The goal of every designer, administrator or computer system user is to achieve the highest level of performance using the least cost possible. Simulation modelling provides a means through which a designer can predict the system performance without having to perform the operation. By definition, simulation modelling is the process of developing and examining a digital prototype of the model in order to make predictions on its performance in the actual world. A digital prototype is a digital imitation of a given product which is used to test the form, fitness and function of that product. Simulation modelling uses a simulation software, which enables a user to analyse an operation by way of the simulation without necessarily doing the operation. Engineers, for example, can use simulation models to understand the strengths and weaknesses of a machine they plan to produce.
In order to better understand simulation modelling, one must familiarise with the components that constitute a simulation model i.e. simulation, model and system. To simulate means to manipulate a model so that it can operate on both time and space, thus, enabling one to understand the interactions that are usually not perceived because of how they are isolated in space or time. A model is a representation of a physical system at a specific space or time which gives insight on the real system. Both the simulation and modelling create a profound understanding of how a system or part of the system works: the system, in this case, is described as an entity that remains in existence as a result of the interactions of its components. Simulation in a computer involves execution or running of the computerized model by using a stimulating program.
Besides using simulation modelling to understand and increase the reliability and performance of a system, simulation can also be used to assess the correctness or accuracy of designs. For example, nearly all digital ICs (Integrated Circuits) produced today to go through extensive simulation before being put into production to ensure that they are free from errors. Importance of involving simulation on the early stages design development is that it helps designers avoid higher costs of repair, which increases progressively from the initial stage.
Simulation software is a set of programs which create a model of a real occurrence using several mathematical formulae. It is used to create a design of equipment so that the ultimate product will accurately resemble the design without the need for further modifications. Simulation of a system replicates the actual system e.g. the daily operations of a financial institution, and staff management in a security company or a hospital in a computer. Unlike in some models where experts are required to build mathematical models, simulation models allow non-experts to model and make an analysis of how the simulation system is operating on a real-time basis.
Types of Simulation Programs
- Autodesk Simulation Machine (ASM)
- Autodesk Simulation CFD
- Autodesk Inventor Professional
- Solidwork Simulation
Application of Simulation Models
Simulation software with the attribute of giving responses has been designed and used in gaming. Other industries are also tapping into this technology e.g. Airlines, nuclear energy and chemical plants. These fields require optimal operating conditions in order to avoid unnecessary and costly operations. Operators in these areas conduct a simulation by connecting the real control panel to the simulation of the physical response, which operates on a real-time basis. The mock-up provides valuable information and enables the operators to analyse the operations without fearing negative results.
The military has also embarked on simulation in their combat training. In this scenario, the military develops what is called ‘virtual environments’ where they conduct their training. Such dynamic environments allow soldiers to interact with the actual battlefield as though they were there. The main advantage of this training technique is that it saves on costs that would have incurred by using real combat crafts and tanks.
Researchers have advanced simulation to include virtually everything e.g. Weather conditions, the behaviour of the power system, biological processes, mechatronics, heat pumps, chemical reactions among others. The theory behind the simulation is that anything that can be expressed as mathematical data or equation has the capacity to be simulated into a computer. The greatest challenge in simulation modelling is that some systems, especially natural systems, have almost infinite influences. The task that arises, in this case, is determining the most vital factor which affects the objectives of the simulation.
Stages in Simulation Modelling
Stages in modelling and simulation can be better understood by considering the following situation. A bank is in need of building a new switch for ATM networks, which has recently appeared in the market. For the bank to succeed in a highly competitive market using the new product, the designer must come up with a switch that gives both high-level performance and relatively constant cost of manufacturing. The designer must, therefore, consider several factors which include the amount of memory needed and the hardware organization in the switch. He or she must, therefore, develop models to test these factors in order to come up with the best.
