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Title: Present System Dynamics as methodology to


1
Applying System Dynamics to Manage Dynamic
Complexity in Enterprises by Jose J Gonzalez
professor dt.techn., dr.rer.nat. AUC
Objectives
  • Present System Dynamics as methodology to
  • identify,
  • define,
  • model enterprise challenges characterized by
    dynamic complexity
  • Communicate how system dynamic simulations serve
    to
  • explore scenarios,
  • test policies,
  • identify robust strategies,
  • provide insights that lead to organizational
    learning
  • Exemplify by means of real-life cases
  • (mis)managing traffic pollution
  • boost and bum in semiconductor industry
  • time and cost overruns in large-size projects
  • erosion of security and safety standards
  • dealing with volatility and uncertainty in
    offshore

2
Issues
  • Characteristics of Enterprise Challenges
  • Dynamic Complexity The Logic of Failure
  • System Dynamics Methods and Applications
  • Learning in Complex Domains
  • Organizational Learning

3
Issues
  • Characteristics of Enterprise Challenges
  • Dynamic ComplexityThe Logic of Failure
  • System Dynamics Methods and Applications
  • Learning in Complex Domains
  • Organizational Learning
  • Examples of Enterprise Challenges
  • Analysis
  • Main Conclusions

4
Characteristic of Enterprise/Public Challenges
  • Consider the following enterprise (or even
    public) challenges
  • (Mis)managing traffic pollution in Mexico city
  • Boom and bust in semiconductor industry
  • Cost time overruns and quality problems in
    large-scale projects
  • Ubiquitous erosion of safety security
    standards, making companies and nations
    vulnerable (organizational accidents, cyberwar,
    terrorism)
  • Rig management in offshore companies
    (specifically, Statoil) fronting high risks (hugh
    investments per rig, volatile oil prices,
    unpredictable demand, unsafe conditions, emerging
    technologies)

5
Characteristic of Enterprise/Public
ChallengesManaging traffic pollution
  • Traffic pollution in Mexico city
  • Air pollution in Mexico City is amongst the worst
    in the world
  • The authorities decided to limit vehicle use
    every car has a color-code, and for one workday a
    week is banished
  • The expected result was a 20 reduction in car
    usage on weekdays
  • there now seems more cars than ever, and they
    seem to be producing ever increasing pollution
  • Link to Causal-loop analysis explains why
  • Such behavior is known as policy resistance. It
    is a typical outcome when planning ignores the
    propagation of effects and the impact of
    (counteracting) feedback.

6
Policy Resistance Attempt to Control Pollution
(Courtesy Prof. Graham Winch)
Intended effect Less pollution
Unintended effect Higher car use and more
polluting cars
Unintended effect More cars
RETURN to Managing traffic pollution in Mexico
7
Characteristic of Enterprise ChallengesBoom and
Bust
  • Boom and bust in semiconductor industry
  • An international diversified company was forced
    to write down several hundred million dollars in
    investments in semi-conductor capacity
  • New entrants were eager to capitalize on the
    buoyant market, which was exaggerated by perverse
    buying practices by the customers
  • In just a few years, that industry went from boom
    to bust from acute shortage to book-to-build
    ratios of only 70 at the trough
  • Link to Causal-loop analysis explains why
  • Among the crucial errors committed was failure to
    distinguish between perceived and real demand and
    to account for the impact of delays

8
Reference behavior for semiconductor industry
(Courtesy Prof. Graham Winch)
PHASE 1
PHASE 3
PHASE 2
Perceived demand
30
Demand
70
Capacity
FORWARD to examples of Modeling Perceptions
time
9
Causal loop for semiconductor industry (Courtesy
Prof. Graham Winch)

