Lesson 1 –
Introduction to Systems Thinking
IE2141 Systems Thinking and Dynamics
Dr. LI Haobin
Senior Lecturer
Department of Industrial Systems Engineering and Management (ISEM)
College of Design and Engineering
National University of Singapore
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Why to Learn Systems Thinking?
 Example of urban transportation in mega cities
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Why to Learn Systems Thinking?
 Various roles of Designers and Engineers
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Why to Learn Systems Thinking?
 Interdisciplinary consideration
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Why to Learn Systems Thinking?
 System problems, E.g.
 How effective is the odd-even restriction policy?
 Car plate bidding or lottery, which is better?
Car Plate
Lottery
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Bidding
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Why to Learn Systems Thinking?
 Consideration of stakeholders with different levels of
perspectives, E.g.
 Should the government restrict car ownership, if the car manufacturers
are the major industry and taxpayers?
 Car ownership vs. road usage, who is bearing the cost?
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Why to Learn Systems Thinking?
 Good system solutions, E.g.
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COE with validity period, road and fuel tax, ERP
Well designed road network
Shared parking facility
Subsidised public transit
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Why to Learn Systems Thinking?
 As Engineers,
 How shall we design a good system?
 How to design a product that helps to build the good system?
 Alternatively, how to design a product that can fit well into the
dynamics of the system?
 To achieve this, you will need to
 Lean to think from the system perspectives, and
 Master tools to understand and analyse
system dynamics
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1st Law of System Thinking
 Today’s problems come from yesterday’s “solution”.
Often we are puzzled by the causes of our problems, when we merely
need to look at our own solutions to other problems in the past.
Solutions that shift problems from one part of a system to another often
go undetected because, those who "solved" the first problem are
different from those who inherit the new problem.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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2nd Law of Systems Thinking
 The harder you push, the harder the system pushes back.
The phenomenon of "Compensating feedback": when well-intentioned
interventions call forth responses from the system that offset the benefits
of the intervention.
The more effort you expend trying to improve matters, the more effort
seems to be required, either through an increasingly aggressive
intervention or through increasingly stressful withholding of natural
instincts. Yet, as individuals and organizations, we not only get drawn
into compensating feedback, we often glorify the suffering that ensues.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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3rd Law of Systems Thinking
 Behaviour grows better before it grows worse.
The better before worse response to many management interventions is
what makes decision making counterproductive, in the situations where
factors other than the intrinsic merits of alternative courses of action
weigh in making decisions – factors such as building one's own power
base, or "looking good," or "pleasing the boss.“
In complex human systems there are always many ways to make things
look better in the short run. Only eventually does the compensating
feedback come back to haunt you.
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4th Law of Systems Thinking
 The easy way out usually leads back in.
We all find comfort applying familiar solutions to problems, sticking to
what we know best. Very often, the keys are off in the darkness. After all,
if the solution were easy to see or obvious to everyone, it probably would
already have been found.
Pushing harder and harder on familiar solutions, while fundamental
problems persist or worsen, is a reliable indicator of nonsystemic
thinking – what we often call the "what we need here is a bigger
hammer" syndrome.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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5th Law of Systems Thinking
 The cure can be worse than the disease.
Sometimes the easy or familiar solution is not only ineffective, but
addictive and dangerous. The long-term, most insidious consequence of
applying nonsystemic solutions is increased need for more and more of
the solution. The phenomenon is called "Shifting the Burden to the
Intervenor.“
Instead, any long-term solution must, strengthen the ability of the system
to shoulder its own burdens. Sometimes that is difficult; other times it is
surprisingly easy.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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6th Law of Systems Thinking
 Faster is slower.
Virtually all natural systems, from ecosystems to animals to
organizations, have intrinsically optimal rates of growth. The optimal rate
is far less than the fastest possible growth. When growth becomes
excessive, as it does in cancer, the system itself will seek to compensate
by slowing down; perhaps putting the organization's survival at risk in the
process.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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7th Law of Systems Thinking
 Cause and effect are not closely related in time and space.
“Effects" means the obvious symptoms that indicate that there are problems.
“Cause" means the interaction of the underlying system that is most
responsible for generating the symptoms, and it could lead to changes
producing lasting improvement.
