The Arms Race To Leverage Machine Learning In Supply Chain Planning Posted on : Oct 16 - 2017

Artificial intelligence (AI) is hot.  Crunchbase reported that over the last year over $4 billion in venture capital has been invested in AI firms just in the US.

In the supply chain realm, the branch of AI known as machine learning is where most of the activity is, particularly for software suppliers offering supply chain planning (SCP) applications. Arnaud Hedoux, the Director of Marketing for Demand & Supply Chain Planning Solutions at Dynasys, a QAD company, made the point that “The adoption of machine learning is the key driver in the ‘arms-race’ between software vendors to achieve differentiation.”

Supply Chain Planning has long been an application that has relied on Ph.D.s in operations research (OR) or statistics to build the math that powers these applications.  Thus, few planning supplies are buying third party machine learning technologies to embed in their solutions. The planning vendors have built, and are building, machine learning solutions into their SCP applications.

But what is machine learning?  Some of the SCP companies I talk to have been too modest in taking credit for their activities in this area. If they can’t label their solution as being based upon a computational technique generally associated with machine learning, they are apt to say their solution has “characteristics” of machine learning, but is not truly a machine learning application.

I think they are being too modest. 

Adeel Najmi - the Senior Vice President for Products at One Network Enterprises, who holds a doctorate in Industrial Engineering and Operations Research from Berkeley - agrees.  “Learning occurs when a machine takes the output, observes the accuracy of the output, and updates its own model so that better outputs will occur.  Any machine that does this is using machine learning. It doesn’t matter if data science methods are used or not.  It does not matter if neural networks or some other superviced or unsupervised learning technique is being used.  It’s important not to get bogged down on the specific technique. What matter is if the machine is itself capable of learning and improving with experience.” View More