Supply
Perceived Demand


(B) Businessexpansion


Capacity Building
Demand Gap
(R) Ghost Demand


Prices and Profits
Demand

time delay
Ghost Orders

RETURN to Boom and bust in semiconductor industry
10
Characteristic of Enterprise ChallengesLarge-sca
le projects
  • Cost time overruns and quality problems in
    large-scale projects
  • Large-scale projects (e.g. design construction
    of civil works infrastructure, development of
    complex software or new products, military
    projects) are consistently mismanaged
  • Typical for commercial projects 140 costs
    190 time overruns
  • for military projects 310 costs 460 time
    overruns.
  • Link to Famous case Ingalls Shipbuilding, USA
  • Among the crucial errors committed was failure to
    consider the impact of propagations of delayed
    effects and to distinguish between perceived and
    real project progress

11
Famous CaseIngalls Shipbuilding
  • John D Sterman, Business Dynamics, 2000, Ch. 2
    describes case
  • In 1969-70 Ingalls Shipbuilding won two major
    contracts to build two fleets for the US Navy
  • Prospects looked very profitable but a few years
    later Ingalls was facing bankrupt with project
    cost overruns in the order of 1.5 billion USD in
    terms of year 2000 dollars
  • Ingalls blamed the US Navy for causing most of
    the project delays by due to many changes in the
    specifications
  • The US Navy disagreed sharply, since the changes
    though numerous were of minor nature
  • Ingalls sued the US Navy and based its case on a
    system dynamic model of the project developed by
    Pugh-Robert Associates.

12
Cost time overruns Ingalls ShipbuildingRef.
JD Sterman, Business Dynamics, 2000
13
Cost time overruns Ingalls ShipbuildingRef.
JD Sterman, Business Dynamics, 2000
Out-of-Sequence Work, Worksite Congestion, Bad
Coordination Morale
Dashed lines NegativeFeedbacks
Burnout
KnownRework
Obsolence Rate
Solid lines PositiveFeedbacks
FORWARD to examples of modeling perceptions
14
Famous CaseIngalls Shipbuilding
  • John D Sterman, Business Dynamics, 2000, Ch. 2
    describes result of court dispute
  • The system dynamic model showed, indeed, that
    changes in specification were responsible for
    most of the cost increase
  • The US Navy argued that the system dynamic model
    had been manipulated to provide desired results
    for Ingalls Shipbuilding
  • Modelers invited US Navy to criticize the model,
    accepting changes when reasonable
  • The revised model led to even greater impact of
    specification changes on costs
  • Hence, the US Navy accepted Ingalls claim

RETURN to Famous case
15
Characteristic of Enterprise ChallengesErosion
of standards
  • Ubiquitous erosion of safety security
    standards, making companies and nations
    vulnerable (organizational accidents, cyberwar,
    terrorism)
  • Human failure accounts for 70-90 of
    organizational accidents and security problems
  • but human failure must be seen as interacting
    with technology and working environment.
  • Rich variety of causes priority conflicts, human
    behavior economics, shrinkage of viable actions
    as system is patched, and last not least
    reinforcing of wrong attitudes modulated by risk
    misperception
  • Link to Causal loop analysis shows why
  • Crucial causes of the erosion of standards are
    misperception of risk and superstitious
    learning apparent (but not real) empirical
    confirmation of misperceptions and wrong causal
    attributions

