Digital twins allow for the simulation of their physical ‘twin’, which will streamline and manage the supply chains and business strategies of the future.
A digital twin is a computer programme composed of real-world data about a physical object or system that can be used to model real-world behaviour. Internet of things (IoT) sensors on a real object gather data, such as telemetry, weather, temperature, humidity, motion and other factors, and feed that through to the programme, which can then render that information into a useful format. This information can then be used to simulate different, alternative routes to market, different packaging tolerances and different environments. Digital twins provide an unprecedented level of granularity and transparency to the supply chain, providing a company with greater cost savings through true optimisation between production and the consumer, even providing clues about what a company should do next.
In recent years, the internet of things (IoT) has exploded in popularity, with sensors becoming more detailed and ubiquitous. Digital twins can be used to predict different outcomes based on variable data. By joining up IoT data with machine learning and predictive AI, producers will be able to model which routes cause the least product failures, for instance due to climate change altering some areas’ weather to become more turbulent, or causing increased delays. A hypothetical example might be that the programme registers or simulates delays in certain ports caused by Covid-19, and then sends that prediction to the producer before the products are shipped along that route.
Digital twins offer real-time feedback on the status of physical assets, and this can alleviate maintenance and faultfinding burdens. Chevron , for instance, is applying the technology to oil fields and refineries, and expects millions of dollars in cost savings in the region as a result. The evolution of sensor technology, cloud services and machine learning are enabling companies to predict when maintenance is likely to be required. Digital twin technology can gather and analyse a range of data regarding equipment efficiency, calculating the long-term maintenance costs associated with operating a piece of equipment and even suggesting the optimal time to have the machine operational per day. Major retailers are using IoT sensors to collect enormous amounts of data from their warehouses and logistics processes with the goal to reduce operating costs and improve efficiency.
Sustainability is another key area of interest, with digital twins likely to emerge as a key tool in dealing with CO2 going forward; in addition to sensors quantifying where and when energy is used and lost through inefficiency. This wealth of information in turn will be used to model new warehouse standards and systems to combat the problem. Beyond helping to simply design changes to existing supply chains, the technology promises to help decide which design change saves the most labour, carbon and immediate cost. The wide variety of applications will undoubtedly expose the technology to businesses not just in energy and retail, but those working across FMCG, packaging, foodservice and beyond.
Businesses that lower waste and inefficiency will be able to publicise their environmental credentials and make themselves more attractive to the ethical and environmentally concerned consumer. Indeed, GlobalData’s Week 3 Covid-19 recovery consumer survey found that 44% of global consumers said that how ethical, environmentally friendly or socially responsible the product or service is ‘always’ or ‘often’ influences their choice.
The next generation of supply chain innovation is on the horizon and the apt deployment of digital twin technology will enable consumer-focused companies to expect lower costs, lower waste and more-impressive environmental credentials.