Simplifying Complexity in Supply Chains through Digital Twins -


A digital twin enables an enterprise to seize full control of its supply chain system. A virtual supply chain replica, a digital twin could be a component, an asset, an employee, or a process, as in manufacturing. The digital twin model could also encompass items packed in containers, moving through the physical world to distributors and customers. By emulating the supply chain’s performance, digital twins can offer insights to augment decision-making across several multiple planning horizons.

Digital twins are being deployed in supply chains because of the increasing use of the Internet of Things (IoT) and machine learning in tandem. The connection with the physical model and its digital twin is established by generating real-time data using sensors. The digital twin in the supply chain allows a comparison between current and historical data on performance, wherever a sensor is located. Moreover, the digital twin could inherit data from the process that created the product at one end of the chain, and inform a customer model at the other end.

However, to realize the benefits of this capability, standards, agreed upon taxonomies, and commercial development tools and platforms should be developed. Consequently, practitioners from product lifecycle management, IoT, and analytics, and data science fields are beginning to focus on resolving some of these foundational standards.

The Digital Twin Consortium is one such group working on defining a taxonomy and standards; and enabling technology including Artificial Intelligence and simulation. The overarching goal of the consortium is to bring industry, government, and academia together to drive consistency in vocabulary, architecture, security, and interoperability of digital twin technology.

An inclusive digital twin could offer an end-to-end view of the supply chain. Along with an understanding of the status and history of assets and processes, machine-learning tools can be leveraged to execute simulations, optimizations, and predictive capabilities to the models. In short, with a digital twin of supply chain processes, enterprises can simulate, predict, and gain insights into every level of logistics execution.

Click here to read the original article published by the Cambridge Innovation Institute.

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