Digital Twin Technology - From Concept to Reality

Digital twins are the next step in the evolution of simulation technology. Early developments began in the 21st-century, starting from computer-driven simulations to the creation of AutoCAD and other simulation applications that have grown to current system design programs. Technology such as increased computing power and reliable sensors has made digital twins a real possibility.

What exactly are digital twins?

The digital twin is a solution to help bridge the physical and digital world. Digital twins take 3D modeling to the next level by capturing more than just static information and becoming a live e-model of the product. Sensors capture dynamic data that provides the accurate state of the product to better support areas such as prevention, troubleshooting, and quality management. It provides feasible and realistic simulations that will or could happen with a product. To put into simpler terms as defined by IBM, “The digital twin is the virtual representation of a physical object or system across its life-cycle. It uses real-time data and other sources to enable learning, reasoning, and dynamically recalibrating for improved decision making.”

The common key characteristics of a digital twin given by DHL are:

1.       A digital twin simulates both the physical state and behavior of the thing

2.       A digital twin is unique, associated with a single, specific instance of the thing

3.       A digital twin is connected to the thing, updating itself in response to known changes to the thing’s state, condition, or context

4.       A digital twin provides value through visualizations, analysis, prediction, or optimization

Effects in the Supply-Chain and Logistics Industry

Digital twins in the early stages were being used in high-value and high-criticality products such as aerospace and defense. Now, companies are starting to collect data for the use of digital twins outside of these industries. This can create a major influence in the product life cycle and manufacturing.

Product life cycle

·       Faster design iterations

·       Reduced development costs due to decrease in physical prototypes

·       Competitive advantage in fast-tracking a product into the market

·       Increased reliability of final product

Manufacturing:

·       Distinct specifications for suppliers, creating optimized shipping and manufacturing designs

·       Models with specific components and materials for all products

·       Conduct realistic simulations for layouts, processes, and material flow before facility is created

·       Troubleshoot product faults to help find root causes and optimize future performance

Challenges

The biggest challenge that digital twins face in the logistics industry is the quality of data. Current digital twins are not exact e-replicas of the product. This is because it is extremely expensive to collect and clean data such as the physical, chemical, electrical, and thermal state of a product. To bypass this, engineers are using assumptions and simplifying their models.

Collecting high-quality data can be expensive which poses the question if implementing digital twins are worth it. High-value and high-criticality industries will continue to expand digital twins, but we may not see adoption in the commercial industry in the near future.