16
CLD Erosion of security safety standardsRef.
JJ Gonzalez, 1995, 2002
and since most breaches do not lead to accidents
modern technology is forgiving risk is
misperceived, implying stronger reinforcing of
noncompliance
until the inevitable mishap happens and the
lesson is learned the hard way.
but noncompliance is reinforced since it brings
personal gains
All other things being equal employees would
comply with prescriptions
RETURN to Erosion
17
Characteristic of Enterprise ChallengesRig
management
  • Rig management in offshore companies
    (specifically, Statoil)
  • Hugh risky investments for rig brokers rigs
    costs typically 1 billion USD, take ca. 3 yr to
    build, financing groups demand, up-front 70 rig
    leasing within 5 yr to cover financial risks,
    emerging competing, technologies, changing safety
    legislation
  • Users offshore companies risk volatile oil
    prices (between 10 and 30 UDS pr barrel),
    uncertain profitability of lots, variety of
    operational conditions (tasks, climate, depth),
    and large price differences between long-term and
    spot rig leasing, overruns of offshore project
    costs and times.
  • Hence, unpredictable long-term demand for rig
    brokers
  • and unpredictable long-term supply for offshore
    companies.
  • Analysis shows that most aspects of The Logic of
    Failure are involved
  • Complexity challenges related to big delays,
    propagation of effects, uncertain external
    conditions, long time intervals up to 30 yr ,
    hugh financial stakes, misperception of feedback
    in short, most of the features identified as
    failure factors (Dietrich Dörner The Logic of
    Failure)

18
Issues
  • Characteristics of Enterprise Challenges
  • Dynamic Complexity The Logic of Failure
  • System Dynamics Methods and Applications
  • Learning in Complex Domains
  • Organizational Learning
  • About Dynamic Complexity
  • The Logic of Failure

19
Dynamic Complexity The Logic of Failure
  • There are two kinds of problem complexity
  • Combinatorial, a.k.a. detail complexity (many
    components and relationships)
  • Dynamic complexity (complex behavior over time)
  • The major challenge is dynamic complexity, found
    in non-linear systems, because it poses
    tremendous challenges The unaided mind is very
    poor at predicting the time development of
    non-linear systems, even if they only have a few
    components
  • Failure to deal with future developments has
    crucial consequences for companies Over one
    third of the Fortune 500 largest companies in
    1970 had disappeared 13 years later (Arie de
    Geus The Living Company)

20
Dynamic Complexity The Logic of Failure
  • Research by Dörner et al. about thinking,
    decision-making and acting in complex domains
    Most people fail and the behavior patterns are
    (quite) universal but a few master complexity.
  • Dörner found determinants of human failure
  • Linear thinking fails to account for
    propagation ramification of effects
  • Poor ability to perceive understand feedback
    (misperception of feedback, wrong causal
    attribution), hence policy resistance
  • Ignoring time delays, wrongly assigning causes to
    events close in time and space
  • Problems to perceive nonlinear growth and decay
  • Encapsulation falling in love with a
    particular aspect, ignoring other, often much
    more important aspects
  • Thematic vagabonding unfocused, poorly
    structured thinking
  • Etc

21
Issues
  • Characteristics of Enterprise Challenges
  • Dynamic Complexity The Logic of Failure
  • System Dynamics Methods and Applications
  • Learning in Complex Domains
  • Organizational Learning
  • About System Dynamics
  • Model development
  • Modeling perceptions delays
  • Structure and behavior
  • Types of system dynamics models
  • Integrated Solutions

22
About System Dynamics
  • System Dynamics is a discipline explicitly
    designed to manage systems characterized by
  • nonlinear dynamics,
  • feedback,
  • time delays,
  • soft factors,
  • interdisciplinary aspects
  • Founded 1957 by Jay W. Forrester as extension of
    control theory/cybernetics to management
  • Later succesfully applied to all kind of complex
    dynamic systems, involving psychological, social,
    technological or even environmental aspects

23
Qualitative System Dynamics
  • Qualitative System Dynamics employs causal loop
    diagrams to explain the likely mechanism of
    complex phenomena, such as attempts to manage
    traffic pollution in big cities or boom and bust
    in high-velocity industries.
  • At this level, causal loop diagrams explain
    cause-effect influences by an arrow pointing from
    cause to effect. No indications of strength nor
    or type (i.e. direct impact, cumulative impact,
    etc.) of the effect are given.
  • Even at this simple level, causal-loop diagrams
    can qualitatively explain phenomena, or even if
    the causal-loop diagram is designed in advance
    prevent the decision-maker from costly mistakes
    and suggest better measures to manage the system.
  • To understand the relationship between (causal)
    structure and dynamic behavior one needs
    quantitative methods, i.e. System Dynamics proper.