A fundamental characteristic of complex human systems: "cause" and
"effect" are not close in time and space. However, most of us assume, most
of the time, that cause and effect are close in time and space. There is a
fundamental mismatch between the nature of reality in complex systems and
our predominant ways of thinking about that reality.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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8th Law of Systems Thinking
 Small changes can produce big results, but the areas of
highest leverage are often the least obvious.
Some have called systems thinking the “new dismal science” because it
teaches that most obvious solutions don't work – at best, they improve
matters in the short run, only to make things worse in the long run.
But there is another side to the story. For systems thinking also shows
that small, well-focused actions can sometimes produce significant,
enduring improvements, if they're in the right place. Systems thinkers
refer to this principle as “leverage”.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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9th Law of Systems Thinking
 You can have your cake and eat it too – but not at once.
Sometimes, the knottiest dilemmas, when seen from the systems point
of view, aren't dilemmas at all. They are artifacts of "snapshot" rather
than "process" thinking, and appear in a whole new light once you think
consciously of change over time.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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10th Law of Systems Thinking
 Dividing an elephant in half does not produce two small elephants.
Living systems have integrity. Their character depends on the whole. The
same is true for organizations; to understand the most challenging
managerial issues requires seeing the whole system that generates the
issues.
Incidentally, sometimes people go ahead and divide an elephant in half
anyway. It results in a complicated problem where there is no leverage to be
found because the leverage lies in interactions that cannot be seen from
looking only at the piece you are holding.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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11th Law of Systems Thinking
 There is no blame.
We tend to blame outside circumstances for our problems. “Someone
else” – the competitors, the press, the changing mood of the
marketplace, the government – did it to us.
Systems thinking shows us that there is no outside; that you and the
cause of your problems are part of a single system. The cure lies in your
relationship with your “enemy”.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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What is a System?
 A system is a group of interacting or interrelated elements that
act according to a set of rules to form a unified whole.
 A system, surrounded and influenced by its environment, is
described by its boundaries, structure and purpose and
expressed in its functioning.
Source: Alexander Backlund (2000). "The definition of system". In:
Kybernetes Vol. 29 nr. 4, pp. 444–451.
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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How to Define a System?
 Elements / Parts
 Interaction / Interrelation / Structure
 Boundaries / Environment
 Purpose / Functions
Elements / Parts
Boundaries
 Input / Output
Input
(Dependency on
Environment)
Output
(Purpose / Functions)
Interaction / Interrelation / Structure
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Examples of Systems
 Human Body Systems
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Examples of Systems
 Computer Systems
Software System
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Hardware System
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Examples of Systems
 Industrial Systems
Container Port System
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Warehouse System
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Examples of Systems
 Natural / Technological Eco-Systems
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Examples of Systems
 Social Systems
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Systems vs. Collections
 Collection is a set of items or amount of material procured or
gathered together while system is a collection of “organized” things
Elements / Parts
Boundaries
Input
(Dependency on
Environment)
Interaction / Interrelation / Structure
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Elements / Parts
Boundaries
Output
(Purpose / Functions)
System
Collection
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Systems vs. Collections
 Collection is a set of items or amount of material procured or
gathered together while system is a collection of “organized” things
 Examples of Collections:
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Environment / External Boundaries
 PESTLE
 Political
 Economic / Financial
Elements / Parts
 Socio-Cultural / Societal
Boundaries
 Technological
 Legal
 Environmental
Input
(Dependency on
Environment)
Output
