A supply chain digital twin (SCDT) is a simulation model that mimics supply chain processes like warehouses, transportation networks, and production facilities. The model uses real-time data and snapshots to anticipate the dynamics of a supply chain.
The Digital Twin (DT) represents a physical object or process contextualized in a digital version of its environment. It helps firms understand the supply chain’s behavior, predict and address anomalies.
Moreover, DT obtains data from various sources like IoT devices and operations databases. These insights enable companies to explore real-world scenarios and variables to understand the impact.
The SCDT refers to a detailed model using real-time data to anticipate the supply chain dynamics. Companies can deploy DT in the supply chain to-
Firms use static methods to design and plan their supply chains. However, applying static models to dynamic and evolving supply chains results in inefficiencies.
DT enables companies to test their supply chain design to understand its capability to withstand setbacks after disruptive events like blocked transportation routes and distribution center shutdowns.
DT provides robust visibility, detects patterns, and discovers opportunities to optimize current processes. It builds supply chain resilience by ensuring businesses make rapid, smart, and data-driven decisions.
Most designs and planning were strategized for optimal scenarios based on rationality, stability, and supply availability assumptions. With increasing uncertainties, companies must embrace continuous and dynamic designs.
DT helps create contingency plans and identify transportation or logistics routes. It allows firms to run numerous scenarios simultaneously and address challenges in various strategic areas. The tech improves the existing system, helps foresee disruption, and addresses the crisis before it occurs.
DT helps maintain and test potential breaking points in the supply chain and allows firms to take action to improve overall resiliency. The DT model helps firms to-
The DT model in the supply chain facilitates business continuity and helps address risks before they occur. The model calculates benefits, potential ROI, and savings.
For instance, a company might generate DT models to revamp global operations by simulating many scenarios.
DT allows firms to check, identify the best actions, and explore various scenarios in a virtual environment to boost business stability.
A DT helps firms limit their carbon footprint by identifying opportunities to cut energy consumption. It helps increase sustainability in their supply chain.
For instance, DT helps assess transportation routes for fuel-efficient options and recommend alternative transportation modes.
Moreover, firms can optimize their production processes to cut energy usage, leading to a more sustainable and eco-friendly supply chain.
Companies can identify supply chain shortcomings by running simulations with DT. They can also analyze how the changes will influence the existing operations.
For example, a firm modifying its inventory management strategy can test the modifications to the supply chain in real time.
Also, they can use DT simulation to predict how it affects productivity. These simulations can identify other unintended factors throughout the supply chain.
DT helps the supply chain withstand and recover from disruptions. Real-time insights can help companies identify potential weaknesses and take measures to minimize the impact of disruption.
For instance, a DT model can alert a potential bottleneck in its production process.
It can help create contingency plans as per insights from the end-to-end model. Most importantly, it helps maintain high customer satisfaction levels.
Also Read: Why Composability Is Essential for Scaling of Digital Twins
As per a recent Gartner survey,
The cause of this minimal implementation the lack of recognition of the DToC’s potential to transform demand forecasting accuracy. A SCDT integrates with company systems, unites data flows, and stores, offering a universal working window.
By implementing DT, firms can use data on customer locations and demand patterns that help them optimize their operations. This will start from planning a robust distribution route to identifying areas for inventory storage.
As per a recent report by Markets and Markets, “Digital Twin Market Global Forecast 2028,”
DT will mature to offer immersive, automated, and more intelligent supply chain operations in the coming years.
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Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.
