Necessity is often said to be the mother of invention, and the story of Apollo 13 in 1970 is a prime case in point.
After lift-off, an oxygen tank explodes in the side of the spacecraft, depleting its oxygen supply and power. The ground crew issue guidelines to the astronauts, which prove to be irrelevant because they bear no relation to the real-world circumstances.
At Mission Control, a fellow astronaut realizes the problem and organizes a team to replicate as exactly as possible the conditions being experienced in space. They equip their own physical twin replica of the spacecraft with only the tools and materials available to the Apollo 13 crew, carrying out experimentation in a safe, offline environment to find ways round the problems not in the manual.
Almost 50 years later, we’re increasingly seeing the development of non-physical, digital twins for the same purpose.
Driving the virtuous circle
As a digital replica of potential and actual physical assets, processes, people, places, systems, and devices, digital twins are quickly becoming established in domains heavily based on the Internet of Things (IoT).
Digital twins provide a playground for human and artificial intelligence (AI) minds to meet, pulling in the data needed to train AI models that can bring huge benefits to domains of finance and accounting (F&A), human resources (HR), and supply chain management (SCM). In this realm, the data-heavy process itself is the asset – in effect, a production line for processing data.
Part of the usefulness of a digital twin lies in its capacity to be modeled on reality but developed in isolation from the real world until it approaches the best possible performance in its current and anticipated circumstances. Capturing the “as is” metadata of an organization, its activities, people, and systems, drives a virtuous circle cycle of business mining, modeling, and improvement that provides a clear perspective on how things are operating, and helps shape and define a model of the digital twin.
This model can then be used to simulate any number of scenarios that explore hypotheses and opportunities for change.
Testing the limits
On top of a cyclical sequence of steady, incremental improvement, digital twins enable organizations to test scenarios to their heart’s content. By taking things to the max, businesses can simulate radical changes to operations to see what happens, in a way no one would dare attempt in real life.
While the cyclical approach aims to achieve steady improvement in a stable environment and the extreme approach addresses cases of unlikely triumphs and disasters, a third application provides a means of developing an appropriate reaction to possible or even probable scenarios before they happen.
On top of this, machine learning (ML) can be applied to the mining data received from the company’s systems to makes predictions for key metrics and service level agreements (SLA) that will improve over time. This gives organizations foresight over business operations, enabling considered and prepared responses.
Navigate the future
The implications for organizations extend beyond simply process improvement, and can result in some exciting prospects:
- A continuous data stream that maintains the digital twin in perfect synchronicity with an organization’s business operations
- More advanced monitoring that improves compliance and isolates key data to support root cause analysis
- A world of prediction that helps the organization reinvent its digital operations
- An active feedback loop between strategy and execution that brings new evidence to performance management
- A means to test and evaluate change scenarios that enliven a continual cycle of improvement
- Divisional and enterprise-level modeling for enhanced visibility of business operations through combining digital twins
- Predictions of business-impacting events that can lead to less reactive management of SLAs
- Creation of an AI playground by collecting training data used to seed any number of AI algorithms.
The digital twin is already transforming the efficiency of current business processes, but can also enable organizations to transform their current models to adapt to the changing circumstances – benefiting the organizations, their suppliers, and customers alike.
Apollo 13 had a happy ending – the space crew all returned safely to Earth. The story of digital twins will also be a happy one – but here, there won’t be a splashdown. The journey is just beginning.
Read a point of view on the application of the digital twin to finance, HR, and supply chain, and its implications for business operations.
Lee Beardmore has spent over two decades advising clients on best strategies for technology adoption. More recently, he has been leading the push in AI and intelligent automation for Capgemini’s Business Services. Lee is a computer scientist by education, a technologist at heart, and has a wealth of cross-industry experience.