Chapter 2: Literature Review
“If someone were to analyze current notions and fashionable catchwords, he would find “systems” high on the list. The concept has pervaded all fields of science and penetrated into popular thinking, jargon and mass media.” (Bertalanffy, 1973, pp. 3)
The economic boom and industrial development after World War II led to the necessity of “Systems Thinking” and “Systems Approach”. The new technologies that were being developed involved interaction between heterogeneous technologies such as mechanical, electrical, chemical and physical. In addition to these interactions, the relationship between man and machine has continued to play an important role in the development of new technologies. Various financial, economical, social and political problems started dominating this development.
All of these factors, due to their interaction with each other, created a complex set, or a system. This led to an approach where the system is seen as whole rather than just considering its unit components. This approach is a “systems approach”. The systems approach aims to integrate various natural and social sciences within its frameworks. This systems thinking and systems approach are the by products of General Systems Theory. Bertalanffy (1973) describes the origins of the general systems theory and then how it was evolved and applied to various engineering and economic disciplines.
General System Theory
The term “General Systems Theory” was introduced by Ludwig von Bertalanffy (1973) when he was carrying out his biological research on living organisms. He felt a necessity to study the organisms not only as isolated parts but also to account for their interaction when studied within the whole. He proposed that this “organism-as-a-whole” (Bertalanffy, 1973) approach can be used in various other fields including engineering, social, physical and science fields.
The concepts of general systems theory were applied to a number of fields. The approach of systems thinking was seen widely applicable in industrial engineering and management. Industrial Dynamics was proposed by Jay W. Forrester (1958) when he was studying management problems in corporate settings. He was especially concerned with the problem of fluctuations in factory production that was inconsistent with corporate growth and declining market share.
He developed his theory of Industrial Dynamics. In his paper, “Industrial Dynamics: A Major Breakthrough for Decision Makers” (Forrester, 1958), he states that the success of an industrial company depends on the interaction between the flows of information, materials, money, manpower and capital equipment. The interactions among these factors where one amplifies the behavior of the other and causes fluctuations, forms a basis for understanding the system structure and for anticipating the outcome of decisions, policies and investment choices.
In his book Industrial Dynamics (Forrester 1961), he describes the following industrial dynamics approach so as to design more effective industrial and economic systems.
- Identify a problem.
- Isolate the factors that appear to interact and to create the observed symptoms.
- Trace the cause-and-effect information feedback loops that link decisions to actions that result in information changes and to new decisions.
- Formulate acceptable formal decision policies that describe how decisions result from the available information streams.
- Construct a mathematical model of the decision policies, information sources, and interactions among the system components.
- Generate the behavior through time of the system as described by the model and compare results against all pertinent available knowledge about the actual system.
- Revise the model until it is acceptable as a representation of the actual system.
- Redesign, within the model, the organizational relationships and policies that can be altered in the actual system to find the changes that potentially will improve system behavior
- Alter the real system in the directions that the model experimentation has shown so as to lead to improved performance.
Industrial dynamics was used to study and solve problems related with management in industry. But then it was used to address problems in various disciplines such as urban planning, economics, traffic engineering and medicine. The term Industrial Dynamics was transformed into a new more general term entitled “System Dynamics”.
Fundamentals of System Dynamics
“System Dynamics is a methodology for understanding certain kinds of complex problems.” (Richardson and Pugh, 1981, pp. 1)
By saying “certain kinds of complex problems”, Richardson and Pugh refer to complex problems as in feedback-rich environment. This environment, or a system, incorporates various components within itself that interact with each other and generate feedbacks that make it complex. In addition to that there are some factors outside the system that also affect the system in some way.
System dynamics is a policy-based methodology that evaluates the effect of policy changes on a system. For any system, the decisions that we make affect the behavior of the system. System dynamics tries to find out the factors that cause the characteristic behavior of that system. Then how the system reacts to the changes associated with these factors is observed. Based on these reactions, the changes in policy are then suggested. Thus the main purpose of system dynamics is to better understand the complex and dynamic systems and suggest the changes in the decision-making rules so as to improve the performance. System dynamics is fundamentally used to understand policy decisions and feedbacks.
Policy is a set of ideas that express the strategies that are based on some information. A policy decision is the part of decision-making process in which the decisions are based on the ideas conveyed by the policies. Forrester (1958) defines policy as a “rule that states how the day-by-day operating decisions are made”. Policy decision is an important aspect of system dynamics. System Dynamics is a policy assessment methodology that re-designs policies that will aim to remove existing problem in the system.
As the name says, there is something that is fed back. This “something” is nothing but the information. Consider one of the elements of the system that has some effect on the other element of that system. The effect meaning the change in first element causes some sort of change in the other element. Now suppose that this change in second element effects back on the first element, then that constitutes “feedback”.
This feedback generates disequilibrium and hence dynamics in the system. The disequilibrium is caused because of the change in one element causes change in the other element, which, in turn, leads to the change in first element that again causes the change in second element. The feedbacks can be of two types, viz. positive or self-reinforcing and negative or self-correcting.
