Section a Group7 Term Paper

8/11/2019 Section a Group7 Term Paper

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System Dynamic Modeling

Operations Research

Term Paper

Group 7

Anuradha Dhote (PGP/17/010)

Jignesh Rathod (PGP/17/037)Sushant Kumar (PGP/17/053)

Usha Bhakuni (PGP/17/057)

Yashasvi Kansotia (PGP/17/062)


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1.Abstract

System Dynamic Modelling is an approach to frame and solve complex problems. It is widely used

for policy design and analysis. System dynamics analysis is a part of Systems Theory which is used to

study the dynamic behaviour of complex systems. The theory is based on acknowledgement of the

fact that the inter-relations between the various components of a model, time delays between

processes are as important to analyse the behaviour of a model as is the analysis of individual

components. SD models solve the problem of simultaneity (mutual causation) by updating all

variables in small time increments with positive and negative feedbacks and time delays structuring

the interactions and control.

In this paper, we have discussed the basic methodology of the technique, and the modelling process.

Then, we have discussed the applications of SDM ranging from conventional ones like project

management and science and engineering to exploring the concepts of SDM in Brand management

and public health issues.

2.Historical background

This technique of solving problems was developed in 1950s by Prof Jay Forrester ofMassachusetts

Institute of Technology, during his involvement with General Electric. He developed his insights

about the foundations of engineering which helped him in the creation of system dynamics. He

studied the problem of instability in GE employment. In this he did hand simulations (or calculations)

of the stock-flow-feedback structure of the GE plants, which included the existing corporate

decision-making structure for hiring and layoffs. Forrester was able to show how the instability in GE

employment was due to the internal structure of the firm and not to an external force such as thebusiness cycle. Thesehand simulations were the beginning of the field of system dynamics.

During 1960s, the problem solving evolved from hand simulations to formal computer modelling.

During this time programming languages based on this approach were developed, like SIMPLE and

DYNAMO.

3.Why is System Dynamic Modelling needed

A model usually has many factors related to it and it is very important to study each of thesefactors to gain complete knowledge about the subject, though it is not so easy, it takes a lot

of effort to integrate these and study the changes in one relative to the other, this is exactly

where SDM comes to our rescue, provides us with a common platform and thus it enablesus

to combine the various factors involved in a model and study the whole model as one single

unit rather than studying different factors one at a time.

In the long history of evolution it has not been necessary until very recent historical times

for people to understand complex feedback systems. Evolutionary processes have not given

us the mental ability to interpret properly the dynamic behaviour of those complex systems

in which we are now embedded. - Forrester, 1973
http://en.wikipedia.org/wiki/Massachusetts_Institute_of_Technologyhttp://en.wikipedia.org/wiki/Massachusetts_Institute_of_Technologyhttp://en.wikipedia.org/w/index.php?title=Stock-flow-feedback&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Hand_simulations&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Hand_simulations&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Stock-flow-feedback&action=edit&redlink=1http://en.wikipedia.org/wiki/Massachusetts_Institute_of_Technologyhttp://en.wikipedia.org/wiki/Massachusetts_Institute_of_Technology


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Another main reason why we need SDM is that we now have to face Complex systems and if

we need to make an improvement to the same one cannot change one part without a

certain risk of affecting other remote parts. Thus we are obliged to understand the whole

system.

4.System Dynamic Modelling Approach

System Dynamic Modelling provides an approach to analyse complex real world problems over a

period of time. In this approach, the modeler attempts to identify the patterns of behaviour of the

system measured by certain parameters of the system, and then build a model that can mimic the

patterns. Once the model is built, it can be used for testing by altering a systems behaviour.

Time paths

A critical step in examining a system is to identify its key patterns of behavior -called "time paths."

They describe how a system behaves over time. There are various types of time paths that a system

followslinear, exponential, S-shaped, Oscillatory, etc.

Stocks and Flows

System Dynamic Modelling is based on the Principle of Accumulation which states that all dynamic

behaviour in a system occurs when flows accumulate in stocks. The stock-flow structure is the

simplest dynamic system. In case of SDM, information and non-information move through flows and

are stored/ processed in stocks.

Theoretically, a stock can have infinite number of inflows as well as outflows. However, in practice

only 5-6 flows are present with a stock.

