How can machine learning work from evident inefficiencies to introduce new efficiencies for business?

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One of the biggest potential applications of machine learning systems is the mining of important efficiencies for business processes and operations. This field is still booming as machine learning evolves, and vendors offer companies more powerful tools to evaluate business scenarios.

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In general, machine learning can provide efficiencies through examining a greater range of possibilities and choices, some of which may seem inefficient on their face. An excellent example is a process called simulated annealing that involves algorithms that produce results in some of the same ways that engineers cool metal after forging. In a sense, the system takes in the data and examines these inefficient paths or outcomes to find whether, if combined, altered or manipulated in any way, they can actually produce a more efficient result. Simulated annealing is just one of many ways that data scientists can create complex models that can root out deeper efficient options.

One way to think about this type of machine learning capability is by looking at how GPS navigation systems have evolved in recent years. The early generations of GPS navigation systems could provide users with a number of most efficient paths based on very basic data – or rather, data that now to us seems very basic. Users could find the fastest route using highways, fastest route without tolls, etc. However, as motorists learned, the GPS was not optimally efficient, because it did not understand issues like roadwork, accidents, etc. With brand-new GPS systems, these outcomes are built into the machine, and the GPS provides much more efficient answers, again, because the algorithm is considering paths that may seem inefficient to a more basic system. By learning, the machine uncovers efficiencies. It presents these to the user, and as a result, delivers a much more optimized service. That's the type of thing that machine learning would do for enterprise – it will free up efficiency by uncovering hidden paths that are optimal and efficient, even though they require some analytical complexity. These systems, which are so geared toward providing optimal outcomes, are not just used for digital business intelligence mining; for example, a report from GE shows how using machine learning systems can dramatically improve the operation of coal plants providing power to communities.

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Justin Stoltzfus
Justin Stoltzfus

Justin Stoltzfus is an independent blogger and business consultant assisting a range of businesses in developing media solutions for new campaigns and ongoing operations. He is a graduate of James Madison University.Stoltzfus spent several years as a staffer at the Intelligencer Journal in Lancaster, Penn., before the merger of the city’s two daily newspapers in 2007. He also reported for the twin weekly newspapers in the area, the Ephrata Review and the Lititz Record.More recently, he has cultivated connections with various companies as an independent consultant, writer and trainer, collecting bylines in print and Web publications, and establishing a reputation…