A simulation is a model that mimics the operation of an existing or proposed system, providing evidence for decision-making by being able to test different scenarios or process changes. This can be coupled with virtual reality technologies for a more immersive experience.
Simulations can be used to tune up performance, optimise a process, improve safety, testing theories, training staff and even for entertainment in video games! Scientifically modelling systems allows a user to gain an insight into the effects of different conditions and courses of action.
Simulation can also be used when the real system is inaccessible or too dangerous to assess or when a system is still in the design or theory stages.
Key to any simulation is the information that is used to build the simulation model and protocols for the verification and validation of models are still being researched and refined, particularly with regard to computer simulation.
Simulation works through the use of intuitive simulation software to create a visual mock-up of a process. This visual simulation should include details of timings, rules, resources and constraints, to accurately reflect the real-world process.
This can be applied to a range of scenarios, for example, you can model a supermarket and the likely behaviours of customers as they move around the shop as it becomes busier. This can inform decisions including staffing requirements, shop floor layout, and supply chain needs.
Another example would be a manufacturing environment where different parts of the line can be simulated to assess how their processes interact with those of others. This can provide an overview of how the entire system will perform in order to devise innovative methods to improve performance.
There are a range of advantages to be gained through the use of simulation, including:
Simulation is less expensive than real life experimentation. The potential costs of testing theories of real world systems can include those associated with changing to an untested process, hiring staff or even buying new equipment. Simulation allows you to test theories and avoid costly mistakes in real life.
A simulation allows you to test different theories and innovations time after time against the exact same circumstances. This means you can thoroughly test and compare different ideas without deviation.
A simulation can be created to let you see into the future by accurately modelling the impact of years of use in just a few seconds. This lets you see both short and long-term impacts so you can confidently make informed investment decisions now that can provide benefits years into the future.
The benefits of simulation are not only realised at the end of a project. Improvements can be integrated throughout an entire process by testing different theories.
A simulation can also be used to assess random events such as an unexpected staff absence or supply chain issues.
A simulation can take account of changing and non-standard distributions, rather than having to repeat only set parameters. For example, when simulating a supermarket you can input different types of customer who will move through the shop at different speeds. A young businesswoman who is picking up a sandwich will move through the shop differently from an old couple or a mother doing a weekly shop with two children in tow. By taking such changing parameters into account, a simulation can more accurately mimic the real world.
Even the process of designing a simulation and determining the different parameters can offer solutions. By thinking in-depth about a process or procedure it is possible to come up with solutions or innovations without even using the final simulation.
A visual simulation can also help improve buy-in from partners, associates and stakeholders. You can visually demonstrate the results of any process changes and how they were achieved, improving engagement with interested parties or even enabling a simulation based sales pitch.
While there are a great many advantages to using simulation, there are still some limitations when compared to other similar techniques and technologies, such as digital twin.
A digital twin expands on simulation to incorporate real time feedback and a flow of information between the virtual simulation and a real life asset or assets. The difference being that while a simulation is theoretical, a digital twin is actual.
Due to this, simulations have limitations when it comes to assessing actual real-world situations as they occur.
Simulation is used to evaluate the effect of process changes, new procedures and capital investment in equipment. Engineers can use simulation to assess the performance of an existing system or predict the performance of a planned system, comparing alternative solutions and designs.
Simulation is used as an alternative to testing theories and changes in the real world, which can be costly. Simulation can measure factors including system cycle times, throughput under different loads, resource utilisation, bottlenecks and choke points, storage needs, staffing requirements, effectiveness of scheduling and control systems.
Any system or process that has a flow of events can be simulated. As a general rule, if you can draw a flowchart of the process, you can simulate it. However, simulation is most effective when applied to processes or equipment that change over time, have variable factors or random inputs. For example, our supermarket from earlier has variable and random factors due to customer use times, requirements and stocks.
Using simulation to model complex and changeable dynamic systems can offer insights that are difficult to gain using other methods.
While simulation can be used to manage processes, procedures and assets, Swedish philosopher Nick Bostrom took the notion of simulation further in his 2003 paper, ‘Are You Living in a Computer Simulation?’ He argues that by adding artificial consciousness to simulations, you can blur the lines between reality and simulation, making it difficult to tell if you are living in reality or if you are living in a simulation. This simulation hypothesis argues that, should you become aware that your ‘reality’ was not actually ‘real,’ your memories could be edited by the simulation to once again make you blissfully unaware that you are not actually a real person in the real world!
Moving away from the realms of post-human simulation, let’s return to some ‘real world’ types of simulation…
Simulation can be broken down into three overarching types, as follows:
Modelling a system as it progresses through time, for example;
Modelling a system as it progresses through space, for example;
Modelling physical interactions between two or more systems, for example;
There are many examples of simulation across industry, entertainment, education, and more. Here are a few notable examples:
Simulation allows the characteristics of a real vehicle to be replicated in a virtual environment, so that the driver feels as if they are sitting in a real car. Different scenarios can be mimicked so that the driver has a fully immersive experience. These type of simulators can help train both new and experienced drivers, offering a route to teach driving skills that can reduce maintenance and fuel costs and ensure the safety of the drivers themselves.
Simulation can be applied to biomechanics to create models of human or animal anatomical structures in order to study their function and design medical treatments and devices. Biomechanics simulation can also be used to study sports performance, simulate surgical procedures, and assess joint loads. An additional example is neuromechanical simulation that unites neural network simulation with biomechanics to test hypotheses in a virtual environment.
Simulation can be used to design new cities and urban environments as well as to test how existing urban areas can evolve as a result of policy decisions. This includes city infrastructure and traffic flow among other potential models.
