Digital Twin technology has been receiving a lot of buzz lately, appearing on the Gartner Hype Cycle for Manufacturing in 2018, and on the Supply Chain Planning Hype Cycle in late 2019. It was featured as one of Gartner’s Top Ten Strategic Technology Trends in 2019 and is a common, desired outcome of Hyperautomation – one of the Top Ten Trends in 2020.
So what exactly is a supply chain digital twin? How does it differ from classic supply chain modeling? And how can those differences drive better management of the supply chain? Watch the video below to learn more about Expeditors' digital twin technology, the Living Model service.
The term ‘digital twin’ in the present context was first used by John Vickers of NASA in 2002 and later by Dr. Michael Grieves of the Florida Institute of Technology, though the concept itself is much older. Simply stated, a digital twin is a virtual representation in virtual space of a physical structure in real space and the information flow between the two that keeps the former synchronized with the latter. The key difference between classic supply chain modeling and the digital twin concept lies in that last part – the information flow between the two that facilitates continuous synchronization of the digital to the real, and in the way the models are constructed that allow them to be continuously updated.
For NASA, the concept was first applied in the 1970s during the Apollo 13 program, where engineers on the ground needed to be able to rapidly account for changes to their vehicle while exposed to the extreme conditions in space, and with lives on the line. When life support failed, NASA found they could no longer base corrective decisions on the original model because the actual module had undergone significant changes as the result of exposure to an extremely hostile environment. The original model needed to be updated to more closely mirror the current state of the module. And although the original digital twin at NASA was a combination of physical and mathematical models, today’s information technology allows for pure virtual renderings with amazing precision.
The same dynamics apply to supply chain management. While static supply chain simulation and optimization models can be valuable tools in gaining a better understanding of current performance and the impact of change, the problem is that as soon as the virtual models are created they begin to diverge from the actual, physical entity or structure they represent. The reason is simple – things change, sometimes quite rapidly and in unexpected ways.
By creating a digital twin of the supply chain – a ‘living model’ that is kept constantly updated – decisions are always made based on current conditions, not conditions months, quarters, or years ago, when the original study was conducted. In this approach, modeling and analytics can be tightly coupled with execution – enabling a cycle of continuous improvement and innovation. Measure performance, design based on current, data-driven observations, execute to design, and then measure execution to design; continuously adjusting and course-correcting to adapt to changing market conditions and to exploit emergent, potentially fleeting opportunities.
In order for a study to return real value, its recommendations must be implemented – which means getting stakeholders to believe and take action. Since it is always the same model that is used, all supply chain studies are conducted in the same way validated by transaction-level comparisons to spend. The predictive power of the model enables rapid decisions, increases supply chain adaptability and agility and provides the means to leverage the supply chain as a source of competitive advantage.
