What Are Digital Twins?

Digital twins are a precise, high-level representation of a physical object, system or process. These virtual replicas benefit from a wealth of real-time data to generate models that mirror the characteristics and behaviors of their physical counterparts. Digital twins are utilized in a wide range of industries and applications.

In supply chain management, digital twin models utilize data — sales orders, demand and supply figures, weather conditions, pending approvals, warehousing capacity and more. This information comes from several sources, including:

• Internet of Things (IoT) devices: special sensors and trackers
• Operations databases: sales platforms and transaction information
• Logistics and transportation databases: inventory management, delivery tracking and route optimization information
• Supplier or vendor information: CRM data, order information, invoices and bills
• User experiences: online reviews, satisfaction surveys and customer service tickets
• Competitive analysis: competitor websites

Digital twins technology allows supply chain companies to actively monitor elements and dynamics within a virtual system to make data-driven decisions about the real-life supply chain. For example, DHL uses digital twins to track Tetra Pak packages in real-time at its Singapore warehouse.

Are Digital Twins the Same as Simulation Models?

While digital twins and simulation models share some similarities, they serve distinct purposes. The primary difference is that simulations use set parameters to analyze system performance before implementation in real life. In contrast, digital twins use real-time data to observe operations as they occur.

In this way, simulation models answer the question “What could happen if …” while digital twin models answer “What is happening?”

How Digital Twins Improve Supply Chain Management

The global supply chain management market is growing fast, with an estimated CAGR of 10.9% over the next nine years. The sheer volume of data and documentation to process is massive since it’s a customer-centric industry with complex operations across geographical borders. This presents challenges for supply chain managers, such as:

• How to make accurate decisions and maintain flexibility in the live market
• How to monitor manufacturing and transporting conditions to minimize disruptions
• How to track performance and detect risk factors on the go

Digital twins can help address these challenges by providing an analytic view of the intricate data points impacting these events. Specifically, they can help supply chain managers:

Forecast Problems

Digital twins technology provides an end-to-end view of processes across the supply chain, allowing managers to identify potential bottlenecks and inefficiencies. Since supply chains are still feeling the effects of disruptions from the pandemic, digital twins are an invaluable tool for assessing risk and developing tailored response plans.

Test Scenarios

Supply chain management stakeholders can use digital twins to test different scenarios without affecting the day-to-day operations of the real-world supply chain. For example, they can check for potential issues related to health pandemics, geopolitical conflicts and other black swan events to understand their potential disruptive impact. Armed with these insights, managers can plan strategic responses accurately and effectively.

Optimize Logistics

Implementing a digital twin supply chain allows for advanced modeling of the logistics process to pinpoint areas where disruptions and shortages mainly occur. It also provides an overview of real-world supply and demand dynamics, allowing logistics teams to model optimal deliveries based on actual data rather than relying on guesses and gut feelings.

Avoid Stockouts and Overstocking

Companies can generate digital inventory replicas using digital twin technology to monitor stock levels and conditions in real time. This benefit is significant in retail supply chain management, where e-commerce firms must often optimize inventory management with demand/supply forecasting to avoid overstocking or stockouts.

Support Sustainability Efforts

Sustainability monitoring is another way digital twins improve supply chains. Businesses can utilize sensors and tracking technologies to highlight where the bulk of their carbon emissions stem from and measure impact in a digital environment. This helps them make informed decisions regarding their sustainability initiatives.

Implementing Digital Twins

Implementing a digital supply chain twin requires a collaborative approach among all stakeholders. The main steps involved in the process include:

1.    Mapping the Real-Life Supply Chain

This step is all about comprehensively mapping out the supply chain assets and operations to ensure a replica in the digital environment.

2.    Determining the Data Sources

Decide where to pull real-time data to feed into the digital supply chain twin. These information sources must be relevant and directly impact physical supply chain operations.

3.    Building the IT Architecture

A robust IT architecture is essential for a digital twin to mirror its real-life counterpart effectively. At the very least, it should be able to connect to multiple data sources and run different accurate simulations based on both real and virtual data.

4.    Building the Digital Twin

Supply chain management is ever-evolving, so it’s vital to model the digital twin to handle long-term operations and variables. The model should be a precise representation of the real-life supply chain.

5.    Simulating and Analyzing the Model

Run various simulations on the digital twin model using real-time data and analyze the results to gain better insights into optimizing the supply chain.

Digital Twins Can Transform the Supply Chain

Digital twins are completely transforming how supply chains function. These virtual models provide high-level visibility and low-risk testing capacity, allowing teams to minimize disruptions and maintain productivity even during times of uncertainty.

Zac Amos is the Features Editor at ReHack and a contributor at publications like DZone, Open Data Science, and IoT For All. You will find him covering supply chain and logistics tech, the IoT, and cybersecurity. For more of his work, follow him on Twitter or LinkedIn.