Stocks have the following four characteristics

a) Have memory

b)

Change the time shape of flows

c) Decouple flows,

d) Create delays.

These characteristics are crucial in determining the behaviour of the system.

Feedback

Systems can be classified as being open or closed. In open systems, output has no influence on the

system or the inputs. Inputs will be independent of the output. Whereas, in case of closed systems,

the outputs respond to inputs and also influence the inputs. Most commonly existing systems are

closed loop systems.

Stocks and flows form a part of feedback loop that determines the relation between these

components. Every feedback loop must have one stock element.

There are two types of feedback loops


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1.

Positive feedback In a positive feedback loop, a change in any of the components is

amplified, and it causes a large self-change in the original direction. They are responsible for

the growth in the system.

2.

Negative feedback A feedback loop that tends to diminish an initial change in any of the

variables is called a negative loop. These loops are aimed at keeping the system in a desired

state. These loops stabilize the system.

Non linearity

A system is not perfect and its limits are set by the non-linear relationships that exist between its

components. These non-linear relationships can cause change in the overall response of the system.

These relationships can cause the system to behave in a manner of an entirely different system over

time.

5.System dynamic modelling process

The modelling process consists of the following steps

1. Identify the problem In a systems dynamic problem, modelling the entire system is very

complex and its easier to focus on only one problem. It sets a boundary and helps in

considering only the relevant variables rather than the entire system. It is better to begin

with a clearly defined problem statement.

2. Develop hypothesisOnce the problem statement is defined, the next step is to develop a

hypothesis about the possible cause of the given behaviour of the system. Causal loopdiagrams are used to list out some of the assumptions about the possible reasons. The

following process can be used to formulate assumptions

a.

List the relevant system variables

b.

Link the variables according to their inter-relationships keeping in mind the problem

statement

c. Form the feedback loops and try to analyse the possible causes.

This is an important stage to collect the information about the problem and the system.

3.

Test hypothesis In this stage the model is converted into mathematical model and acomputer model is made. Then simulations are run on the computer model to see whether

the system behaves in the desired manner. If it behaves differently, then there is a problem

with either the model or the hypothesis. Then, we need to re-look the entire model.

4. Test policiesOnce the model has been constructed, the root cause of the problem can be

easily identified. Accordingly, solutions to the problem can be proposed. This can help in

determining the uncertainties that are present in the system and how to overcome.

Different decisions can be tested on the model and the model can be improved upon

accordingly.


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Example: System Dynamic Modelling applied to public health issues

Let us take the example of system dynamic modelling applied to public health. Below figure shows a

broader view of population health using SDM approach.

When applied to the issue of chronic disease prevention, an SDM takes into account diseaseoutcomes, health and risk behaviours, environmental factors and health related delivery systems.

This model explains how a hypothetical set of people affected by chronic diseases may be affected

by two types of prevention - upstream prevention of disease onset and downstream prevention of

disease complication.

1.

We model the essential causal structure and policy inputs as below. There is gradually

changing net accumulation of two flows: inflow of disease onset and outflow of deaths


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2.

Here we map the simulation output over 50 years, in three scenarios (Status Quo, More

complications prevention and More Onset Prevention), for four variables

a. Onset prevention fraction

b.

Complication Prevention fraction

c.

People with disease

d.

Deaths from complications

Here we see that with the increase in new tools for complication prevention lead to an increase in

preventable fraction of complications from 25% to 50%. But reduction in deaths means longer

average stay in diseased population stock, which leads to greater disease prevalence. This in turn

increases the need for resource for complication prevention. Hence, this is a vicious cycle which

health care system may suffer from given its limited resources for disease prevention and need for

best possible management of existing disease.

References

1. http://www.systemdynamics.org/DL-IntroSysDyn/

2. SYSTEM DYNAMICSMODELINGINSCIENCEAND ENGINEERING, Hans U. Fuchs

3. System Dynamic modelling for Prjoect Management, John D Sterman

4. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1470525/

5.

Application of system dynamics to brand management, Vanguard Brand Management
http://www.systemdynamics.org/DL-IntroSysDyn/http://www.systemdynamics.org/DL-IntroSysDyn/http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1470525/http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1470525/http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1470525/http://www.systemdynamics.org/DL-IntroSysDyn/

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