System Dynamics Modelling in Engineering

The objective of an engineering analysis of a dynamic system is to predict its behaviour and performance. Real world dynamic systems are quite complex and often their exact representation and analysis is not possible. However, making simplifying assumptions, one can reduce the system model to an idealized version whose behaviour or performance approximates that of the real system. The process by which a real physical system is simplified to obtain a mathematically tractable situation is called the mathematical model or simply the model of the system. System dynamics deals with the mathematical modelling of dynamic systems in order to understand the dynamic nature of the system and improve system performance.

System dynamics evolved from the field of systems science and control engineering. This may give an impression that the field of control engineering is close in philosophy and practice to system dynamics and system dynamics modelling. However, there is a philosophical difference between control engineering and system dynamics; the concrete applications and practice of the two fields appear totally different. In most cases, control engineering is narrowly focused, whereas system dynamics are broad. In many cases effective communication between managers and modelers are essential to develop insights and implement system changes. Unfortunately, these participants in the modelling and analysis are often unable to understand or use mathematical models. System dynamics has a unique capability to overcome such constraints by its tools and methods, which are effective with non-technical participants, as well as experienced modelers.

Until now system dynamics has been applied in many complex social, environmental and engineering systems (Lee et al., 2006), to model their respective problems. Much of the early work of the group was concentrated in the field of production distribution system design pioneered by Forrester (1961).

System dynamics is used in the power generation industry to understand the signals that drive the installation of power generation capacity (Kadoya et al., 2005). Dyson and Chang (2005) and Shelley et al. (2001) applied system dynamics to solid waste management. Dyson and Chang (2005) also used it to develop a set of models for the prediction of solid waste generation in a fast-growing urban setting.

Hughes (1971) studied the planning problem of a manufacturer of an item for the Christmas market; because of the extreme seasonality of demand, the item had to be produced throughout the year. Barnett (1973) considered the problem of how an oil company should best develop a new oil field given that the initial development plans were based on rather inaccurate estimates of basic parameters such as field size. The Chemical Plant Investment cycle is one of a number of such cycles that is generally recognized and whose effects on supplying industry are quite marked. Hill (1972) constructed a preliminary model of the interactions between the chemical industry and the design contractors and hardware suppliers that arise through chemical investment. The first model of Coyle (1970) was an aggregate industry model designed to explore the possibility of copper producers stabilizing prices via their production and stockholding policies. It was found that the model of the existing production system gave rise to price instabilities similar to those observed in practice. In his second model (1972), Coyle examined the policies that an individual mining company might follow in order to survive and grow in existing unstable markets.

System dynamics is also applicable in the construction industry. The system dynamics based ‗change management system‘ of Lee et al. (2006) models the effects of construction errors and plan changes on project durations. Ogunlana et al. (2003) also used system dynamics to improve the organization and efficiency of large-scale, complex construction projects in developing countries.

The trade cycle has a marked impact on demand for paper products, and the amplitude of the cycle appears to increase as it moves up the chain from end user through the merchants to the bulk producer. The paper industry is a major importer of costly and scarce wood pulp. At the same time an increasing proportion of its raw material is drawn from recycled waste paper. A Department of Industry funded project used a system dynamics model to assess the impact of changes in technology on the industry and the changes in management policies required to obtain the maximum benefit from them (Price, 1975). System dynamics is also applicable in the shipping industry—disclosing, for example, that the orders for new ships show a very marked cycle with a pronounced boom, which generally lags increases in freight rates, followed by a long slump in which very few orders are placed (Taylor, 1976a; Taylor, 1976b; Raiswell, 1976). Thus, system dynamics models help to inform management in decision-making, in the capacity of what Ahmad and Simonovic (2006) term decision support systems. It may be mentioned at this point that the system dynamics is not just useful for resource management applications, but has also been applied to improve understanding of basic physical characteristics and processes.

According to Stave (2003: 304), the system dynamics approach is so broadly applicable because it clarifies the problem under study, the behaviour of the resulting model, and the real-world effects of potential solutions. The process of creating a simulation model helps clarify the resource management problem and makes modellers‘ assumptions about the way the system works explicit. Explaining the necessity of this kind of tool, Forrester (1987) pointed out that while people are good at observing the local structure of a system, they are not good at predicting how complex, interdependent systems will behave. Sehlke and Jacobson (2005: 722) explain that system dynamics models allow the user to conduct multi-scenario, multi-attribute analyses that resulting in relative comparisons over time of many alternative management strategies.

Essentially, system dynamics is most useful for engineering applications where the physical systems of interest are subject to strong social or economic influences (Xu et al., 2002). From the above discussion it can be concluded that system dynamics is useful in a variety of engineering fields: solid waste management, power generation, production, shipping, utility planning, design, construction, and mining industry.