We explain how firms can make the most of AI systems capability to drive value at a time of significant cost uncertainty.
Leo Whyte is the Head of Digital Platform at Vendigital. He recently shared his insights with The Engineer.
The need for manufacturers to find ways to decrease energy consumption and decarbonise processes is a boardroom priority. Digital twins are data-driven virtual replicas of a physical asset, such as a product or factory, and are widely used to simulate scenarios to inform decision-making.
With calls for decarbonisation growing across industries and 46 per cent of manufacturing companies already implementing decarbonisation plans, this technology is a major lever that boards can use to gain greater insight into their business.
Scenario modelling can rapidly find efficient solutions with a higher chance of success compared to traditional real-world trial and error, whilst enabling the ability to de risk operations. This escape from trial and error is what makes digital twin technology stand out as a decarbonising superpower, with the ability to simplify processes in a cost-effective and high impact way, saving cost and time. However, while some industries have taken to digital twinning quickly, others have been slow to adopt this ground-breaking technology.
One of the main barriers when it comes to the adoption of new technology is under-investment, as businesses may lack the capital reserves needed to invest upfront in the development of a digital twin. Manufacturers may also not perceive this technology as ‘business critical’ as they may be using lots of processes that have worked well for many years, so why change now? A third reason is that even if there is a willingness to invest in digital transformation at Board level, there may not be enough people with the right technical skills to take a digital twinning strategy forward.
Despite these issues, one third of manufacturers have said that decarbonisation is a high priority for their businesses. By putting digital twinning at the top of the Board agenda and seeking professional guidance where appropriate, a successful decarbonisation strategy driven by reliable data can be implemented, and manufacturers will quickly begin to reap the rewards.
For example, digital twin technology can examine energy usage throughout the factory and demonstrate how it can be optimised over time. It could also interrogate the combination of people and skills within the factory to identify gaps and suggest ways to optimise work patterns. It could even become a value-added tool to support upskilling, by training staff using augmented or virtual reality; familiarising them with new systems or machinery before they gain access to the factory floor.
Alternatively, digital twin technology can enable predictive maintenance which continuously monitors machine performance. It can use the data to schedule ongoing maintenance before problems grow out of control as well as predict potential failures: improving efficiency while reducing machine downtime. This will not only save the business capital, but more efficient machines will actively help to reduce a business’ carbon footprint.
A digital twin could additionally give data-based insights into whether operational space is being utilised as effectively as possible, allowing manufacturing businesses to get the most out of their existing space. The same technology could also look at the location of the plant or any planned expansions to understand whether a different operational footprint might be more favourable: for example, when it comes to transport infrastructure links or accessibility.
With so many potential benefits on offer, knowing where to start could be daunting. There are two main approaches that Boards should consider when planning a digital twin strategy: a phased approach or an all-encompassing approach. A phased approach typically starts by looking at a single product or asset and can help to deliver series of quick wins that lower costs while simultaneously reducing carbon.
These quick wins can be useful to demonstrate the potential of the technology and confirm that the business has set out on the right path. While a phased approach might initially seem simpler than a whole-factory approach, a clear strategy is still needed to maximise results. For example, the phased approach could begin with a single product, or it could isolate an entire process and develop a digital twin model for that specific area of operations. Regardless, the most important consideration for businesses should be to demonstrate progress quickly, based on a planned roadmap, ensuring that any efficiencies achieved are not offset by other areas that may retain poor performance.
The second, all-encompassing approach could deliver benefits across an entire factory more quickly, however, it will require more upfront investment and planning to get things right. This approach should begin by assessing all business functions, beyond product development and production line efficiency.
Digital twin technology has the power to transform the UK’s manufacturing sector by streamlining costs, optimising process and operational efficiency and accelerating the way to decarbonisation. As data maturity and connectivity via the Internet of Things increases, these technological capabilities will only grow more advanced. Manufacturers should ensure that they have the right skills in place now and learn from industry best practice to maintain competitiveness with other countries that are actively adopting this technology – competing as an industry on a global scale, rather than as individuals.
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