24
System Dynamics Methods
  • As methodology, System Dynamics spans from
    knowledge capture problem articulation to
    scenario policy analysis and improvement of
    organizational knowledge.
  • System Dynamics is best understood as an eclectic
    methodology a joint venture of disciplines
    borrowing methods and tools from other
    disciplines and amalgamating interdisciplinary
    sources of knowledge, such as
  • Methods Data mining, statistical parameter
    estimation, econometric methods, optimization,
    risk assessment management
  • Disciplines Nonlinear numerical methods, control
    theory cybernetics, management science,
    economics, psychology, group dynamics, supply
    value chain science, organizational learning,
  • System dynamics models can be stand-alone, but
    leading tool developers (High Performance
    Systems, Powersim Corporation, Ventana Systems)
    provide a variety of interfaces to other tools
    (API, OCR, ASP, etc).

25
Model development
  • Model development involves the following
    activities (that can be iterated)
  • Problem definition and articulation
  • Who cares and why?
  • Problem symptoms
  • Desired behavior
  • Policy behavior
  • Audience model purpose and uses
  • System boundary
  • Model conceptualization
  • Articulating issues, identifying variables,
    sketching causal loop diagrams, formulating a
    dynamic hypothesis
  • Designing model with software tool, e.g. Powersim
    Studio
  • Verifying and validating model
  • Tuning model
  • Testing model looking for policies
  • Optimization, risk assessment, risk management
  • last, not least, organizational learning

26
System Dynamics Stock-and-Flow Diagrams
  • System dynamic models are visualized through
    diagrams, the icons stocks, flows, auxiliary
    variables and constants having semantic
    content, i.e. specific topological and
    mathematical properties.

Constants (actually parameters)
Stock, cumulated by inflows and de-cumulated by
outflows
Information links, expressing dependencies
Model sector
Flow, here an inflow
Auxiliary variables
27
System Dynamics Stock-and-Flow Diagrams
  • System dynamic models typically contain physical
    processes, information flow, human aspects, soft
    factors, formation of perceptions and
    expectations and delays.

Physical processes, i.e. how staff comes in and
out of the project
Information flow, e.g. how desired workforce
affects hiring
Workforce adjustment time depends on human
decisions and market conditions
28
System Dynamics Stock-and-Flow Diagrams
Formation of perception soft factors (time to
perceive productivity), soft relationships
(formation of expectation)
  • System dynamic models typically contain physical
    processes, information flow, human aspects, soft
    factors, formation of perceptions and
    expectations and delays.

Show model
29
System Dynamics Modeling Perceptions and Delays
  • Human behavior and decision-making is based on
    perceptions of reality rather than reality
    itself.
  • Examples
  • Link to Boom and bust in high-velocity industries
  • Link to Project management
  • Link to Erosion of security standards

30
Modeling Erosion of Security Standards
31
Modeling Erosion of Security Standards
Perceived risk is out of phase with actual
(current) risk due to a perception delay. Wrong
perceptions lead to increasing actual risk.
Accidents happen with increasing probability when
current risk enters the accident zone
Accidents
32
Modeling Erosion of Security Standards
Conditioning of higher compliance only occurs
during a short interval in a "risk perception
cycle." Misperception of risk and the absence of
accidents due to secure technology act during
a longer interval to de-condition desired
behavior (extinction zone).
RETURN to examples of modeling perceptions
33
Modeling Perceptions
  • How does a project manager assess the
    productivity of staff? In a large-scale project
    one has several important factors affecting
    productivity
  • Tasks apparently completed are reported and
    accepted by management as being completed
    further down the road some of the tasks turn out
    to be faulty and must be reclassified as rework
  • Existing staff experience increases, thus leading
    to higher productivity
  • New hires dilute experience and require
    counseling from experienced staff, both aspects
    decreasing average productivity
  • All these factors generate information that
    changes the project managers perception of
    staff productivity. Perception can be seen as a
    smoothing of information (Change in perceived
    productivity) with a characteristic (individually
    different) time constant (Smoothing time)

