For many years, we have created dummy models of the real-world and given shapes to replicate the world objects. Today, the evolution of technologies enables us to link previously made offline physical assets to digital models.
This helps alter processes or modes experienced by the physical object reflected in the digital model to get valuable insights and control unprecedented situations.
The latest technologies like the internet of things (IoT), big data, artificial intelligence, cloud computing, and digital reality technologies augmented the emergence of digital twins enabling the physical and digital worlds to manage as one. The increasingly sophisticated digital twins can now help companies design, visualize, monitor, manage, and maintain their assets more effectively.
According to a report by Growth Market Reports, the global digital twin market was valued at USD 3.1 billion in 2020 and is projected to reach USD 113.3 billion in 2030. Further, it is expected to expand at a CAGR of 42.7% between 2021 and 2030. Digital twins are already creating new value and unlocking new business opportunities for the engineering, manufacturing, automotive, healthcare and energy industries.
The Supply chain industry is not behind in adapting to this latest technology. Digital twins in connection with artificial intelligence will bring huge changes in the traditional supply chains, depending on how the sector might embrace this technology to improve processes and overcome challenges.
What is a digital twin?
A digital twin is a virtual representation of a real object or process that replicate a physical object/ asset, situation or process designed on the foundation of historical and predictive data and algorithms collected over time. Digital twins help create a risk-free hypothetical scenario analysis by developing a predictive and prescriptive decision-making platform and getting answers to “what-if” conditions. In the supply chain context, a digital twin is a software replica of various supply chain scenarios related to warehousing, logistics, shipments, routes, weather conditions, port congestions, inventory and many more.
Underlying technologies facilitating digital twin
Digital twins use several technologies to provide a digital model of an asset, namely the internet of things, cloud computing, APIs and open standards, artificial intelligence, and digital reality technologies.
The Internet of Things (IoT) – IoT technology is gaining popularity for enabling monitoring, gathering, remote access and tracking data from a wide range of objects. IoT technologies make digital twins possible by structuring, accessing, and analysing complex product-related data both technically and economically.
Cloud computing – Cloud computing allows data to be managed effectively by storing it centrally in one location without any dedicated hardware infrastructure. Cloud storage now enables companies to access data from centre networks via software-as-a-service (SaaS) solutions and acquire computing resources from anywhere, anytime, as per their requirement. Digital twins require developing, maintaining and storing huge amounts of data from various sources. Cloud computing has made it easier and cost-effective for the supply chain to access information remotely for digital twins.
APIs and open standards –APIs and open standards allow dealing with various supply chain activities, monitoring the consequences and handling questions.
The APIs enable companies to connect their platforms to all stakeholders, including carriers, shippers, ports, banks and other information sources. This helps supply chain companies streamline sharing and exchange of data, thus making it possible for users to combine data from multiple systems and tools quickly and reliably. The valuable insights simulation available from APIs and open standards also help design friendly interfaces for interactions with a digital twin.
Advanced analytical tools have dramatically transformed the way supply chain companies extract insights from complex data sets.
Artificial Intelligence – Artificial Intelligence is capable of processing massive amounts of data. The machine learning frameworks enable the development of systems that make decisions autonomously and predict future demand based on historical and real-time data.
AI in digital twins helps design an intelligent model for the entire supply chain network that makes the availability of all information possible in advance and increases transparency among the partners involved.
Visualizing tools Virtual, Augmented and Mixed Reality – To effectively represent the scenarios generated by digital twins and to leverage, consume and predict, so far data is rendered in two-dimensional space. But increasingly, advanced technologies like augmented, mixed & virtual reality enable to display of digital content in 3D space. In addition, mixed reality allows interaction with digital content in the existing physical environment, and virtual reality can create entirely new environments. These technologies will now help render digital twins in a highly immersive way, facilitating maximum consumption and interaction of information and making it real to the user.
How do digital twins help improve overall logistics operations?
Digital twins can be used for better understanding a supply chain’s behaviour, foresee uncertain circumstances, and create an action plan in advance to save costs and increase process efficiency. The ability to immediately visualize the status of an asset via its digital twin makes information more accessible and easier to interpret from a distance.
Digital twins can not only predict the issue that may occur but also propose possible solutions. Digital twins will play a significant role in developing smart solutions capable of making autonomous decisions to answer what, when and how to enhance customer satisfaction and profitability. Let us delve into how digital twins can reduce challenges and add value to the supply chain processes.
Identify Bottlenecks – By analyzing data, digital twin helps to identify potential bottlenecks and can provide an end-to-end view of processes across the supply chain. This helps in finding a more agile solution with little human intervention.
Predict dynamic changes – A digital twin can help businesses to identify trends and simulate the effects of changes before they arise through result prediction. The models can further help design numerous simulation scenarios, including production, inventories, routes and product delivery, and calculate benefits, savings, and possible ROI.
Understanding supply chain dynamics and increasing productivity –
Every supply chain runs on numerous unprecedented ifs and buts scenarios. The digital twins can help to identify areas of weakness with simulations run and see how specific changes in the supply chain dynamics will affect current operations.
Furthermore, the digital twin simulation can also demonstrate the other unintended consequences, monitor risks and test contingencies that may occur throughout the supply chain.
Digital twins also facilitate the optimisation of transportation and inventory planning, forecasting operations over the coming days and weeks, cash-to-serve and cost-to-serve analysis and examining overall supply chain management’s performance. Above all, by analyzing the evolving customers’ preferences, digital twins are already enabling early adopters to stay ahead of today’s supply chain disruptions.
The future of supply chain digital twins as models of existing supply chain assets is paving the way for a future that will help in data-driven decision-making and collaboration, streamlined business processes and more resilient new business models.