Mistaken beliefs blunt the effectiveness of machine learning

This is an excerpt of the original article. It was written for the March-April 2019 edition of Supply Chain Management Review. The full article is available to current subscribers.

March-April 2019

A few days ago, a colleague sent me “The Death of Supply Chain Management,” an article in the Harvard Business Review. If the title wasn’t enough to grab my attention, the last sentence in the first paragraph had me checking out job openings on LinkedIn: “Within five years to 10 years, the supply chain function may be obsolete, replaced by a smoothly running, selfregulating utility that ….. requires very little human attention.” Read more carefully, what the authors are really arguing is that as NextGen technologies find their place in our organizations, the role of the supply chain manager, including procurement managers, is going to…

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Machine Learning (ML) models power technologies that recommend movies we might like, assist in detecting health risks, suggest routes to dodge traffic and beat world-class chess players. Over the last decade, we have witnessed an explosion of emerging ML-enabled solutions across industries from health care to supply chains, enhanced by algorithms capable of making better predictions based on past data. Yet, as the range of industry problems in which ML systems can play a role continues to expand, it is essential to separate the hype from the reality, and understand misconceptions about ML as well as its limitations. Companies also need to be aware of the skills they require to harness the benefits of ML.

Common ML misconceptions

In his seminal paper “Computing Machinery and Intelligence” published in 1950, Alan Turing introduced a test to assess whether or not a machine is capable of learning, of representing knowledge and of performing other cognitive functions generally associated with the human mind. Building on philosophical and technical arguments, Turing’s paper laid the foundations of modern ML, and more generally, of Artificial Intelligence.

The ML boom in recent years may mislead us to believe that ML systems are new. As the Turing paper shows, the scientific community has been developing this field for more than 60 years.

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