The simulation, by replicating in vitro complex phenomena, is the ideal tool that companies use to plan, evaluate the risks and for decision-making in order to assess strategic options.
Microsoft Excel is certainly the most used tool by companies because allows you to find analytical solutions to business problems.
This is true if:
- the number of parameters that influence the system is small;
- the system has a linear behavior;
- dependencies between variables are clear and the mental model is simple.
Fair Dynamics help the customer choose the most appropriate methodology in relation to the addressed problem.
There is a fourth method called "hybrid" or "multi-method" that, by allowing any combination of the three methods mentioned above, enables the creation of models very close to reality.
This unique feature of AnyLogic development platform allows modelers to focus on the business problem rather than to think up techniques to work-around aspects or details that will lead to paradigms unsuitable to represent the aspects themselves ("heroic" hypothesis).
Our consultants, in collaboration with qualified international partners, implement software models by using some of the best simulation tools available on the market.
The purpose of the simulation is to reproduce, in a controlled environment, the dynamics and complexity of real systems. Simulation highlights the interdependencies among the different components of the system, shows its evolution over time, gives the possibility to monitor the parameters that influence it by providing performance indicators.
This is possible through the use of software tools that allow modelling complex systems and quickly exploring possible future developments in respect of possible changes (decision-making and / or external events).
The benefits of using these techniques are many, but the main ones are:
- reduction of costs and risks - experiments carried out directly in the reality can be very expensive and the analysis of "what if" simulation allows a careful analysis of risk and a reduction in costs associated with decision mistakes;
- study of non-linear and complex phenomena - through simulation models it's possible to study and reproduce non-linear phenomena otherwise difficult to evaluate through the use of other analytical tools (eg Microsoft Excel); the simulation is therefore a valuable tool to dominate the complexity;
- the value of time - running models into 'a time frame'; the consequent "real time" evolution of performance indicators allows a unique tool that help understand the addressed problems.
There are several methodologies related to the simulation of complex business processes (without easily searchable analytical solutions), each of which is suitable to analyze problems based on different levels of abstraction.
The three major methodologies we use are:
- System dynamics
- Agent based modeling
- Discrete event modeling
But what if we are dealing with a problem in which:
- there are many parameters or too many combinations of scenarios that affect the system;
- there are time dependencies or causal feedback loops between the variables (non-linear behavior);
- the system behavior is not as intuitive or immediate;
- we are in presence of uncertainty (stochastic system).