By Anthony Liu
“What I cannot create, I do not understand.” – Richard Foreman
Digital twin is entering more and more industries and fulfilling their potential alongside artificial intelligence (AI). According to Fortune Business Insights, the global digital twin market size is projected to reach USD 96.49 billion in 2029, at a CAGR of 40.6% during forecast period. Digital twins can simulate products or buildings, while supply chain twins can simulate connections between raw materials, finished products, and customers. When a company’s supply chain is interrupted due to the epidemic and transportation links, the importance of supply chain twins becomes apparent.
What is Digital Twin
A digital twin is a virtual representation (often in three dimensions) of a system in real life. There is data flow between the virtual sibling and its matching real-life sibling, which is updated via sensors on the real system so that the status of the real system can be monitored in real time. This process is more than just a status update of the real-world system. Virtual systems can be programmed to run hypothetical scenarios to get the best parameters.
Typically, digital twins contain different layers, with each virtual layer representing an equivalent physical layer. These layers can include physical, material, structural, electronic, fluid, etc. As long as the system can be measured, it can be modeled in a digital twin, and the more data that can be taken from the real system, the more powerful and accurate the digital replica will be.
The rapid development of digital twin mainly relies on four key technologies:
The Internet of Things (IoT), which uses low-cost sensors to collect vast amounts of data from the physical world;
Cloud Computing, which provides (flexible enough) infrastructure support for the storage and processing of large amounts of data;
Artificial intelligence (AI), which allows people to discover patterns in large amounts of data and make optimal decisions;
Virtual reality/Augmented reality/Mixed reality (VR/AR/MR), which makes the digital world seem more "real".
Digital Twin in Supply Chain
The digital-twin approach can be applied to products, manufacturing processes, or even entire value chains. Digital twin supply chain can break through the bottlenecks of response speed and cost of the traditional supply chain by penetrating the digital twin into every link of a supply chain system. This can effectively connect the upstream to the downstream, realize supply chain coordination, improve the efficiency, and accelerate the response with supply chains.
Specifically, supply chain collaboration includes relying on the digital twin to provide data, visual insight into market demand, linkage to the twin supply chain planning and supply chain execution, so as to make the supply chain run efficiently. According to McKinsey, digital twin can help companies boost revenue by up to 10%, improve time to market by around 50%, and upgrade product quality by up to 25%. Below are some of the core areas where DT can be applied in supply chain.
Packaging
Digital twin can help us design new materials for packaging, so as to reduce environmental pollution and waste caused by logistics packaging. For example, Math2Market's material digital twin system can simulate the process of new material packaging going through different temperatures, vibrations, sorting and handling during the logistics process to predict the performance of new material packaging.
Shipping
With Digital Twin, we can digitally track the transportation process of some high-value goods, record the temperature, humidity, and the condition of the goods in the whole process, so as to determine whether the carrier has protected the goods according to the agreement. In case of goods damage, there would be sufficient evidence to support the claim. In addition, taking the ocean-going freighter itself as a model tracking object of the digital twin can help to monitor its transport time and prevent accidents.
Warehouse/Distribution Center
The digital twin in warehousing is akin to building a smart factory. A digital model of the whole warehouse based on data collection can be created, which will carry out detailed simulation of warehouse layouts. This can help optimize space utilization and increase personnel efficiency. In addition, based on the digital warehouse, people can further optimize the unmanned forklift, picking system and so on.
Risks of Digital Twin
However, as the demand of digital twin becomes more prevalent, there are concerns of privacy and security to impede the solution. For developing the digital twin, the solution involved the integration of multiple IoT sensors and other digital technologies. With the increasing number o IoT sensors and programmable electronic devices, there is an augmented risk of security, complacence, and data protection, as well as legislation.
Conclusion
There's a lot we can learn from digital twins, whether it's a diagnosis or a prediction. The information can be used to save time and increase productivity and agility. It can be used to trace back to the product development stage from the actual life service cycle, thus making it possible to continuously optimize future products within a closed product life cycle.
Backed by massive amounts of data, as unique virtual representations of potential or actual physical objects and processes, digital twins enable companies to design, visualize, monitor, manage, providing a new way for us to interact with the real world.
References:
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