Show model
34
Structures and Behavior
Structure drives model behavior over time
Issue Identification and Brainstorming
Events
Historical Results and Patterns of Behavior
Behavior
Simulation
Structure
35
Feedback and Behavior
Feedback loops are linked to specific kinds of
behavior
Basic Behavior Patterns
All behavior involving feedback is made up of
combinations of these behavior patterns.
36
Diverging behavior
  • Created by positive feedback loops

The higher the population, the more births, which
in turn leads to increased population (over time)
Debt with compounding interest (no installments)
37
Converging behavior
  • Created by negative feedback loops

Production gradually empties reservior, causing
reservior pressure to drop and production to
decline
The higher the quality gets, the more difficult
it gets to increase the quality further
38
Oscillating behavior
  • Created by negative feedback loop involving major
    delay

Inventories typically fluctuate since it takes
time before a decision to correct the inventory
will result in new products being received
(production and delivery delays).
39
S-shaped behavior
  • Caused by shift in feedback loop dominance from a
    positive loop to a negative loop

Positive loop
Negative loop
Phase 1
Phase 2
In the first phase sales grow exponentially due
to the word-of-mouth effect. As the market gets
saturated, sales decline.
40
Types of System Dynamics ModelsManagerial View
of the Enterprise
Strategic
Tactical
2 10 years Horizon
From 25,000
From 10,000
Operations
1 2 years Horizon
Hours/Days/ Weeks/Months
From 1,000
Length of simulation run
To Years
From Days
Jump to Learning in Complex Domains
41
Why Business Simulation?
Objectives of business simulations
High
Decision Complexity
Low
High
Development Complexity
42
Varieties of business simulations
Different types of business simulators for use at
various levels of the organizational structure.
Value Communication Simulators
C- level
Integrated Decision-Support Simulators
Middle managers
Department Managers
Customers
Suppliers
Line Supervisors Systems Operators
Stakeholders
Training Simulators
43
Issues Domain
Issues Domain
Levels of Management Planning Decision-making
High
Strategic (Planning) Long-term
Decision Complexity Risk Magnitude
Tactical (Control) Medium-Term
Operational (Execution) Short-term
Low
44
Implementation Process
45
The Decision Circle
46
Examples of Integrated SolutionsSEM-BPS Dataset
SEM-BPS dataset provides realistic input data to
business simulations
Industry Specific Models
Enterprise Data Warehouse
47
Examples of Integrated SolutionsData Manager
Data Manager approach lets users connect to
databases and import/export Powersim variables.
  • Simple, custom-built control panel gives
    capability to
  • send database info to Studio at the start of a
    time step,
  • advance the Powersim simulation model, and
  • transfer data back from Studio to the database.
  • Connects to any SQL/ODBC database (e.g. Oracle).
  • Uses a mapping database (implemented with MS
    Access) to link database queries/fields to
    Powersim variables.
  • Implemented in Visual Basic.

48
Examples of Integrated SolutionsWeb delivery
  • The interface is a mix of DHTML and JavaScript

User Interface DHTML/JavaScript
Client
  • All communication between client and server is
    HTTP

Presentation Tier
HTTP
  • Active Server Pages (ASP) is used to control the
    server objects
  • The UI Dependent Objects implement all business
    logic for the UI objects
  • The PS Model objects are used to access Engine.
  • The Data Objects are used to ensure object
    persistence and for historical and live data.
  • Powersim Engine runs 1..n instances of a
    simulation requested by the PS Model Objects

