Simudyne’s expertise is in the use of a consultative, systems thinking approach to facilitate the characterization of complex problems. These systems and interactions are then modeled using a combination of non-linear mathematics, a multi-discipline simulation platform and advanced visualization tools that combine geospatial data with simulation outputs. Our clients use our technologies and services to better understand the interactions in complex, interrelated systems and the robustness of positive interventions across a wide range of emerging future scenarios.

At the very highest levels of abstraction, all projects involve four critical steps.

1. The first step in the process is the creation of a hypothesis — a restatement of the problem which is often in the graphical format of data over time. Graphics succinctly capture the particular problem to be studied, and more importantly, what is not to be studied.

2. The hypothesis becomes a platform for the creation of a qualitative model. As the name suggests, the qualitative model doesn’t use numbers, but rather in words and pictures describes the underlying physics of the problem at hand. This is often in the form of process flow diagrams, causal diagrams, and simple graphic illustrations.

3. The qualitative model then becomes the blueprint for a quantitative model. We will use data and equations to “layer” onto the qualitative model to make it “come to life” analytically. The quantitative model will use visualisation to help increases its communicative powers for executives.

4. With the quantitative model complete, we will work with the team to conduct experiments. This is the model analysis stage of the project.  Analysis involves changing parameters in the model to reflect real world policy (replace supplier A with supplier B; relocate facility 37 to Bolivia, etc.).  Many experiments can be conducted in “real time” with the project team present – some may require off-line runs.  In any case, all simulation runs will be captured for recommendation support.

The need for a methodology to control the simulation development process emerged immediately with the first few major assignments. It was not, and is not, an effort to control the creative process of the developer, but to align that process with the expectations of the client, a client who is used to doing business a certain way as it relates to controlling software projects. The fact that a good methodology is all about collecting and using the best practices of the discipline to make development more efficient and effective is a beneficial, but secondary, objective.

Please contact us to request a copy of our white paper on Simudyne’s simulation development methodology.

We share our thoughts in the hope that such an effort will contribute to the ongoing dialogue on the issue amongst the clients and members of Simudyne. That dialogue will hopefully lead to a heightened awareness and understanding of the fundamentals of large scale simulation development projects, and, perhaps more importantly, of the standards and practices already in place in global corporations on the subject that, if we are lucky, we will have to deal with as the capabilities and popularity of the discipline gradually work their way into the “best practices” of critical decision makers in business.