SEMS probably is the only simulation and modeling lab in Indonesia that has the complete modeling and simulation tools with state of the art hardware to conduct all types of modeling methods and simulation.

Our Supporting Software

Plant Simulation

Tecnomatix Plant Simulation is a discrete event simulation tool that helps to create digital models of logistic systems (e.g., production), so that we can explore the systems’ characteristics and optimize its performance. These digital models allow us to run experiments and what-if scenarios without disturbing existing production systems or – when used in the planning process – long before the real production systems are installed. Extensive analysis tools, such as bottleneck analysis, statistics and charts let us evaluate different manufacturing scenarios. The results provide uswith the information needed to make fast, reliable decisions in the early stages of production planning.

Using Plant Simulation, you can model and simulate production systems and their processes. In addition, we can optimize material flow, resource utilization and logistics for all levels of plant planning from global production facilities, through local plants, to specific lines.

Powersim as a Tool for System Dynamics Modeling

Written by Professor Pål I. Davidsen.

All human beings live in a complex, dynamic reality of natural and social systems. Most systems are not in equilibrium and their states change continuously. In order to understand, adapt to, and control this reality, methods must be developed to capture it in formal models. Powersim is a major contribution to the set of tools that facilitate the study of dynamic systems.

Modeling reality

The mental models of decision makers cannot process the variety and complexity of most of the systems experiencing problems. With the use of computers, however, it is possible to explicitly represent, combine, and formalize these models, and communicate their assumptions to laymen, students, colleagues, and policy designers who will subject them to constructive criticism.

Simulation models, in particular, can be used to investigate the intimate relationship that exists between the structure and behavior of dynamic systems. That is, how problematic behavior arises from the underlying structure of a system and how this structure can be modified to alleviate system’s problems.

Method developed at MIT

In the late 1950s, Professor Jay W. Forrester of the Sloan School of Management at the Massachusetts Institute of Technology, developed the system dynamics method for this purpose. Since then, this method has been applied to a wide variety of issues and problems in both the public and private sectors. Large corporations and governmental agencies make use of the insights gained from building system dynamics models in their design of policies and strategies, and in their tactical and operative decision making.

Powersim is a major contribution to the set of tools that facilitate the study of dynamic systems. It is an excellent MS Windows-based software package that makes possible the formulation of simulation models in the graphical notation originally developed by Professor Forrester. (Editors note: The first simulation tool was named Powersim. The product referred to is now replaced by Powersim Studio.)

Experimental approach

In order to identify the underlying causes of real problems, realistic models must be developed, analyzed and modified experimentally.

In the past, policy makers have spent a lot of time and effort developing and teaching effective ways to analyze relatively simple models. In particular, they have been concerned with identifying their equilibrium conditions.

Less time and effort have been spent on developing tools that can be used to represent systems in dynamic transition between equilibria never attained.

Within the system dynamics paradigm, emphasis is placed on model conceptualization and on the utilization of a wide spectrum of criteria for model validity that helps ensure that the resulting models correspond to real systems — structurally as well as behaviorally.

“Better to be almost right than exactly wrong”

The emphasis on realism has two implications. First, it implies that models must reflect the complexity of real systems. Although model simplicity is a virtue, complexity is often a necessity. Second, it implies that optimal solutions in many cases cannot be found in closed form, and that simulation techniques and the experimental identification of realistic solutions must be utilized. As a wise person once noted: “it is better to be almost right than exactly wrong”.

Properties of complex systems

Complexity defies human intuition. In particular, there are four kinds of structural properties that humans find cognitively challenging in dynamic systems.

First, there is the origin of dynamic behavior itself – the relationship between flows and levels. Levels accumulate flows and flows cause the levels of levels to change over time. Although simple in principle, humans often find it difficult to distinguish between real levels and flows and to identify the behavioral consequences of flows acting on levels.

Second, there are delays or lags in actual systems. Delays distribute the effects of changes in variables throughout a system over time and often cause information to arrive at its destination in an untimely, and hence harmful, manner. Delays and lags lead humans to discover and give priority to short-run gains and to ignore and postpone actions against future losses. Delayed reactions typically cause systems to over and undershoot and thus to exhibit oscillatory behavior.

Third, there is feedback. Real world systems are usually characterized by circular causality. Their structures contain feedback loops that transmit the dynamic behavior of one attribute to the next until the circle is closed and the signal, in a modified form, is fed back to its origin. Such loops have a tendency to stabilize or to destabilize a system. When humans try to manage a feedback system, their actions are typically amplified or counteracted, depending on which feedback structure are dominating the system at the time.

Finally there are non-linearities. Nonlinearity implies that system attributes influence each other in a non-proportional way and that they interact so that their partial effects, playing out over time, cannot easily be distinguished. Such interactions may cause shifts in the structural dominance of a system over time. That is, substructures that have dominated a system’s behavior for some time may, suddenly or gradually, loose their influence while other substructures gain influence. This typically causes a dramatic modification of the system’s dynamic behavior.

These characteristics of complex systems tend to mask the relationship between cause and effect, and thus to obscure current problems and hide the means of solving them. Successful solutions are thus often counterintuitive and hard to identify. In addition, there are often uncertainties associated with system, and humans tend to state their perceptions, policies, preferences and attitudes vaguely. As a result, realistic models of systems require the use of both random and fuzzy numbers in simulations.

Supports constructivism

System dynamics supports constructivism. It facilitates the building of microworlds that allow the study of how complex patterns of behavior arise when micro-structures, exhibiting simple behavior patterns in isolation, are connected. Conversely, it facilitates the analysis of the structures underlying complex behavior patterns. Powersim, as a system dynamics tool, is useful for investigating the relationship between micro and macro structure as well as behavior.