(Purpose / Functions)
Interaction / Interrelation / Structure
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Environment / External Boundaries
 Political Factors relate to the pressures brought by political
institutions
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Elections and political trends
Internal political issues
Inter country relationships
Local commissioning processes
Corruption, Bureaucracy
Wars, terrorism and conflicts
Government policies
Lobbying and pressure groups
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Environment / External Boundaries
 Economic Factors relate to economic policies and structures
 Local economy
 Taxation, inflation, interest
 Economy trends seasonality issues
 Industry growth
 Import / export ratios
 International trade
 International exchange rates
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Environment / External Boundaries
 Social Factors relate to the cultural aspects that affect the demand of
products and how business operates
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Demographics
Media views of the industry
Work ethic
Brand, company, technology image
Lifestyle trends
Consumer buying patterns
Ethical issues
Advertising and publicity
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Environment / External Boundaries
 Technological Factors relate to the technological aspects,
innovations, barriers and incentives
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Emerging technologies
Maturity of technology
Technology legislation
Research and Innovation
Information and communications
Competitor technology development
Intellectual property issues
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Environment / External Boundaries
 Legal Factors relate to the laws, regulation and legislation that will
affect the way businesses operate
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Current legislation
International legislation
Employment law
Consumer protection
Health and safety regulations
Tax regulations
Competitive regulations
Industry specific regulations
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Environment / External Boundaries
 Environmental Factors relate to the aspects of climate and
natural environment
 Environmental regulations
 Ecological regulations
 Reduction of carbon footprint
 Sustainability
 Impact of adverse weather
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Classification of Systems
 Classification by system characteristics
 Static vs. Dynamic Systems
 Causal vs. Non-Causal Systems
 Time-Variant vs. Time-Invariant Systems
 Linear vs. Non-Linear Systems
 Invertible vs. Non-Invertible Systems
 Stable vs. Unstable Systems
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Static vs. Dynamic Systems
 Static System – output of system depends only on present values of input
 Memoryless system
 Dynamic System – output of system depends on past or future values of
input at any instant of time
 System with memory
𝑋𝑋 𝑡𝑡 − 𝛿𝛿
𝑋𝑋 𝑡𝑡
𝑋𝑋 𝑡𝑡 + 𝛿𝛿
Example of static systems:
𝑌𝑌 𝑡𝑡 = 𝑋𝑋 𝑡𝑡 + 3, 𝑌𝑌 𝑡𝑡 = 2𝑋𝑋 𝑡𝑡
SYSTEM
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
𝑌𝑌 𝑡𝑡
Example of dynamic systems:
𝑌𝑌 𝑡𝑡 = 𝑋𝑋 𝑡𝑡 − 1 , 𝑌𝑌 𝑡𝑡 = 3𝑋𝑋 𝑡𝑡 + 2 ,
𝑌𝑌 𝑡𝑡 = 𝑋𝑋 𝑡𝑡 + 2𝑋𝑋 𝑡𝑡 − 1
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Static vs. Dynamic Systems
 Examples
 Static systems – furniture, dishes, buildings, bridges, fix deposit, onetime investment
(simplification, approximation or abstraction of real-world dynamic systems)
 Dynamic systems – human body, computer, machinery, car, property,
trading strategy
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Causal vs. Non-Causal Systems
 Causal System – output of system is independent of future values of input
 All real-life system, all practical or physically realizable systems are causal systems
 Non-Causal System – output of system depends on future values of input at any
instant of time
 Anti-causal system – output of system only depends on future values of the input
Example of cause systems:
𝑋𝑋 𝑡𝑡 − 𝛿𝛿
𝑋𝑋 𝑡𝑡
𝑋𝑋 𝑡𝑡 + 𝛿𝛿
SYSTEM
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
𝑌𝑌 𝑡𝑡
𝑌𝑌 𝑡𝑡 = 𝑋𝑋 𝑡𝑡 + 3, 𝑌𝑌 𝑡𝑡 = 2𝑋𝑋 𝑡𝑡 + 𝑋𝑋 𝑡𝑡 − 1
Example of non-causal systems:
𝑌𝑌 𝑡𝑡 = 𝑋𝑋 𝑡𝑡 − 1 + 2𝑋𝑋 𝑡𝑡 + 3𝑋𝑋 𝑡𝑡 + 2
Example of anti-causal systems:
𝑌𝑌 𝑡𝑡 = 2𝑋𝑋 𝑡𝑡 + 1
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Causal vs. Non-Causal Systems
 Examples
 Causal systems – furniture, dishes, buildings, bridges, fix deposit,
one-time investment, human body, computer, machinery, car, property,
trading strategy
 Non-causal systems – an ideal predictive maintenance system, a
perfect just-in-time system
(not practical or implementable in real-life)
Just-In-Time
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Predictive Maintenance
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Time-Variant vs. Time-Invariant Systems
 Time-Variant (TV) System – a system whose output response depends on
moment of observation as well as moment of input signal application.
 In other words, a time delay or time advance of input not only shifts the output signal
in time but also changes other parameters and behavior.
