Manufacturers in the UK have faced a turbulent couple of years, with inflationary pressures and supply chain disruption making it harder for them to maintain agility and respond to new challenges.
The ongoing semiconductor shortage is a widely reported example of the supply chain challenges affecting UK-based manufacturers, particularly those in the automotive sector. Other supply shortages affecting the raw materials needed to make batteries, for example, are also having a knock-on effect on global production of electric vehicles. This environment of ongoing supply disruption and increased cost volatility is encouraging businesses to unlock the potential of new data-driven technologies to inform their decision making, enabling them to build a more resilient business model.
One area that can be used to support better decision making is digital twin technology. In simple terms, a digital twin is a virtual representation of a physical asset or process. It is continually updated from real-time data, analysing multiple scenarios at once to optimise decision making and generate performance insights. These insights enable manufacturers to improve overall business agility by responding to a range of ‘what if’ scenarios much more quickly.
For example, a manufacturer may want to optimise the total cost of ownership (TCO) of high wear assets such as train wheels, with a view to realising opportunities to reduce costs while extending the life of the asset. Digital twin technology will enable the manufacturer to continually optimise real-time decision making around maintenance and repair while supporting better procurement processes, driving supply chain efficiency, and balancing other performance-related KPIs.
In another scenario, digital twin technology could be used to improve production line downtime and increase OEE (Overall Equipment Effectiveness) by continually monitoring equipment condition and optimising decisions around when to carry out critical maintenance across an entire production system with a view to either minimising cost/ waste, increase uptime or balance the optimum across all performance KPIs. It is important to remember that the definition of ‘real time’ will very much depend on the use case, for example in a production environment this may be in seconds, whereas in the train wheel example this could be weeks or even months. Over time, digital twins can therefore become an invaluable source of data, leading to the introduction of improved machine maintenance practices and better value stream monitoring.
One of the main challenges for manufacturers looking to implement digital twin technology, is having access to accurate and reliable data. It’s also vital to gain stakeholders’ trust in the use of digital twin technology and its potential to unlock value. As such, steps must be taken to clearly understand and communicate the full range of benefits to both the organisation as a whole and the individual. Like all successful technology-led transformations, digital twinning should become an invaluable aid to decision making that works in the background, without requiring any major change to existing working practices.
As part of a holistic approach to cost predictability, along with the use of digital twin technology, another effective technique for improving agility in the current economic climate is the use of inflation modelling. When combined with product ‘should-cost’ modelling techniques, this can help to not only predict the impact of future market volatility on supply chains, but also understand which suppliers are most likely to be passing on price rises, based on historical pricing trends. These modelling techniques can help businesses to manage market-related uncertainties, and with inflationary pressures mounting, they are more important than ever.
While manufacturers can’t influence inflationary pressures and supply chain shortages show no signs of ending, digital twin technology can help them to regain some control by predicting future costs, allowing them to plan ahead. However, effective communication with key stakeholders and reliable data are needed to realise its potential.
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