Present System Dynamics as methodology to

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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

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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

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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)

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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.

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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
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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

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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
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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
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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

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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.

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Cost time overruns Ingalls ShipbuildingRef.
JD Sterman, Business Dynamics, 2000
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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
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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
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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

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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
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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)

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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

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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)

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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

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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

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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

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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.

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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).

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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

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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
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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
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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
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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

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Modeling Erosion of Security Standards
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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
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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
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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
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Structures and Behavior
Structure drives model behavior over time
Issue Identification and Brainstorming
Events
Historical Results and Patterns of Behavior
Behavior
Simulation
Structure
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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.
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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)
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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
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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).
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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.
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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
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Why Business Simulation?
Objectives of business simulations
High
Decision Complexity
Low
High
Development Complexity
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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
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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
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Implementation Process
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The Decision Circle
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Examples of Integrated SolutionsSEM-BPS Dataset
SEM-BPS dataset provides realistic input data to
business simulations
Industry Specific Models
Enterprise Data Warehouse
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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.

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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
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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

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Single-loop Learning
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Double-loop Learning
Reality domain
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Virtual Worlds and Double-loop Learning
Reality domain
Information feedback
Decisions
Mental models
Policy
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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

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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.

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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.

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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

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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