The 21st century has been characterized by a series of new innovations which has drastically changed many business environments. Increased connectivity, far-evolved data storage capabilities and ever-increasing computing power has enabled the development of disruptive technologies, including Artificial Intelligence (“AI”), Machine Learning (“ML”), and Internet of Things (“IoT”). So, let us have a closer look how Machine Learning and Internet of Things are disrupting manufacturing.

The objective of Artificial Intelligence – and more specifically Machine Learning – is to find relevant patterns within available data with the use of sophistic mathematical models. When detected, these patterns can be used to perform future predictions, which may provide companies with valuable business insights. However, the quality – and thus the performance – of these models is highly dependent on both the quality and the quantity of the data that was used to train it.

Internet of Things is the concept of connecting all objects – which could be machines, mobile devices, wearables, and so on – with the Internet, allowing one to retrieve relevant data from it. Current estimates show that by 2020, over 26 billion IoT devices will be connected to the Internet. In this way, Machine Learning and Internet of Things perfectly complement each other in that IoT provides the fuel (i.e. the data) that is needed in order to train machine learning algorithms. Read more about IoT here.

An area in which the combination of machine learning an IoT has turned out to be very valuable is the manufacturing industry. By equipping machines and machinery components with IoT sensory devices, a large amount of data becomes available. This data enables machine learning models to derive valuable business insights regarding the manufacturing process and machinery status. This has resulting in the development of one of the most prominent use-cases of combining IoT sensory data and machine learning algorithms in a manufacturing environment: predictive maintenance.

In predictive maintenance, machine learning models an advanced statistic are used to predict the condition – and possibly the future failure – of machines or individual mechanical components. Thanks to data obtained from IoT devices, the performance and accuracy of such predictive models can be increased drastically, providing significant business value to manufacturing environments:

Reduced Outage Time

Classic maintenance programs – such as preventive maintenance – requires companies to inspect machines and components on a frequent basis, often resulting into unnecessary maintenance procedures. By implementing predictive maintenance programs, manufacturing environments only intervene when necessary, thereby reducing outage time and optimizing production output.

Preventive maintenance programs require personal to regularly check machine and/or component status, usually resulting in invasive actions such as disconnecting electric cables, removing and re-installing mechanical components or loosening and tightening bolts. Such invasive actions may result to incidental damaging of components or wrong reassembly procedures when carried out on a frequent basis.

Reduction in Secondary Damages

Most component or machine failures results in so-called secondary damages: damaging of surrounding components which were not part of the initial failure. By identifying failures ahead of time, manufacturing environments are able to diminish the financial losses incurred by secondary component damages.

Cost reduction

Machines and components are only inspected or repaired when indicated by the prediction algorithm, allowing to reduce the cost needed for hiring permanent maintenance staff.

Thus, combining Internet of Things data with machine learning algorithms allow manufacturing environments to implement a data-driven, machine-specific maintenance program, resulting in reduced outage time, reduced costs, and increased product output when compared to classic maintenance schedules. It is thought that increasingly more businesses – including manufacturing business as well as non-manufacturing businesses – will implement IoT devices to gain data of all aspects of their environment, and – more importantly – to train machine learning models to derive valuable insights which can be used for business decision making processes.