Digital Twin

🧑‍🔬 DI in Industry (DIiI)

Andy Weeger

Neu-Ulm University of Applied Sciences

February 13, 2024

Introduction

Companies can begin the journey by starting with just one digital twin that has a data product at its core, evolving it over time to provide increasingly powerful predictive capabilities and the foundation of an enterprise metaverse. McKinsey & Co.

Relevance

Digital twin technology provides capabilities to enable new business models and decision support systems by incorporating data collected from multiple sources and analytic capabilities.

Origins

The concept originates is nearly 15 years old and stems from the field of product lifecylce management Grieves (2014).

Definition

A digital twin is “a virtual representation of a physical system (and its associated environment and processes) that is updated through the exchange of information between the physical and virtual systems.” VanDerHorn and Mahadevan (2021, 2)

The digital twin can be characterized by three primary components:

  1. A physical reality,
  2. a virtual representation, and
  3. interconnections that exchange information between the physical reality and virtual representation

Components

Dicussion

Based on the definitions, a simple 3-D visualization or stand-alone simulation would not be considered a digital twin.

What are key requirements of a digital twin?

Systems of digital twins

Business value can be increased by interconnecting digital twins to complex ecosystems.

Such interconnected digital twins allow, e.g., to

  • simulate the complex relationships among different entities and generate richer behavioral insights and
  • incorporate dependencies and correlations into the models (by analyzing the interactions)

Example

Combining the digital twins of customers with the twins of retail and online shops would enable the creation of outstanding omnichannel experiences that seamlessly support the customer journey across all channels.

Implementation types

According to VanDerHorn and Mahadevan (2021), current industry implementations of digital twins can be generally grouped into three categories:

  1. Digital twin component solutions (e.g., provided by Microsoft),
  2. commercial off-the-shelf solutions (e.g., provided by OEMs like GE or Siemens), or
  3. custom hybrid combinations.

Examples

McKinsey and Co. outline following examples for digital twins:

SoFi stadium (in California)
To help optimize stadium management and operations, a digital twin aggregates multiple data sources, including information about the stadium’s structure and real-time footfall data.
SpaceX
A digital twin of the Dragon capsule enables operators to monitor and adjust trajectories, loads, and propulsion systems. The goal is to maximize reliability and safety during transport.
Anheuser-Busch InBev
A brewing and supply chain digital twin enables brewers to adjust inputs (for instance, “add more hops to mixer #3”) based on active conditions and can automatically compensate for production bottlenecks when, for example, vats are full. It also gives the company’s production engineers remote assistance and AR capabilities for live troubleshooting on how to fix pump leaks and other common issues.

Future application fields

The growing consensus is that digital twin implementation will expand in many areas including healthcare as promising field (Saracco 2019).

Digital twin applications in healthcare and education imply that the concept will be extended from artificial systems to people.

The availability of millions of digital twins of people would make inference possible (e.g., by applying AI) and help practitioners gain knowledge that could be used for precision medicine and proactive health care.

Technology challenges

Saracco (2019, 62) outlines following challenges related to the design and implementation of digital twins:

Interoperability of digital models, data interconnection, extended digital twins, and data privacy, availability, and ownership.

Q&A

Literature

Grieves, Michael. 2014. “Digital Twin: Manufacturing Excellence Through Virtual Factory Replication.” White Paper 1: 1–7.
Saracco, Roberto. 2019. “Digital Twins: Bridging Physical Space and Cyberspace.” Computer 52 (12): 58–64.
VanDerHorn, Eric, and Sankaran Mahadevan. 2021. “Digital Twin: Generalization, Characterization and Implementation.” Decision Support Systems 145: 113524.