Applying Machine Learning Tech: A race for Supply Chain Excellence

In the last year, $4 Billion in VC cash has been crammed into the pockets of organizations working within AI (artificial intelligence) development.

Supply Chain Management (SCM) organizations and service providers are - now - too, looking to cut off a piece of the pie. SCM professionals see the domain of supply chain planning (SCP) as a management area in which the capabilities of AI would be applicable. In particular, firms have been looking at developing machine learning (ML) capabilities.

ML, applied within SCP could help with forecasting within inventory, demand and supply. If applied correctly through SCM work tools, ML could revolutionize the agility and optimization of supply chain decision-making.

Arnaud Hedoux, Director of Marketing for Demand & Supply Chain Planning Solutions at Dynasys has remarked, “The adoption of machine learning is the key driver in the ‘arms-race’ between software vendors to achieve differentiation.”

This comment illustrates the potential value that ML, implemented though SCM software, could bring to SCM activities. In the meantime, the race is on among various B2B software vendors to develop an AI powered solution that's worthy of adoption on a wide-scale.

Technology implementation has become the norm for many supply chain activities. Companies globally are looking to find their competitive edge by being more innovative withiin supply chain technology. The implementation of artificial intelligence was once a sci-fi pipedream, but the materialization is much closer than one would think.

So what is the true action machine learning technology could bring to SCP activities?

Combining intelligent algorithms with complex big data sets, software/tools bearing ML technology could help put best possible scenarios in front of a user.

“A forecasting engine with machine learning, just keeps looking to see which combinations of algorithms and data streams have the most predictive power for the different forecasting hierarchies” (forbes.com 2017). It's an endless loop of forecasting, constantly self-improving.

Machine learning is the kind of innovation every supply chain should be taking note of.

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