Seeing double

The digital twin uses technologies such as artificial intelligence (AI), Internet of Things (IoT), virtual reality (VR/AR), and sensor technologies to create digital models – twins – of real devices, systems, or processes. This can include manufacturing or production lines where models can be used to monitor the real system to improve production processes and optimise performance, for example to predict machinery breakdowns. 

A typical manufacturing environment is made up of several interconnected systems  so any breakdown can cause a ripple effect throughout the entire production line. If a robot that performs an important serial function upon which the rest of the production relies, should break down it can have serious consequences – production could be slowed, interrupted or even halted, leading to a loss of revenue. Digital twins can help manufacturers to optimise performance and more accurately predict business obstacles.

Digital twins, operating in parallel to the physical factory, where thousands of sensors constantly collect, process locally (edge computing) or send back data for processing on a larger scale, are rapidly becoming commonplace. 

Specific benefits of the digital twin approach can include:

• Constant monitoring to determine if a machine is about to fail, so any potential issue can be mitigated without interrupting function, but it can be modelled on the digital twin in real-time to assess the size of any problem.

• Data monitoring and analysis to make iterative improvements to operations, increase efficiency and reduce costs in real-time. 

• Ability to plan – probably one of the greatest uses of the digital twin, as an entire factory can be simulated before the first brick is laid. 

Of course, there are challenges to deploying a digital twin. These can include issues caused by multiple technologies of varying ages vying for a place in the IIoT, which introduces the problem of interoperability. This means that a digital twin may have to cope with processing data from many different protocols and legacy systems. As this requires decoding and recoding activity, extra latency and a greater margin for error is introduced. There is also the cost associated with replacing existing equipment to interoperate and dock effectively with the digital twin. 

As the convergence of enterprise IT and operational technology sees systems and devices exchanging and interpreting shared data, cybersecurity also becomes a threat to the smart factory. 

Digitisation and the increasing connectivity offered by the IoT brings opportunities, but also unforeseeable risks and serious vulnerabilities that can be exploited by new forms of cybercrime.

As more complex technology and connectivity is utilised in plants it presents a greater number of attack surfaces and has the potential to cause a much more severe impact than a mechanical breakdown. This means that robust information and digital security is a major priority factor which must be considered. 

It is essential to deal with cybersecurity measures at the early planning stage of any system, be it a product or a production plant. While there are defined cybersecurity standards available globally, some may not be complete and ratified, or mandatory. However, they do represent a first line of defence, and as a first step any ‘connected’ stakeholder must:

• Think ‘secure by design’ and take a proactive approach to cybersecurity by recognising that attacks are ‘when’ and not ‘if’.

• Ensure up to date compliance with all standards.

• Constantly review the “cyber resistance” status of all systems.

Tackling the problems of cybersecurity risks can only be realised by comprehensive planning, periodic evaluation, updates and monitoring – from design through to obsolescence. While it is clear that the digital twin is changing industry, there are many challenges still to be resolved.