Transfer of knowledge

The system dynamics method enables decision makers to use their experience. When people learn from studying one dynamic system they can often transfer their knowledge to other, similar systems. The symbols utilized to create systems dynamics models are quite general. As a result, system dynamics models are often able to generate insights across disciplines. Such generalized models are called generic structures, and their associated behavior is generic as well. The exponential growth or goal seeking behavior arising from simple first order positive and negative feedback loops, the S-shaped pattern created by a shift in dominance between positive and negative loops, and the oscillations typically resulting from lags in negative feedback loops, are simple examples of generic structures and behaviors. Knowledge of such structures and behaviors can be applied to many specific systems, under many specific circumstances.

Powersim is particularly useful at utilizing generic models. These models can be stored in a library from which they can be copied, modified, and incorporated as co-models or integrated (pasted) as sub-models, in a larger “main” model.

The ability of Powersim to describe and solve problems, however, suggests that its real benefit arises from its use in the model building process itself, rather than from its ability to simulate a particular model. As a result, the people who both know the system experiencing the problem, and are charged with implementing model-based results, should participate fully in the modeling process. Their participation increases the likelihood that they will believe the model they helped to create and will implement its results. Powersim’s graphical user interface greatly reduces the barriers to the participation of policy makers in the modeling process.

Improving dynamic intuition

Experience tells us that the dynamic intuition of humans is poorly developed. When presented with simple dynamics problems, humans tend not to take feedback, delays, non-linearities, and uncertainties appropriately into account. This is mainly because the training decision makers traditionally receive is inadequate for dynamic thinking. Three conclusions can be drawn from this:

First, students must be trained from an early age to develop a fundamental understanding of complex, dynamic systems. The founder of the field of system dynamics, Professor Forrester, devotes much of his time to the System Dynamics in Education Project at MIT, aimed at developing educational material for students and teachers in American high-schools. Realizing the importance of establishing a dynamic intuition among young people, the Norwegian Ministry of Education have included dynamic modeling as a central part of the new informatics course for Norwegian high schools.

Models are undeniably abstract, and do not always aid in the effective communication of system insight or problem solutions. Recently, however, microworlds or, more specifically, management flight simulators with capturing interfaces, have been developed to create the requisite engagement in complex systems. Although apparently successful, many questions regarding the educational benefits from these simulators remain unanswered.

Although such simulators may serve to trigger an interest in a problem, they typically emphasize behavior at the cost of providing a deeper understanding of the structural origin of that behavior. By making full use of the MS Windows interface, Powersim facilitates the creation of spectacular management flight simulators. In addition, any user of a flight simulator, can gain access to the underlying model structure, and, when necessary, modify that structure.

This illustrates how Powersim aids in both decision making, and policy design that requires a structural understanding and structural modifications to a system.

How are decisions made?

The questions arising from the utilization of microworlds in management training are currently under intense scientific investigation.

• How are decisions made in a complex, dynamic environment?
• What information is taken into consideration?
• How is that information transformed into a decision, and how are decisions being implemented?
• What circumstances affect this process?
• Which system properties pose the major challenges to the decision maker?

Policy design

These types of questions must be answered in the process of designing appropriate policies. For that purpose, policy makers are exposed to a management flight simulator. Traditionally, these simulators have not allowed the policy maker to experiment with the models underlying their structures (i.e., they have not allowed the policy maker to alter their structures as they would do in real life).

Powersim has been developed so that participants in a management game can simulate off-line while designing their own strategies. Combined with constrained or free access to the underlying model, this allows them to investigate a wide spectrum of research topics in decision and management sciences.

Powersim important for the growth of system dynamics

Managers, scientists, teachers, and students should be highly satisfied with the successful development of Powersim. It will be of major importance to the growth of the field of system dynamics and to the utilization of system dynamics as a policy design method in complex, dynamic environments.

Pål I. Davidsen is a professor in system dynamics at the Department of Information Science at the University of Bergen

ProModel

ProModel is a powerful, Windows-based simulation tool for simulating and analyzing production systems of all types and sizes. ProModel provides the perfect combination of ease-of-use and complete flexibility and power for modeling nearly any situation, and its realistic animation capabilities makes simulation come to life. ProModel provides engineers and managers the opportunity to test new ideas for system design or improvement before committing the time and resources necessary to build or alter the actual system. ProModel focuses on issues such as resource utilization, production capacity, productivity, and inventory levels. By modeling the important elements of a production system such as resource utilization, system capacity, and production schedules, you can experiment with different operating strategies and designs to achieve the best results. As a discrete event simulator, ProModel is intended primarily for modeling discrete part manufacturing systems, although process industries can be modeled by converting bulk material into discrete units such as gallons or barrels. In addition, ProModel is designed to model systems where system events occur mainly at definite points in time. Time resolution is controllable and ranges from .01 hours to .00001 seconds.

• Typical applications for using ProModel include:

•  Assembly lines

•  Job Shops

•  Transfer lines

•  JIT and KANBAN systems

•  Flexible Manufacturing systems

•  Supply chains & logistics

Use of ProModel requires only a brief orientation and virtually no programming skills. With ProModel’s convenient modeling constructs and graphical user interface, model building is quick and easy. All you do is define how your particular system operates, mostly through part flow and operation logic. Automatic error and consistency checking help ensure that each model is complete prior to simulation. During simulation, an animated representation of the system appears on the screen. After the simulation, performance measures such as resource utilization, productivity and inventory levels are tabulated and may be graphed for evaluation.

Our Supporting Hardware

Workstation