Introduction of System Dynamics

1. Introduction of System Dynamics Chapter One

2. Historical Overview (1) • System dynamics is a methodology for analyzing complex systems and problems with the aid of computer modelling and simulation software.

3. Historical Overview (2) • Originated from the research of Professor Jay W. Forrester at Massachusetts Institute of Technology in the late 1950s. • Initial goal was to determine how his background in science and engineering could be brought to bear, in some useful way • Involve d with managers at General Electric (GE) during the mid-1950s. The business cycle was judged to be an insufficient explanation for the employment instability • From 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 proves - instability in GE employment was due to the internal structure of the firm.

4. Historical Overview (3) • Late 1950s and early 1960s - Forrester and a team of graduate students developed formal computer modelling stage.  • SIMPLE (Simulation of Industrial Management Problems with Lots of Equations) by Richard Bennett – first SD computer modeling language. • Phyllis Fox and Alexander Pugh wrote the first version of DYNAMO (DYNAmicMOdels), an improved version of SIMPLE • From the late 1950s to the late 1960s, system dynamics was applied almost exclusively to corporate/managerial problems. • The second major noncorporate application of system dynamics - Jay Forrester was invited by the Club of Rome (organization devoted to solving what its members describe as the "predicament of mankind“) —that is, the global crisis that may appear sometime in the future, due to the demands being placed on the Earth's carrying capacity (its sources of renewable and nonrenewable resources and its sinks for the disposal of pollutants) by the world's exponentially growing population.

5. SD Definitions

6. HARD AND SOFT MODELLING (1) • Model is defined as being representation of the real world e.g. physical, analog, digital (computer), mathematical, etc. • Hard modelling – refers to quantitative modelling • Soft modelling – refers to conceptual and contextual approaches that tend to be more realistic, pluralistic and holistic than hard modelling

7. HARD AND SOFT MODELLING (2)

8. System Thinking, Mental Model and System Dynamics • Four level of system thinking new event(s) or thing(s), or issue(s) happen Same event happen in so many times, creates pattern Studies about how such trends and patterns relate to and effect one another and how the interplay of different factors brings about the outcomes that we observe Visualization of systemic structure, in term of the problem(s), feedback structure(s), cause(s) and effect(s), influenced and influencing factor(s), solution(s)

9. System Thinking, Mental Model and System Dynamics (2)

10. System Thinking, Mental Model and System Dynamics (3)

11. SYSTEM DYNAMICS METHODOLOGY • Linear versus system thinking • Collections versus systems • Event versus patterns • Symptoms versus root causes • Solution versus leverage

12. Discrete Event Simulation versus System Dynamics (1)

13. Discrete Event Simulation versus System Dynamics (2)

14. Discrete Event Simulation versus System Dynamics (3)

15. WHY AND WHY NOT SYSTEM DYNAMICS • Advantages of SD • Captures the interdependencies between all subsystems that make up the whole • Combines qualitative and quantitative aspects • To enhance understanding of a system and the relationships between different system components • To provide an understanding of the modes of behaviour • Holistic, with aggregate flows • To provide prediction

16. WHY AND WHY NOT SYSTEM DYNAMICS (2) • Advantages of SD • Used to model the relationships between system variables, rate of change over time and explicit feedback • Associated with higher level types of problems, especially consideration of the impact of policy and strategy decision • Enables users to understand why structure produces behaviour (the base case), and how behaviour varies under different conditions (the policy analysis) • Provides feedback loop

17. WHY AND WHY NOT SYSTEM DYNAMICS (3) • Disadvantages of SD • Cannot provide individual analysis • Not suitable for assessing hard/discrete/tangible factors • Not suitable to provides micro perspectives • Not suitable to assess short term effect

18. Hybrid Simulation (1) • Combining the DES and SD – will be beneficial when the detail, stochastic and individual analysis (provided by DES) and whole system approach (by SD) is combined. • Hybrid technique will work sort of information sharing, where DES and SD will consider taking and giving one to another • It will help the decision maker to consider a more reliable model prior to the implementation of any decision, as they can see from detail perspective to the whole picture

19. - Individual analysis - Short term decision making - modelling hard or tangible factor Hybrid Simulation (2) System dynamics lack of provides provides - Long term decision making - Feedback loop - modelling soft or intangible factor Discrete Event Simulation lack of

20. APPLICATIONS OF SYSTEM DYNAMICS • Healthcare • Pandemic issues • Obesity • Ageing • Public Management • Corruption • Pension • Poverty • Production and Manufacturing • Link between production and human resource • Supply chain management • Car manufacturing • Construction • Weather and construction activities • Building plan • Economics • Tax planning • Demand and supply • Financial