of analyzing and discover key factors that affect performance. The advantage here is that the machine is working all the time, even while you’re taking care of more human kinds of things. At some point in its evolution machine learning can take its findings and develop solutions, or at least experiments that can test its proposed solutions. It can then iterate through multiple versions until the best results are obtained. The changes can then be either presented to humans for approval or simply put in place depending on how much autonomy the machine is allowed.
Integrate multiple technologies
New tech added to the supply chain isn’t always perfectly connected to existing systems. With the expansion of IoT, logistics systems, and manufacturing 4.0 there are plenty of missed connections. And even when systems are connected the interactions haven’t been fully explored to take advantage of their implications. Machine learning can monitor disparate systems and discover relationships that could benefit operations, then recommend actions that leverage the individual capabilities of multiple systems. The resulting advances could be ones that would take human intervention years to discover, if ever.
Humans are great at looking at last year’s sales and predicting volumes for coming seasons. But our ability to monitor, or even to know what factors to evaluate is limited. As humans we tend to repeat what we did before but AI doesn’t have the same kinds of time and attention limitations we have. Machine learning can reach across multiple channels and evaluate historical data to compare results and make predictions.
In general machine learning can step in to assist and improve on many supply chain issues that professionals rely on their experience to deliver. Learn to push machine learning ahead of your own capabilities and take advantage of what it can do to advance your own efforts.Last modified on Tuesday, 12 June 2018