 Time variant systems respond differently to the same input at different times.
 Time-Invariant (TIV) System – a system where the opposite is true for.
𝑋𝑋 𝑡𝑡
SYSTEM
𝑌𝑌 𝑡𝑡
Delay by 𝑡𝑡0
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Delay by 𝑡𝑡0
𝑋𝑋 ′ 𝑡𝑡 = 𝑋𝑋 𝑡𝑡 − 𝑡𝑡0
𝑌𝑌 ′ 𝑡𝑡 = 𝑌𝑌 𝑡𝑡 − 𝑡𝑡0
SYSTEM
𝑌𝑌 ′′ 𝑡𝑡 ≠ 𝑌𝑌 ′ 𝑡𝑡
𝑌𝑌 ′′ 𝑡𝑡 = 𝑌𝑌 ′ 𝑡𝑡
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Time-Variant vs. Time-Invariant Systems
 Examples
 Time-variant (TV) systems – investment in stocks
 Time-invariant (TIV) systems – investment in CPF Accounts
(TIV systems are relative in real-life, e.g., reaching age of 55)
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Linear vs. Non-Linear Systems
 Linear System – a system which follows the principle of superposition
 Law of Additivity + Law of Homogeneity
 Non-Linear System – a system for which the principle of superposition is
violated.
𝑋𝑋1 𝑡𝑡
∑
𝑋𝑋2 𝑡𝑡
SYSTEM
𝑋𝑋1 𝑡𝑡 + 𝑋𝑋2 𝑡𝑡
𝑌𝑌1 𝑡𝑡
SYSTEM
SYSTEM
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
𝑌𝑌2 𝑡𝑡
𝑌𝑌 ′′ 𝑡𝑡 = 𝑌𝑌 ′ 𝑡𝑡
𝑌𝑌 ′′ 𝑡𝑡 ≠ 𝑌𝑌 ′ 𝑡𝑡
∑
𝑌𝑌 ′ = 𝑌𝑌1 𝑡𝑡 + 𝑌𝑌2 𝑡𝑡
Law of Additivity
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Linear vs. Non-Linear Systems
 Linear System – a system which follows the principle of superposition
 Law of Additivity + Law of Homogeneity
 Non-Linear System – a system for which the principle of superposition is
violated.
𝑋𝑋 𝑡𝑡
SYSTEM
𝑘𝑘
𝑘𝑘𝑘𝑘 𝑡𝑡
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
𝑌𝑌 𝑡𝑡
SYSTEM
𝑘𝑘
𝑌𝑌 ′ = 𝑘𝑘𝑘𝑘 𝑡𝑡
𝑌𝑌 ′′ 𝑡𝑡 = 𝑌𝑌 ′ 𝑡𝑡
𝑌𝑌 ′′ 𝑡𝑡 ≠ 𝑌𝑌 ′ 𝑡𝑡
Law of Homogeneity
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Linear vs. Non-Linear Systems
 Examples
 Linear systems – pricing for groceries at FairPrice, total time spent by a
crowd watching a movie
 Non-linear systems – pricing for stocks at SGX, total time spent by a
crowd queueing for a restaurant
(real-life systems are difficult to control as many of them are non-linear)
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Invertible vs. Non-Invertible Systems
 For an invertible system, there should be one to one mapping
between input and output at each and every instant of time
One to one mapping
1
𝑎𝑎
2
3
𝑐𝑐
6
𝑏𝑏
2
𝑋𝑋 𝑡𝑡
Many to one mapping
SYSTEM
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
𝑎𝑎
4
𝑌𝑌 𝑡𝑡
𝑋𝑋 𝑡𝑡
𝑏𝑏
SYSTEM
𝑌𝑌 𝑡𝑡
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Invertible vs. Non-Invertible Systems
 For an invertible system, there should be one to one mapping
between input and output at each and every instant of time
𝑋𝑋 𝑡𝑡
Invertible
System
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
𝑌𝑌 𝑡𝑡
Inverse
System
𝑌𝑌 ′ 𝑡𝑡 = 𝑋𝑋 𝑡𝑡 ± 𝛿𝛿
𝑌𝑌 ′ 𝑡𝑡 ≠ 𝑋𝑋 𝑡𝑡 ± 𝛿𝛿
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Invertible vs. Non-Invertible Systems
 Examples
 Invertible systems – identifying a person by his/her IC, knowing a
person by his/her spouse, sending emails by an internet user
 Non-invertible systems – paying bills, scoring in an exam, sending
emails by a hacker
(real-life systems are complex as many of them are non-invertible)
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Stable vs. Unstable Systems
 For a stable system, output should be bounded for bounded
input (BIBO) at each and every instant of time
𝑋𝑋 𝑡𝑡
𝑋𝑋 𝑡𝑡 ∈ 𝐿𝐿𝑋𝑋 , 𝑈𝑈 𝑋𝑋
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
SYSTEM
𝑌𝑌 𝑡𝑡
𝑌𝑌 𝑡𝑡 ± 𝛿𝛿 ∈ 𝐿𝐿𝑌𝑌 , 𝑈𝑈 𝑌𝑌
𝑌𝑌 𝑡𝑡 ± 𝛿𝛿 ∈ −∞, ∞
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Stable vs. Unstable Systems
