How the world’s 250 Digital Twins compare? Same, same but different.

The digitization boom of the last 6+ years has brought up some truly exciting new technology concepts. Digital Twin is one such concept that has gained wide interest and according to Google Trends even tops general search interest in widely searched topics such as the “Industrial IoT” or “IoT Platforms”.

Exhibit 1: Google Trends analysis of search interest in “Digital Twins” versus “Industrial IoT” and IoT “Platform”
What is a Digital Twin?
Today many companies offer “Digital Twin technology” but a closer comparison reveals vast differences. IoT Analytics recently looked at Digital Twins in-depth and published a 90-page insight report on the topic.
A first glance at the websites of companies that offer Digital Twin technology shows that many definitions for digital twins already exist, however there is no commonly accepted definition.

Siemens , for example, defines Digital Twins as “ a virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics ”.

Dassault Systems takes a narrower view and defines Digital Twin technology as “ a virtual representation of what has been produced .”

IBM limits the scope to real-time data and defines it as “ a virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning and reasoning ”.

The commonality in most definitions is that Digital Twins are a “ digital abstraction or representation of a physical system’s attributes and/or behavior ”. The devil is in the details because one could still refer to a fully-automated dynamically-recalibrating virtual representation of a physical system or alternatively just one software element of that setup – or something else.
The 250 classifications of Digital Twin technology
The research that IoT Analytics performed on the topic showed that there are 3 dominant dimensions by which Digital Twins can be classified:

The hierarchical level the Digital Twin is applied to (6 levels identified)
The lifecycle phase in which the Digital Twin is used (6 phases identified)
The use of the Digital Twin (7 most common uses identified)

There are 252 potential combinations of the 6 levels, 6 phases, and 7 most common uses (6x6x7=252), all of which describe a different classification of a Digital Twin.

Exhibit 2: The 250 classifications of Digital Twin technology
The resulting Digital Twin cube explains why a digital replica which is used for a “ product simulation during the design phase ” is completely different to a “ process parameter prediction during manufacturing operations ”. Both are called Digital Twin but only have limited overlap.
One should note that a fourth dimension could be added to describe the data type used...