2.3 System Dynamics
2.3.1 System Dynamics defined
The systems approach or thinking originated in the physical sciences where it challenged the prevailing norms by considering instability, non-linearity, discontinuity and chaotic behaviour (Mingers & White, 2010). The fundamental principle in system dynamics states that the structure of the system gives rise to its behaviour (Sterman, 2000). This is due to the feedback loops and relationships that inherently exist between variables and within a system. Systems thinking generally include the following:
• Viewing the situation as a set of diverse interacting elements within a holistic environment.
• Recognises that the relationships or interactions between elements are more important than the elements themselves in determining the behaviour of the system.
• Acknowledges that different levels of hierarchy exist and causality exists both within and between levels.
• Accepting, especially in social systems that people will act in accordance with differing purposes or rationalities.
System dynamics modeling is essentially a digital computer aided approach for mapping managers’ mental models of their system. This is converted into a simulation model to facilitate what-if experimentation that facilitates experiential learning. Simulation and simulation software has the functionality to evaluate variations, interdependencies, capture a greater level of detail than conventional modeling techniques as well as capture specific qualitative aspects (Azadeh, Layegh,
& Pourankooh, 2010). Information collection during this process can be acquired from three sources viz mental, written and numerical databases. However, the key source of information is from the mental database with the content of information decreasing as one goes to from the mental, to written to numerical databases as illustrated in Figure 2.1 below. Qualitative data collected will be transformed into a format relevant for use in the software specified.
30 Figure 2.1: Decreasing information content in moving from mental to written to numerical databases.
Forrester, J.W. (1986). Lessons from system dynamics modelling. The 1986 International Conference of the System Dynamics Society. Sevilla, October, pg9.
It is clear from the above diagram that a high reliance is placed on the mental database for inputs in the model building process. Lune-Reyes & Andersen (2003) stated that qualitative analysis when done properly brings a high level of rigor and robustness to the model built.
System dynamics is a tool in today’s high pressure environment where there is a tendency to look at solutions with short-term benefits only. It seeks to evaluate the impact of not only individual decisions or policies but a combination of one or more decisions and policies. Lyneis, Cooper &
Els (2001) stated that if the consequences of individuals actions or decisions were summed up it would be less than the actual impact seen post the implementation of all actions and decisions. In other words, the sum of the individual changes and their corresponding impact is less than the actual impact experienced.
System dynamics seeks to capture the views and perspectives of individuals, develop an overview, share the big picture and thereby try to anticipate the consequences of decisions. This is done via the development and use of a model. A model is a physical representation of the real world and an
31 aid to imagination and learning, a transitional object to assist individuals to make better sense of a partially understood problem (Morecroft, 2010). System dynamics is about feedback systems thinking, which breaks down silo thinking and narrow functional perspectives. System dynamics also models the interplay of the various feedback processes (Morecroft, 2010). Feedback systems’
thinking is different from event oriented thinking because it strives for solutions that are
“sympathetic” with their organisational and social environments. Solutions are not implemented in a vacuum and consideration is given to short and long term consequences. System dynamics highlights that using this approach gives thought to further factors by showing that often there is more going on then meets the eye (Morecroft, 2010).
Richardson, (2011) defines system dynamics as the mental effort to uncover endogenous sources of system behaviour. System dynamics is the use of informal maps and formal models with computer simulation to uncover and understand endogenous sources of system behaviour. System dynamics practitioners use system thinking, management insight and computer simulation to:
• Hypothesis, test and refine endogenous explanations of system change
• Use these explanations to guide decision and policy makers/making.
System dynamics is an approach that is able to compensate and repair some of the shortcomings seen in typical quantitative models. System dynamics models takes into consideration delays, bounded rationality and goal setting. Setting of model boundaries is important and system dynamics considers most factors as endogenous whilst other approaches consider key factors such as customer demand as exogenous (Akkermans & Dellaert, 2005). Whilst the external environment does contribute to demand fluctuations, internal policies, decisions and behaviour also contribute towards creating this imbalance between supply and demand. System dynamics states that the boundary of the model needs to be determined in a manner in which exogenous factors are included within the model boundaries. This therefore transforms exogenous factors into endogenous factors (Morecroft, 2010).
Compared with the more common approach of discrete event simulation, which inevitably models a system in operational detail such as every single machine, the system dynamics approach provides a means of modelling at a higher aggregated level, which results in efficient and effective modelling and time savings (Lin, Baines, O'Kane & Link, 1998). There is an erroneous assumption that the dynamics of the problem/system can be attributed to exogenous events which
32 results in individuals not looking at the true root cause and hence not identifying the true potential for improvements. They therefore do not identify the critical leverage points that will yield the most sustainable results.
System dynamics lends itself to the development of simple causal loop diagrams, which encapsulates a portion of the business in which systemic feedback loops, systemic delays and unintended consequences are evident and highlights the real business dynamics that should be considered. Traditional simulation models are discrete-event simulation and do not take into account the hidden dynamics of a problem (Ashayeri & Lemmes, 2005). System dynamics models help to organise information in a more understandable way and link the past condition into the present one and extend the present into future alternatives through scenario development (Suryani, Chou, Hartono & Chen, 2010).
Rather then predict the future, system dynamics models tell a consistent future story of the system based on the structure as provided by managers (Cagliano, DeMarco, Rafele, & Volpe, 2010).
Whilst the model is mathematical in nature, the key data that is used is qualitative in nature (Lune- Reyes & Andersen, 2003). This relates to the required data originating primarily from either the mental or the written databases. Computer based modeling makes the process of modeling simpler.
The difference between the mental model and the properly conceived computer model is the ability of the computer model to determine the dynamic consequences when the assumptions within the model interact with each other (Forrester, 1971). System dynamics seeks to take the separate parts of the social system and to combine them into a computer model and to learn the consequences.
Richardson (1999) lists four areas that system dynamics looks at to achieve the required outputs:
• Computer technology
• Computer simulation
• Strategic decision making
• Feedback thinking
Computer models are sometimes based on methodologies for obtaining input data that commits the model to omitting major concepts and relationships in the psychological and human areas that is crucial in modeling social systems. With regards to computer models, the key is not to computerize the model but to have a model structure and relationship, which represents the system that is being considered. This model is a statement of the system structure (Forrester, 1971).
33 System dynamics is well equipped to model social systems and the problems that are experienced.
Forrester, (1971) listed the characteristics of social systems as:
• Social systems are insensitive to most policy changes that people select in an effort to alter the behaviour of the system.
• Social systems all seem to have a few sensitive influence points through which the behaviour of the system can be changed
• There is usually a fundamental conflict between the short term and long term consequences of a policy change. A policy which produces an improvement in the short run (within 5 to 10 years) is usually one that degrades the system in the long run (beyond ten years)
System dynamics is able to represent the real world. It can accept the complexity, non-linearity and feedback loop structures that are inherent in social and physical systems (Forrester, 1994).
Systems thinking uses causal loop diagrams (CLD) and stock-flow diagrams to enable understanding of the problem being studied within a particular environment. They highlight the relationships and interactions between the various variables. A CLD is a visual representation of how different variables are interrelated. In order to understand the structure of a system at a more detailed level a different technique is required to create the system and allow us to explore it. The stock-flow diagrams allows the practitioner to do this by visually representing the system together with the underlying mathematical equations (Marquez, 2010). In system dynamics a causally closed system is one in which the causes creating the behaviour of interest lie within the system and are known as endogenous factors (Forrester, 1994). The beer game, which is a well-known feedback based management game, can display all the typical behaviours of a coupler system.
This is a common example of system dynamics in action (Mingers & White, 2010).
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