ASP Interface Server Side VBScript
UI-centric Objects Server installed DLL
Business Tier
Data-centric Objects Server installed DLL
PS Model Objects Server installed DLL
OLE DB/ADO
COM/DCOM
Powersim Engine Server installed OCX and model
file
Enterprise Databases
Representation Tier
Server
49
Issues
  • Characteristics of Enterprise Challenges
  • Dynamic Complexity The Logic of Failure
  • System DynamicsMethods and Applications
  • Learning in Complex Domains
  • Organizational Learning
  • Single-loop learning
  • Double-loop learning
  • Virtual worlds and double-loop learning

50
Single-loop Learning
51
Double-loop Learning
Reality domain
52
Virtual Worlds and Double-loop Learning
Reality domain
Information feedback
Decisions
Mental models
Policy
53
Issues
  • Characteristics of Enterprise Challenges
  • Dynamic Complexity The Logic of Failure
  • System DynamicsMethods and Applications
  • Learning in Complex Domains
  • Organizational Learning
  • Fragmentation of Knowledge
  • Group Modeling and Knowledge Capture
  • Shared knowledge
  • Memory of the Future
  • Improving Mental Models

54
Organizational Learning Fragmented Knowledge
  • Can anyone of you make a humble pencil? (In the
    sense of setting up a pencil factory from scratch
    in a new planet with the same resources the
    Earth to be colonized with an expedition on a
    spaceship.)
  • Can anybody on Earth solve that task?
  • No! A wonderful essay (I pencil by Leonard E
    Read see http//209.217.49.168/vnews.php?nid316
    ) convincingly shows that no one knows how to
    make a pencil. Rather, hundreds of thousands of
    different knowledge fragments have to be pulled
    together by all kind of mechanisms teamwork,
    market mechanisms, demand supply, etc in
    order to make a pencil or by that matter any
    product.
  • Knowledge is fragmented. The great economist
    Friedrich von Hayek wrote
  • Economics has long stressed the division of
    labour But it has laid much less stress on the
    fragmentation of knowledge, on the fact that each
    member of society can have only a small fraction
    of the knowledge possessed by all, and that each
    is therefore ignorant of most of the facts on
    which the working of society rests. Yet it is the
    utilisation of much more knowledge that anyone
    can possess, and therefore the fact that each
    moves within a coherent structure most of whose
    determinants are unknown to him, that constitutes
    the distinctive feature of all advanced
    civilisations.

55
Organizational Learning Group Modeling and
Knowledge Capture
  • Enterprise challenges mostly span across many
    fragmented knowledge domains, including knowledge
    found outside of the enterprise proper.
  • Hence, group modeling processes are necessary
  • In addition, much is still unknown. Hayek again
  • Complete rationality of action demands
    complete knowledge of all the relevant facts. A
    designer or engineer needs all the data and full
    power to control or manipulate them if he is to
    organize the material objects to produce the
    intended result. But the success of any action in
    society depends on more particular facts than
    anyone can possibly know. And our whole
    civilization in consequence rests, and must rest,
    on our believing much that we cannot know to be
    true
  • Implying that data mining, knowledge capturing
    processes, including discovey processes are
    needed and that a substantial proportion of
    assumptions (beliefs) must be made.

56
Organizational Learning Shared Knowledge and
Memory of the Future
  • The very development of a system dynamic model of
    an enterprise challenge leads to shared knowledge
    for the client.
  • System dynamic models should not be used as
    predictive tools
  • rather, they are tools to explore scenarios
    (answering what-if questions), thus creating
    Memory of the Future (term coined by the Lund
    neurologist, professor Dr David Ingvar, 1924,
    2000).
  • The richer such Memory of the Future (e.g. by
    identifying robust policies those working under
    a wide variety of conditions), the better.
  • Ultimately, the objective is improving mental
    models

57
Organizational Learning Improving Mental Models
  • Models should not be used as a substitute for
    critical thought, but as a tool for improving
    judgment and intuition Improving the mental
    models upon which decisions are based is the
    proper goal of computer modeling.
  • John D. Sterman A Skeptics Guide to Computer
    Models
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