How digital technology is transforming manufacturing

Digital technology has long been used to assist manufacturing, and manufacturing has often been at the forefront of technological change. In the 1980s and 1990s, almost every aspect of the manufacturing process came to be ‘Computer Aided’, resulting in a blizzard of acronyms including CAM, CAD, CAPP and CAE. However, it can be relatively hard to see precisely how the rise of technologies such as machine learning, computer vision and artificial intelligence will affect the sector, given the sheer range of companies to consider and the diversity of their operations. The terms ‘Industry 4.0’ or ‘smart manufacturing’ are used as a kind of catch-all for the applications of many of these technologies in manufacturing operations, but the exact meaning or implications are often left vague.

In this blog, our Director of Data Science Finn Wheatley highlights a few of the key data-driven technologies that he believes will affect manufacturing businesses in the coming few decades, and sketch out how and why they could be used.

Digital twins

Digital Twins have the potential to change manufacturing profoundly. While Digital Twinning has been used extensively to build models of individual components (and sometimes processes), in order to perform tasks such as predictive maintenance, the potential of the idea is much greater when applied at a larger scale. The ability to construct digital models of whole systems or organisations will offer transformative opportunities in risk management, logistics, and process optimisation.

Generative design

Generative design has been around for some time. One of the more famous examples is the aerial of the NASA ST5 spacecraft, designed by a genetic algorithm in 2006. What has changed more recently is the use of very powerful deep learning approaches, such as generative adversarial techniques and reinforcement based methods, to construct very complex designs. These tools are becoming increasingly seen in commercially available CAD software, and could change design profoundly, since they use techniques that explore a far wider range of permutations than are possible with traditional methods.

Augmented reality (AR) and virtual reality (VR)

It has been clear for several years that AR and VR techniques will become increasingly widely used in manufacturing. Our client EDF Energy has introduced VR to deliver health and safety training for employees who operate and maintain their nuclear power plants.[1] EDF also uses an ultra-high resolution VR simulation to train the maintenance engineers who operate the plants, reducing down time.[2] In future, VR could be used to allow fully immersive virtual design of manufacturing plants prior to breaking ground, minimising unanticipated obstacles., and product demonstrations to users on the other side of the world, even before any products are produced or shipped.

Additive Manufacturing

3D (and 4D) printing allow manufacturers to deliver individually customised finishes to their product offerings, as well as enabling the construction of shapes that would be difficult or impossible to produce by traditional techniques. Plastic is by far the most commonly used substance. It is easy to see how this would be a boon to the textile industry – for businesses such as clothing, footwear and home furnishings – as well as automotive and toys. Research is under way to expand the range of materials commonly available, which is likely to increase in the future.

Robotics

Many highly automated manufacturing processes have been using robotics extensively for many decades. However, many processes that require skills such as manual dexterity are typically still human-centred. Advances in sensors and deep learning (largely reinforcement learning) have now advanced robotics to the point that humans are less involved. UC Berkeley demonstrated a robot that could learn to accomplish the most boring of human tasks, folding laundry, as long ago as 2011. More impressively, Boston Dynamics has recently unveiled robots with fine-tuned, dynamic motor control and reflex actions, such as balancing on a ball.

Machine Vision

Image processing technologies play a central role in many industrial processes – in many cases, along with IoT devices, image processing is the foundation for robotics. Advanced imaging devices backed by machine learning are central to quality control (QC) in many precision engineering processes. This is likely to increase, and the use of machine vision (MV) will be tightly linked to the increased use of artificial intelligence (AI) in robotics.

Others

Manufacturers may also use customer data or social media analysis to optimise their marketing efforts or improve customer service. Logistics and supply chain data can be another source of competitive advantage for manufacturers. By merging their internal data with external open datasets, it is possible to conduct analytics to assess supply chain disruption risk, predict disruptions before they happen and conduct scenario analysis to assess long and short term costs and potential unforeseen knock-on impacts on operations. Additionally, quasi-data science methods such as robot process automation (RPA, not to be confused with robotics) can be used to automate time-consuming, repetitive processes with minor variation, such as filling in forms, creating presentations or inputting and extracting data.

Take a look at our digital transformation services page to learn more.


About the author

Finn Wheatley, Director of Data Science 

Finn has over a decade of experience working in lead data science and quantitative roles in both the public and private sectors. Following his undergraduate degree from King’s College London, Finn worked for several years in the hedge fund industry in risk management and portfolio management roles. Subsequent to an MSc in Computer Science from University College London, he joined the civil service and helped to establish the data science team at the Department for Work and Pensions (DWP), delivering innovative analytical projects for senior departmental leaders. Since joining Whitehat Analytics, he has been involved in establishing the data science team at EDF Energy.


[1] Lloyd Dean, Head of Digital and Innovation Learning, edfenergy.com, 18.06.18 (Virtual Reality Learning | Innovation Blog | EDF (edfenergy.com). Accessed 26.04.21.

[2] VVProPrepa – The Reactor Building in one click. edf.fr Virtual reality and optimised nuclear power plant maintenance (edf.fr)