 Examples
 Stable systems – market of iPhone 13, restaurant, public transit
 Unstable systems – climate change, financial crisis
(unstable systems are relative, as the output can be always bounded under
a larger system, however it is beyond our control or not in favor)
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Feedback and Control Systems
 A control system possessing monitoring feedback, the deviation signal
formed as a result of this feedback being used to control the action of a
final control element in such a way as to tend to reduce the deviation to
zero (Mayr, Otto 1970).
Parent System
Environment
𝑋𝑋 ′ 𝑡𝑡
Goal
Deviation
(Discrepancy)
Controller
Perceived State
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Action
(Decision)
𝑋𝑋 𝑡𝑡
Observer
Sub-System
System
𝑌𝑌 𝑡𝑡
𝑌𝑌 ′ 𝑡𝑡
Feedback
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Feedback and Control Systems
 Example – Air-Con System
Room
Air-Con
Environment (outdoor temperature)
𝑋𝑋 ′ 𝑡𝑡
Goal
(specific room
temperature)
Deviation
(temperature
difference)
IC Chip
Perceived State
(sensible temperature)
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
Action
(electricity/
power)
𝑋𝑋 𝑡𝑡
Sensor
Compressor
System
𝑌𝑌 𝑡𝑡
(cool
air)
𝑌𝑌 ′ 𝑡𝑡
Feedback
(room temperature)
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Coupling of Sub-Systems
 A coupled system consists of sub-systems, including control
systems, connected in series or in parallel
S6
S5
C2
C1
S1
S4
S2
S7
S3
O1
O2
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Coupling of Sub-Systems
 A Controller or Observer in a control system can also be seen
as a sub-system
S6
S5
S11
S9
S1
S4
S2
S7
S3
S8
S10
IE2141 Systems Thinking and Dynamics – Dr. Li Haobin, National University of Singapore
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Hierarchical Structure of Systems
 The composition of coupled systems are usually represented in
the hierarchical structure
System
S6, S7
S5, S10, S11
S4, S8, S9
S6
S5
S1
S4
S2
S8
S3
S10
S9
S7
S11
S1, S2, S3
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Methodologies of Systems Thinking
1. Identifying a system
 Elements, boundaries, sub-systems
 Environment, purpose, input and output
 Stakeholders (controllers), i.e., who are making decisions and
influencing the sub-systems
 Perceptions (observers), i.e., how the information from feedbacks of
sub-systems are perceived
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Methodologies of Systems Thinking
2. Understanding a system
Understanding how the system works, its interconnections and
behaviors
 Classification of the system / sub-systems
 Level of perspectives
 Behavior over time
 Causal loop diagrams (CLD)
 System archetypes
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Methodologies of Systems Thinking
3. Predicting system behavior
Forecast how the system will behavior in future
 System dynamics simulation
 Stock-flow diagrams (SFD)
 Modelling time delays
 Data collection and estimation
 Verification and validation
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Methodologies of Systems Thinking
4. Devising improvements
How can we modify the system to produce desired results?
 Modelling of decision-making structure
 Identifying leverage points
 Decision analysis methodologies
 Optimization of decision parameters
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THANK YOU
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