Simulations can assist with product design, allowing digital prototyping and testing to create better performing products with a shorter time-to-market, while also assessing the lifecycle of the finished product.
Simulations can replicate emergency situations, to help with disaster preparedness.This includes training and designing responses to events such as natural disasters, pandemics or terrorist attacks. Responses can be tracked and assessed through the simulation, highlighting potential problems and areas where more training may be required for responders, as well as ensuring any mistakes are made in a safe environment ahead of any real life event.
Economics, macroeconomics and finance also benefit from simulations. A mathematical model of the economy can, for example, be tested using historical data as a proxy for the actual economy. This can be used to assess inflation, unemployment, balance of trade and budgets.Elsewhere, simulations can replicate the stock exchange or be used to test financial models. Banks also use simulations to replicate payment and securities settlement systems.
Simulation is widely used for engineering systems to imitate operations and functions of equipment, processes and procedures. Engineering simulations can combine mathematical models and computer-assisted simulation for design or improvement of existing processes.
Simulation can be used to analyse virtual products and working environments incorporating an anthropometric virtual representation of the human, also known as a mannequin or Digital Human Model (DHM). These DHMs can mimic the performance and capabilities of humans in simulated environments. This type of simulation has applications ranging from assembly lines to disaster management and video gaming to waste collection.
Flight simulators have been used for years to train new pilots in a safe environment. This not only allows pilots to be assessed safely, but can also test instrument failures and other problems without risking the pilot, the instructor or the aircraft. You can also easily repeat the exact same scenarios, such as approaching a runway to land, under different conditions, not to mention saving fuel and other costs compared to actual flying time.
Much like flight simulation, it is also possible to simulate working in a ship or submarine. Simulators can include those that mimic the bridge, engine rooms, cargo handling bays, communications or remotely operated vehicles. These are used in training institutions, colleges and navies.
Sometimes referred to as ‘war games,’ military simulations can be used to test out military plans in a virtual environment using computer models. These can also incorporate social and political factors and are used by governments and military organisations around the world.
Simulations have been applied to network and distributed systems to test new algorithms and protocols before they are implemented in live systems. These can be applied to applications including content delivery networks, smart cities and the Internet of Things.
Simulation can be used for project management analysis and training purposes. Whether training managers or analysing the outcomes of different decisions, simulation is frequently conducted with software tools.
Robotics simulations are used to mimic situations that may not be possible to recreate and test in real life due to time, cost or other factors. The results of these tests can then be assessed and transferred to real life robots.
Production systems can be simulated using methods such as discrete event simulation to assess manufacturing processes, assembly times, machine set-up, and more.
Sales can be simulated to examine the flow of transactions and customer orders as well as costs, labour times and more.
The Kennedy Space Centre used simulation to train space shuttle engineers for launch operations. This would see people interact with a simulated shuttle and ground support equipment. Simulation is also used for satellite navigation tests.
Statistics are widely used as part of sport simulation to predict the outcome of events and the performance of individual sportspeople. Sports simulation can also be used to predict the outcome of games and events as well as for fantasy sports leagues. Biomechanics models can also be used to assist training, assess fatigue levels and their effect on performance and more.
Weather forecasting uses simulations based on past data to predict extreme weather conditions such as hurricanes or cyclones.
Simulations are used for a range of applications across industry, saving time and expense while being able to test theories and ideas before implementing them in the real world. Although related techniques such as digital twin may provide added benefits due to the two-way flow of information this allows, simulations still have a great many uses.
Whether testing theories, assessing procedural performance or determining the lifecycle of an asset simulation is a useful tool for many businesses and organisations.
1.1
What is simulationA simulation is an imitation of the dynamics of a real-world process or system over time. Although simulation could potentially still be done “by hand,” nowadays it almost always implicitly requires the use of a computer to create an artificial history of a system to draw inferences about its characteristics and workings.
The behavior of the system is studied by constructing a simulation model, which usually takes the form of a set of assumptions about the workings of the system. Once developed, a simulation model can be used for a variety of tasks, including:
Investigate the behaviour of the system under a wide array of scenarios. This is also often referred to as “what-if” analyses;
Changes to the system can be simulated before implementation to predict their impact in real-world;
During the design stage of a system, meaning while it is being built, simulation can be used to guide its construction.
Computer simulation has been used in a variety of domains, including manifacturing, health care, transport system, defense and management science, among many others.
1.1.1
A simple simulation modelSuppose we decided to open a donut shop and are unsure about how many employees to hire to sell donuts to costumers. The operations of our little shop is the real-world system whose behavior we want to understand. Given that the shop is not operating yet, only a simulation model can provide us with insights.
We could of course devise models of different complexities, but for now suppose that we are happy with a simple model where we have the following elements:
costumers that arrive at our shop at a particular rate;
employees (of a number to be given as input) that take a specific time to serve costumers.
Implicitly, we are completely disregarding the number of donuts available in our shop and assuming that we have an infinite availability of these. Of course, in a more complex simulation model we may want to also include this element to give a more realistic description of the system.
1.1.2
Why simulate?An alternative approach to computer simulation is direct experimentation. In the bagel shop setting, we could wait for the shop to open and observe its workings by having a different number of employees on different days. Considered against real experimentation, simulation has the following advantages:
It is cheaper to implement and does not require a disruption of the real-world system;
It is faster to implement and time can be compressed or expanded to allow for a speed-up or a slow-down of the system of interest;
It can be replicated multiple times and the workings of the systems can be observed a large number of times;
It is safe since it does not require an actual disruption of the system;
It is ethical and legal since it can implement changes in policies that would be unethical or illegal to do in real-world.
Another alternative is to use a mathematical model representing the system. However, it is often infeasible, if not impossible, to come up with an exact mathematical model which can faithfully represent the system under study.