Machine Learning in Manufacturing – Present and Future Use-Cases

Machine Learning in Manufacturing – Present and Future Use-Cases

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Machine Learning in Manufacturing - Present and Future Use-Cases

Major companies including GE, Siemens, Intel, Funac, Kuka, Bosch, NVIDIA and Microsoft are all making significant investments in machine learning-powered approaches to improve all aspects of manufacturing. The technology is being used to bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed.

So-called “smart manufacturing” (roughly, industrial IoT and AI) is projected to grow noticeably in the 3 to 5 years, according to TrendForce. The firm estimates that the global smart manufacturing market will be well over $200 billion this year and will increase to over $320 billion by 2020. That is a projected compound annual growth rate of 12.5 percent. Similarly, the International Federation of Robotics estimated by 2019 the number of operational industrial robots installed in factories will grow to 2.6 million from just 1.6 million in 2015.

This article will focus on how four of the leading companies in the world of manufacturing are using cutting edge AI to make interesting improvements to factories and robotics. It will focus on two main themes:

  1. The different ways machine learning is currently be used in manufacturing
  2. What results the technologies are generating for the highlighted companies (case studies, etc)

From what our research suggests, most of the major companies making the machine learning tools for manufacturing are also using the same tools in their own manufacturing. This makes them the developer, the test case and the first customers for many of these advances. This is a trend that we’ve seen in other industrial business intelligence developments as well.

This same in-house AI development strategy may not be possible for smaller manufacturers, but for giants like GE and Siemens it seems to be both possible and (in many cases) preferred to dealing with outside vendors. In either case, the examples below will prove to be useful representative examples of AI in manufacturing.


The German conglomerate Siemens has been using neural networks to monitor its steel plants and improve efficiencies for decades. The company claims that this practical experience has given it a leg up in developing AI for manufacturing and industrial applications. In addition, the company claims to have invested around $10 billion in US software companies (via acquisitions) over the past decade.

In March of 2016 Siemens launched Mindsphere (in beta), which is a main competitor to GE’s Predix product. Mindsphere – which Siemens describes as a smart cloud for industry – allows machine manufacturers to monitor machine fleets for service purposes throughout the world. At the end of 2016 it also integrated IBM’s Watson Analytics into the tools offered by their service.

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