The Role of the Digital Twin in the IOT 

For the most part, manufacturing lines currently depend on human supervision and decision-making to optimise operations and ensure continuity. However, an evolution is underway which will use new technology to autonomously monitor, measure, interpret and manage the production line – welcome to the smart factory!

Industry 4.0 or the Industrial Internet of Things (IIoT), can be described as industrial processes and manufacturing connecting directly with the latest IoT technology. This convergence, which includes big data, machine learning, artificial intelligence, predictive analytics etc., can help manufacturers optimise performance and more accurately predict business obstacles. The next big step in this evolution is the introduction of the concept of the digital twin.

Advanced new sensors are constantly finding their way into modern manufacturing lines, facilitating informed decision-making, but this is just the beginning. Typically, we think of the smart factory, but it does not stop there. This ongoing digital transformation is driving innovation across a wide range of areas, including aerospace, agriculture, energy, healthcare and transportation. It’s unlikely that any sector is untouched by these new technologies.  

Industrial manufacturing will face massive disruption as development moves towards fully connected, self-organising intelligent factories. For example, this will trigger significant potential for efficiency improvements, such as reducing energy consumption and preventing downtime. Furthermore, industries can utilise smart components to improve asset and supply chain management, enhance quality, and shorten time to market. 

In theory, sensors and physical components can be combined to form a smart factory. It sounds quite simple, but in reality many of these “sensor devices” may employ different technologies and protocols. In other words, the components each speak a different language, which can cause significant interoperability issues. The question is then, how can we facilitate smooth and dynamic interoperability among these differing components? 

The solution could be ‘digital twin’, or ‘administration shell’ of each physical component, in other words, a virtual representation of a real product or system. But what is the use of it? Well, it can be used to perform simulations of the “real things”, such as product design, simulation, monitoring, optimisation, servicing and much more. 

By combining the strengths of the physical and virtual worlds, so-called cyber-physical systems have the potential to significantly enhance industrial performance. Basically, the real systems can be modelled using the digital twin in multiple ways. For example, the digital twin can be used to monitor and model simultaneously in real-time, predicting changes in a system’s dynamics based on real-time sensor data. Alternatively, it can model future scenarios, such as a system failure or even just general optimisation. 

The digital twin contains the complete set of parameters of its physical sibling, as well as adaptive algorithms for decentralised self-optimisation and safety measures. Acting as an intermediary, the digital twin functions as a standardised interface between the smart components, delivering ideal interoperability. This means that the digital twin could emulate the combination of all these different components to determine how it would operate and any issues observed could be mitigated before final completion of the physical system. 

A typical manufacturing environment, is often made up of a number of systems that are interconnected, often running the risk of a breakdown, which could cause a ripple effect throughout the entire production line. For example, if a robot, which performs an important serial function upon which the rest of the production relies, breaks down it can have serious consequences as production could be slowed, interrupted or even drastically halted. This could lead to a loss of revenue, and in some cases discontinuity of the business. 

Manufacturers are continually looking at ways of optimising efficiency to minimise cost and boost profitability. In today’s Industry 4.0 domain, digital twins operating in parallel to the “real” factory, where thousands of sensors constantly collect, process locally (edge computing) or send back data for processing on a larger scale are becoming more commonplace.

Specific benefits of the digital twin approach 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. For example, a programmed robot which is operated in a specific sequence could be constantly modelled in parallel in order to reduce cycle time of that sequence.
• 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, challenges to reaping the benefits of deploying a digital twin also exist. For example, many technologies are vying for a place in the IIOT and, as mentioned earlier, multiple technologies introduce the problem of interoperability. This means that any digital twin may have to cope with processing data from many different protocols. 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” with the digital twin. 

However, a far greater threat now lurks. As the convergence of enterprise IT and operational technology sees systems and devices exchanging and interpreting shared data, cybersecurity becomes a threat to the smart factory. Digitisation and the increasing connectivity offered by the Internet of Things (IoT) bring enormous opportunities, but also unforeseeable risks and serious vulnerabilities that can be exploited by new forms of cybercrime.

As Industry 4.0 uses more complex technology and connectivity, these systems present a greater number of attack surfaces and have the potential to cause a much more severe impact than a mechanical breakdown. A connected manufacturing plant now becomes a target for nefarious attacks and, if not adequately protected, could have disastrous consequences. This means that robust information and digital security is a major priority factor which must be considered. Unfortunately, in many companies this is not always on the list of priorities.

It really 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.

Many manufacturers, whilst having internal security knowledge, will nevertheless benefit from working with external advisors who have wider exposure to assessing various types of product or infrastructure, and will be better equipped to help manage threats. Building a network of trusted partners is therefore a strong first step towards planning cost-effective end-to-end security. Tackling the problems of cybersecurity risks can, after all, 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 remain many challenges which must still be resolved.