Computerized models are run in a computer using simulation software. The process starts by developing a model which is then simulated and its performance studied. The following are the stages involved in simulation modelling:
- determine the objectives and goals to be met
- draft a rough estimate or plan of the simulation
- develop simulation models
- perform analysis of simulation
- implement necessary changes on your mock-up system
- make an assessment of the results. The interactions of the components are extensively studied until one has gained sufficient knowledge of how the system under the study works.
Simulation and modelling from the above stages can be described as both a science and an art. For example, one can learn how to drive a car by reading a book. However, in order to actually learn how to drive, one must apply the knowledge read and use it actively with the vehicle. This is the same reality that simulation modelling follows. One can learn how to model and simulate a system from a book and through discussions. But the skill to develop the models and simulate them can only be acquired by actually being involved in the stages outlined above. In addition one must realise that simulation modelling and, therefore, one cannot learn everything at once: learning takes place gradually.
Complexity of Systems
The complexity of systems, as described by Birtza and Arbex (2007) can be divided into two categories, dynamic and detailed complexities. A dynamic complexity system has cause and effect, which are separated by either time or space. Dynamic complexity poses a great challenge in modelling and simulating because one has to understand how the components of a system are connected and interact. A detailed complexity, on the other hand, represents a system which has many components. Importance of simulation such kinds of systems can be explained using an example. The example below was developed and used by I see Systems.
A company has 120 employees. 60 of these employees are threshold workers while the other 60 are professionals. The company wants to maintain the total number of employees at 120 and therefore must replace an addition rookie in case professional quits. The current trend in the company is that 10 professionals quit every month and it takes about 6 months to develop a single professional from a novice. In addition, threshold workers receive a monthly salary of $10,000 as compared to professionals who are paid $15,000. A model for such a scenario would look like this:
The iThink Model
One would realize the company revenue in the 10th month declined from $1.5 million per month to $1.35 million per month, which is quite puzzling. In order to find where the problem came from, the company would start its search from the 10th month. According to the result, at around this time, the company still had 120 employees but of this number only 30 were professionals with the rest being threshold workers. Further analysis reveals that because of the company policy change within a period of three months, professionals became more annoyed, which in turn increased the rate of quitting of professional from 10 to 15 employees per month. Since the system is automated, it replaced each professional that left with a threshold worker.
Advantages of Simulation Modelling
The first benefit of using the simulation model is that it enables developers to achieve a compression of time and space in studying interactions of a system. The results obtained would not be achieved in a default situation because the interactions involved are spaced out in terms of time and space. The other advantage is that simulation modelling can be used where mathematical models would be rendered ineffective e.g. it would be tedious to use mathematical equations in complex systems. Simulation modelling is also very important in optimizing conditions for a certain operation, thus, minimizing unnecessary costs.
Models can bring out the ignorance of a developer. By simulating the model, a developer can realize that the design of the model had serious errors, or it can bring a better understanding of how systems work, which quite often is dissimilar to common sense. Understanding a system better through design yields a competent product that is free from errors or system that is not prone to failures.
Disadvantages of Simulation Modelling
Simulation modelling is not an ideal technology and is, thus, prone to several weaknesses. Although, it might help save costs greatly, developing a model and analysing it can be very expensive. The functionality of the model requires specialized computer programs and computer systems. In addition to the expenses involved, the simulation may also cause significant delay. A lot of time is needed to build it, thus, implicating that it might not be developed on time. Another weakness of simulation as compared to mathematical models is that it cannot be relied upon to give optimal solutions.
In conclusion, determining whether a simulation is good or bad depends on the design of the model used. The fact that models are simple representations of the real situation means that it is not possible to accurately include all the details. Including very few details may result in a model that fails to capture significant interaction, hence, provide a poor understanding of a system. On the other hand, too many details result in a complicated model which is not only difficult to run but also difficult to understand. Therefore, to develop a good simulation a designer must come up with a model that strikes a balance between little and too many details i.e. a moderately detailed model will be able to promote extensive understanding of how a system works.