Last Week

The last couple of articles in our Digital Twins Series have looked at the role of Digital Twins & Decision-Making for the benefit of our communities; whether that be as a virtual testbed for urban development or a Digital Platform for combining multiple data sources from all across a city. Part 1 and 2 of this sub-series gave us incredible insight into what is needed for Digital Twins to genuinely inform decision-making, but what precisely is the technology that supports the Digital Twin to Decision process?

This Week

To round up this small sub-series, we take a closer look at the technology that underpins Digital Twins, Simulation, and how this key aspect of Digital Twins turns Decision-Making into SMART Decision-Making. Slingshot’s CEO/CTO Dr David McKee gives us a whistle-stop tour of the types of simulation out there and specifically the Why..? Or So What..? of simulation: What’s the point of simulation and why is it a necessary component in future decision-making?

The 3 Types of Simulation

A simulation is an […] imitation of the operation of a process or system;[1] that represents its operation over time.[i]

In other words, a simulation imitates how something has behaved previously, presently and how it could behave in the future.

However, it’s not a one size fits all: Everything in the world has a unique way of operating therefore different types of simulation are required to allow for various processes and systems to be modelled. Simulations fall into one of the following simulation taxonomy categories:

  1. Discrete Simulation is when different parts of the simulation happen at specific points in time. Consequently this type of simulation can be either:
    1. Event-driven simulation, describing a series of events or activities that happen or take effect at a particular point in time, sometimes with a duration.
    2. Time-stepped simulation, described by time steps or clock ticks where things happen at each tick.
  2. Continuous Model Simulation is in the name: the simulation runs continuously with no gaps in between time stamps, unlike Discrete simulation. It uses ODEs and PDEs (i.e. differential equations) such as CFDs (fluid dynamics) and FEA (finite element analysis)
  3. Monte Carlo Simulation is where you are running a lot of simulations at the same time and in random orders until you find the solution.
From left: Discrete vs Continuous Simulation Chart and example of Monte Carlo Solution Space

What is the point of Simulation?

A question that is often asked is why simulate at all? What benefits does simulation offer society?

To the point, simulations allow for testing and experimentation in a more cost-effective and/or safer environment. Using data gathered from the real world, organisations, companies and individuals can create a virtual testbed to play out ideas without having to waste physical resources or putting people’s lives at risk.

To extend on this, if we wanted to study a system or process, such as a jet engine, a new house, or a vaccine, we have a series of options to choose from:

  1. Experimentation with the real thing: i.e. a real engine, someone’s home, or a human being.
  2. Experimentation with a model, using either a:
    1. Physical model, such as a Lego model of a house,
    2. Mathematical or Digital model such as a simulation of a jet engine or a Digital Twin of the Human body.

Going by option 2, simulation can allow us to run tests whilst mitigating risk. No actual systems are being tampered with and if the model fails you can start again with no impact beyond the virtual realm.

Another reason for simulation would be the speed and ease of results. At the heart of Slingshot Simulations is the following phrase:

Rapid Prototyping of SMART Systems using Machine Learning and NO Coding

Simulations and Digital Twins bring value rapidly and cost-effectively, which is beneficial for time pressured environments and situations. If we were to present social distancing and crisis models for COVID-19 by 2021, this isn’t helpful and does little to protect people now. The need is now and useful solutions such as simulations offer the opportunity to deliver on these requirements in a timely manner.

SMART Decision Making

This brings us on to what Slingshot believes is one of the most important aspects of simulation: The facilitation of making SMART decisions. Here we deliberately refer to the well understood definition of SMART objectives or criteria:

  1. Specific or Strategic
  2. Measurable
  3. Achievable or Attainable
  4. Relevant, Realistic, or Results-based
  5. Time-bound or Testable

Simulation provides a tool for making and testing such decisions that in turn make the results trustworthy, reliable and accurate which is obviously crucial for decision makers. For example, a Digital Twin could be used as either:

  • A tool to experiment with various potential solutions given appropriate metrics to compare them against.
  • A model against which to test the real world for its behaviour, for example to see if a real system is behaving as expected.

Types of SMART Decision Making

“What-if” Analysis

What-if Analysis explores a given scenario to understand the impact of an event or sequence of events. For example if there was a fire, can firefights access every area of a building? Last year in July 2019 there was a terrifying example at Ocado’s warehouse in Andover, Hampshire which cost the company over £100m when it burned down. One of the primary challenges was that due to various errors by the time the firefights arrived they were unable to fully access the building, one of Ocado’s most automated centres

Scenario Comparison

Scenario Comparison is where 2 or more different scenarios are explicitly compared to observe the difference that a decision makes. For example, this could be a comparison of the various options for returning to work post-Covid and the implications on commuters patterns, congestion, and therefore pollution.

Multi-Objective Optimisation

Multi-Objective Optimisation is where a wide range of simulations are run to explore a series of possible solutions to try find the best one, or at least one of the best solutions. This typically involves a trade-off analysis along a Pareto front whereby there are multiple “best” solutions that compete across different parameters. For example, there is an obvious trade-off between the price of goods and the number of sales that impacts profits made where the ideal solution is somewhere along the “Pareto Front”:

The Challenges facing Simulation

Challenge 1: Complexity & Expertise

Given the amazing advances and benefits that lie within simulation can we simply add them to our next presentation in 30 minutes time with the tools available to us today?

The answer is unfortunately no!

Firstly if you have already got a dashboard solution connected up to your data where you can analyse and visualise that data to your hearts content, that will only take you so far, and trying to describe complex scenarios or do detailed timeseries analysis in spreadsheets is an artform of its own.

Secondly the simulation tools typically require you to either by an expert in the tool (often with quite a steep learning curve) or a programmer in either C or Java which excludes a huge amount of the working population from accessing the benefits of simulation.

Challenge 2: Democratisation & Accessibility

This leaves us with the final point of the Slingshot vision which is for simulation tools to be accessible, usable, and understandable by anyone! But what does this mean? This means that there cannot be a steep learning curve to using the tool. If you have a basic understanding of the underlying domain that you’re modelling, the aim is that you should be able to pick up the tool and use it almost immediately. That is not to say that there isn’t space for expert users who can do phenomenal things with the tools at their disposal.

However in order for these tools to be truly accessible they really do need to be accessible and should (where possible) not disadvantage anyone due to their age, language, or disability. At Slingshot we are taking this to the next level by exploring the intricate details of UX design down to things such as colour or typeface.

Finally, accessibility is unfortunately often driven by cost and simulation is typically restricted to those who can either afford the software licences or the salaries of the engineers who can use them. If we are to look at democratising Digital Twins in order to truly reap their benefits the price barrier has to be tackled even if it can’t be fully removed.


[i]  J. Banks; J. Carson; B. Nelson; D. Nicol (2001). Discrete-Event System Simulation. Prentice Hall. p. 3. ISBN 978-0-13-088702-3.