Supply chains are the central nervous system of the global economy, facilitating the movement of billions of dollars in trade every day. But as the past few years have shown, disruptions are hard to predict.
As companies look for ways to offset these risks, attention has turned to so-called digital twins. These detailed simulations of real-world objects, systems or processes are built with real-time data and provide snapshots that can help firms to monitor threats, test different scenarios and improve decision-making.
So far digital twins have mainly been trialled in manufacturing, but companies such as SAP and Oracle are starting to explore their use in supply chains. The hope is that they can help to identify trade bottlenecks, predict fluctuations in demand and tackle transport and inventory issues. However, the technology is still at an early stage and its implementation comes with costs and challenges that must be overcome.
‘Verging on revolutionary’
Joseph Buckley, director at Control Risks, a global risk consultancy, is something of an evangelist for digital twins. The potential benefits of using the technology to manage supply chain risk are “vast and verging on revolutionary”, he says.
“By combining critical data, intelligence and indicators from the technologies represented by digital twins, decision-makers will be able to make more effective, proactive and well-informed decisions,” Buckley explains. As a result, they could substantially improve the efficiency and resilience of supply chains, increase productivity and reduce cost, he adds.
For example, Buckley points to supply chains in the shipping industry. Digital twin technology could help the maritime industry map supply chain vulnerabilities more effectively, prevent mechanical failures before they happen and identify optimal shipping routes using data from sources such as GPS, ports, warehouses and shore-side operations.
It sounds exciting, but to date only a few pilots of digital twins in supply chains are underway. The market for supply chain twins also remains relatively small and is only forecast to hit $6bn by 2030, according to Grand View Research.
Complexity is one obstacle. Most digital twin pilots have been run in areas such as process manufacturing or drug development, where developers build a digital doppelganger of a cell to model its interaction with a medicine or of a machine to monitor wear and tear.
But accurately modelling a sprawling global supply chain is arguably much more challenging, given the many variables involved, from changes in the weather to political decisions made by far-off governments.
Technical barriers
To succeed, firms will need to cooperate closely with multiple suppliers – and potentially even competitors – across industries and countries. Digital twins also rely heavily on emerging technologies that most firms are only beginning to adopt. These include internet of things (IoT) sensors that collect real-time information on the ground or communication technologies capable of moving large volumes of data.
Firms must overcome the technical challenges and costs associated with storing and using large volumes of data in various formats and the cybersecurity concerns associated with sharing commercially sensitive data across multiple organisations.
Many businesses lack the technical expertise to embark on transformational initiatives
“Many businesses lack the technical expertise to embark on transformational initiatives,” says Marcin Figurski, technical director at Qodea, a consultancy that supports companies with the creation and application of digital twins. “This is particularly true for supply chain and logistics companies, which haven’t traditionally been viewed as technologically advanced.”
Figurski says many companies already have the data they need to run effective supply chain digital twins, but it is often fragmented and siloed across departments.
“Organisations now need to properly digitise and consolidate this fragmented knowledge. By centralising this information and leveraging technology, companies can create more reliable and objective digital twins,” he says.
This will likely require considerable resources. But as it becomes increasingly difficult to predict disruptions to global supply chains thanks to geopolitical uncertainty and climate change, the need to model risk will grow.
Building confidence
The technologies required to implement digital twins are also improving rapidly. “The concept of digital twins has been around for a while. But only in the past five years have the key technology building blocks – like IoT and machine learning – evolved enough to enable digital twins to become a reality.” So says Sameer Kher, senior director of product development, systems and digital twins at Ansys, a provider of engineering simulation software.
Successful pilots in manufacturing and pharma should build confidence in digital twin technology, says Kher. Meanwhile, the Digital Twin Consortium (DTC) – an industry group that seeks to accelerate the market – is helping to drive digital twin adoption and best practices across the globe.
“Earlier this year, the US announced $285m in funding to build digital twins of semiconductor manufacturing equipment. And in the UK, a new Digital Twin Centre backed by government funding is set to open this year,” Kher says.
Experts think advances in AI could also accelerate the use of digital twins in supply chain management. For instance, AI tools could help overcome some of the data-management challenges associated with digital twins. And the granular data captured by digital twins could be used to enhance the accuracy of generative AI models.
For firms seeking to model complex supply chains with digital twins, there is still a steep hill to climb. Key technologies such as IoT have developed significantly over the past decade, but the obstacles that have so far prevented the use of digital twins in supply chain management remain in place.
Owing to the many barriers to deployment, Buckley believes the widespread use of digital twins in supply chain management “remains unlikely in the coming years”.
