Solutions

Simulation

Simulation is a technique to analyze complex systems based on actual data.
Simulation can help in Decision support in the following areas:

• Strategic planning
• Logistics
• Sales and marketing planning

Simulation models help the management team to get a better understanding of the problems they have to deal with. Simulation is capable to handle processes with uncertainties and feedbacks. Management teams often make wrong decisions analyzing such complex situations and systems because mental or spreadsheet models usually fail to provide correct results.
If a company is experiencing a serious threat to its survival, be it declining market shares or disagreements with the labor union, resources are mobilized to deal with the problem. The company might already be divided into "parts", such as the accounting department, the sales department, and so on. Problems affecting the entire company are often blamed on a department, as when a loss in market shares causes executives to target the sales department for investigation or punishment. The reason for the problem might seem obvious. The company must be losing market shares because the salespeople are not selling the product. What is often lost in the picture is the fact that the sales department depends on many other departments to do its job. Deficiencies may be in any or all of them. Perhaps the management information services department has not provided the salespeople with the computer support they need. Or maybe manufacturing has been suffering from poor scheduling of orders and a backlog has developed. This will in turn make it harder for the salespeople to sell the product to customers who want an immediate delivery. A number of factors may be the cause of the problem, which may come to light only when the interactions among all parts of the corporation, and not just the parts themselves, are examined.

Simulation overview

Almost all of our business processes are Complex systems with feedback loops and material or information flows that can vary over time and have a uncertain behavior - their quantities can only be described with statistical variables. The only efficient method to analyze and predict these systems is simulation.
You can ask that could we survive without simulation so far. Good question.
Management teams need to make decisions regarding these business processes and they do this without using simulation. The difference between the two approaches is the efficiency and the quality of the analysis and decisions. Without using simulations they can look at a system as a black box. They have some experiences - if this is an existing business process - and they have a lot of data. The data describes system behavior at a certain - actual - state of the system. If they need to make decisions to alter the system or predict its outcome they need to try to predict the results using this black box approach. The most common way to do this is using spreadsheet models. The spreadsheet models are static - they cannot handle time (delays or other time related behavior) and feedbacks. When outcome is calculated management teams need to create a mental model and come up with a formula to calculate the outcomes of the altered system.
Sometimes this works, but most of the times the mental model fails, because the human mind is not capable to visualize exponential relationships and time delays well. All of the feedback loops in our business processes however have this type of behavior.
Simulation models are created graphically. This is a perfect way to show the structure of the system - it is not hidden in spreadsheet formulas.
Simulation models are created based on cause - effect analysis. The management team creates a cause - effect diagram based on the system they try model.

System dynamics

Our simulation models are based on system dynamics. System dynamics is a computer-based simulation modeling methodology developed at the Massachusetts Institute of Technology (MIT) in the 1950s as a tool for managers to analyze complex problems.
The word "dynamic" implies continuous change and that is what dynamic systems do - they continuously change over time. Their position, or state, is not the same today as it was yesterday and tomorrow it would have changed yet again.
Using system dynamics simulations allows us to see not just events, but also patterns of behavior over time. The behavior of a system often arises out of the structure of the system itself, and behavior usually changes over time. Sometimes the simulation looks backward, to historical results. At other times it looks forward into the future, to predict possible future results.
Understanding patterns of behavior, instead of focusing on day-to-day events, can offer a radical change in perspective. It shows how a system's own structure is the cause of its successes and failures. This structure is represented by a series of causally linked relationships. The implication is that decisions made within an organization have consequences, some of which are intentional and some are not. Some of these consequences will be seen immediately while others might not be seen for several years.
System dynamics simulations are good at communicating not just what might happen, but also why. This is because system dynamics simulations are designed to correspond to what is, or might be happening, in the real world.

Simulation models

The two most important elements of a continuous simulation model are the levels and the flows.
Levels accumulate their value over time of the simulation. Their value is changed by the flows running in and out of them. Levels are used to represent the status of the system (like the inventory or bank account balance).
Flows represent time delays in the system. Parameter values controlling the flow (flow rate) are changed based on a level and they therefore change the level connected to them in the next time step. This way flows are used as information links in the model. Parameters are calculated once in every time step and they determine the Flow rate. They have no memory - unlike levels.