In the former, the changes are amplified in the system. While in latter the feedback tries to bring the system into equilibrium by opposing the change taking place in the system. It is little easier to predict the behavior of the system with a two-element feedback structure. But for the system with more elements interacting with each other, it is not possible to determine the system behavior with analytical methods. This is due to the fact that there is a combination of positive and negative feedbacks, in addition to the fact that each loop can dominate at different points in time, which adds to the system complexity.
The conceptual modes of the system are usually too complex to analyze analytically. Also conceptual models are usually tested with feedback from the real world, which occurs very slowly. However, simulation provides the decision-makers with a tool by which they can imitate the system and can observe and analyze the effects of decisions on the system. The major advantage of simulation is that the “learning” occurs very fast. The non-linearity present in the real system adds to the complexity of the simulation models. Therefore, computers are used carry out the necessary mathematical calculations and facilitate the design of enhanced decision support systems.
Causal Loop Diagrams
This is an important system dynamics tool that captures the feedback structure of the system. A Causal Loop Diagram (CLD) is used to map the cause-effect relationship between different variables within the system. The two variables are linked with an arrow with one of the two states of polarity, positive (+) or negative (-) (Figure 2.1).
The arrow starts from the “cause” variable and goes into the “effect” variable. The positive polarity of the linkage denotes that the increase (decrease) in cause variable will lead to the increase (decrease) in effect variable, all else being equal. Thus both variables move in the same direction. On the other hand, the negative polarity designates that the increase (decrease) in cause variable will lead to decrease (increase) in effect variable, all else being equal (Sterman 2000). Thus, both variables move in the opposite direction.
Positive Causal Linkage
Figure 2.2 shows that all else being equal, if X increases (decreases) then Y increases (decreases) above (below) what it would have been.
Negative Causal Linkage
Figure 2.3 shows that all else being equal, if X increases (decreases) then Y decreases (increases) below (above) what it would have been. The causal link polarity is, sometimes, represented by “S” and “O”, where S stands for “Same” and O stands for “Opposite”. But it has the same meaning as + and – linkages respectively. Also, the positive linkages may be represented with blue arrows and negative with red arrows.
The main advantage of the causal loop diagram is that it quickly captures the hypotheses concerning the causes of dynamics. The whole system is represented as a series of linkages and feedback loops. This is the first stage of the modeling approach where the whole system is qualitatively represented. It forms the basis for the next modeling step, which is the quantitative description of the model provided by the stock and flow diagram.
System Dynamic Behaviors
Fundamental Modes of Behaviors
The feedback structure in a system gives rise to three different fundamental behaviors. They are generated because of the positive and negative feedbacks within the system and also due to the delays.
Exponential Growth: This behavior occurs due to positive, self-reinforcing feedback. The change in one quantity within the system causes positive change in the other quantity. The change in the other quantity feeds back and causes, again, a positive change in the first quantity. Thus the positive effect is reinforced. As represented in Figure 2.4 (a), the reinforcing loop is represented by “R”. “Net Increase Rate” raises the “State of the System” and Increase in the “State of the System” increases “Net Increase Rate”. The example of exponential growth is the feedback loop between the “Net Birth Rate” and the “Population”. As the net birth rate increases, population increases and as population increases, the net birth rate increases, leading the feedback loop to exponential growth.
Goal Seeking: This behavior occurs due to a negative, self-balancing loop represented in Figure 2.4 (b) by the letter “B”. State of the System is compared with the goal or the desired state of the system. Depending on the discrepancy, corrective action is taken. Corrective action is more if the discrepancy is more. Corrective action takes the state of the system towards the desired state. Again the state of the system is compared with the desired state and depending upon discrepancy, a corrective action is taken. Thus, the structure tries to take the system towards desired state. An example of goal seeking behavior is cooling of coffee to the room temperature. The state of the system is the current temperature of coffee. The goal or the desired state is the room temperature. Depending upon the temperature difference, heat flows out of coffee reducing its temperature. Finally, it reaches the temperature of the room.
Oscillation: This behavior is observed when there is a delay in the negative feedback loop. It is represented in figure 2.4 (c). The loop is similar to the goal seeking loop except the delay in the responses. The negative feedback loop tends to take the state of the system towards the “goal”. But because of the delay, the sy
Chapter 1: Introduction
1.2 Research Objective
1.3 Problem Definition
1.4 Research Motivation
1.5 Overview of Methodology
1.6 Organization of Thesis
Chapter 2: Literature Review
2.2 General System Theory
2.3 Industrial Dynamics.
2.4 System Dynamics
2.5 System Dynamics Modeling
2.6 New Technology Implementation
Chapter 3: The Model
3.1 Steps of the Modeling Process
3.2 Problem Articulation
3.3 Formulation of Dynamic Hypotheses
3.4 Formulation of the Simulation Model
3.5 Causal Loop Diagram
3.6 Technology Integration Stock and Flow Diagram
3.7 The Stock and Flow Structures
Chapter 4: Results, Testing, Verification and Validation
4.1 Simulation Values
4.2 Simulation Runs and Results
4.3 Hypothesis Testing
4.4 Sensitivity Analysis
4.5 Testing, Verification and Validation
Chapter 5: Conclusions
5.1 Overview of the Results
5.2 Verification of Dynamic Hypotheses
5.3 Research Innovations
5.4 Policy Suggestions
5.5 Areas for Future Research
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