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Applying System Dynamics to Manage Dynamic Complexity
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Transcript of Applying System Dynamics to Manage Dynamic Complexity
Presented at Complexity Seminar in Lund, November 4-5, 2002 1
Agder University College
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
Applying System Dynamics to Manage Dynamic Applying System Dynamics to Manage Dynamic Complexity in Enterprises; Complexity in Enterprises; by Jose J Gonzalez; professor dt.techn., dr.rer.nat.; by Jose J Gonzalez; professor dt.techn., dr.rer.nat.; AUCAUC
Presented at Complexity Seminar in Lund, November 4-5, 2002 2
Agder University College
IssuesIssues
1. Characteristics of Enterprise Challenges
2. Dynamic Complexity – ”The Logic of Failure”
3. System Dynamics – Methods and Applications
4. Learning in Complex Domains
5. Organizational Learning
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Agder University College
IssuesIssues
1.Characteristics of Enterprise Challenges
2. Dynamic Complexity:”The Logic of Failure”
3. System Dynamics – Methods and Applications
4. Learning in Complex Domains
5. Organizational Learning
Examples of Enterprise Challenges
Analysis Main Conclusions
Presented at Complexity Seminar in Lund, November 4-5, 2002 4
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Characteristic of Enterprise/Public Characteristic of Enterprise/Public Challenges: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)
Presented at Complexity Seminar in Lund, November 4-5, 2002 5
Agder University College
Characteristic of Enterprise/Public Characteristic of Enterprise/Public Challenges:Challenges:”Managing” traffic pollution”Managing” 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.
Presented at Complexity Seminar in Lund, November 4-5, 2002 7
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Characteristic of Enterprise Challenges:Characteristic of Enterprise Challenges:Boom and BustBoom 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
Presented at Complexity Seminar in Lund, November 4-5, 2002 10
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Characteristic of Enterprise Challenges:Characteristic of Enterprise Challenges:Large-scale projectsLarge-scale 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
Presented at Complexity Seminar in Lund, November 4-5, 2002 15
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Characteristic of Enterprise Challenges:Characteristic of Enterprise Challenges:Erosion of standardsErosion 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
Presented at Complexity Seminar in Lund, November 4-5, 2002 17
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Characteristic of Enterprise Challenges:Characteristic of Enterprise Challenges:Rig managementRig 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)
Presented at Complexity Seminar in Lund, November 4-5, 2002 18
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IssuesIssues
1. Characteristics of Enterprise Challenges
2.Dynamic Complexity – ”The Logic of Failure”
3. System Dynamics – Methods and Applications
4. Learning in Complex Domains
5. Organizational Learning
About Dynamic Complexity
The Logic of Failure
Presented at Complexity Seminar in Lund, November 4-5, 2002 19
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Dynamic Complexity – ”The Logic of Dynamic Complexity – ”The Logic of Failure”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”)
Presented at Complexity Seminar in Lund, November 4-5, 2002 20
Agder University College
Dynamic Complexity – ”The Logic of Dynamic Complexity – ”The Logic of Failure”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
Presented at Complexity Seminar in Lund, November 4-5, 2002 21
Agder University College
IssuesIssues
1. Characteristics of Enterprise Challenges
2. Dynamic Complexity – ”The Logic of Failure”
3. System Dynamics – Methods and Applications
4. Learning in Complex Domains
5. 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 DynamicsAbout 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
Presented at Complexity Seminar in Lund, November 4-5, 2002 23
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Qualitative System DynamicsQualitative 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.
Presented at Complexity Seminar in Lund, November 4-5, 2002 24
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System Dynamics MethodsSystem 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).
Presented at Complexity Seminar in Lund, November 4-5, 2002 25
Agder University College
Model developmentModel development
Model development involves the following activities (that can be iterated):
Problem definition and articulationWho cares and why?Problem symptomsDesired behaviorPolicy 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
Presented at Complexity Seminar in Lund, November 4-5, 2002 26
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System Dynamics: Stock-and-Flow System Dynamics: Stock-and-Flow DiagramsDiagrams
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.
Stock, cumulated by
inflows and de-cumulated by
outflowsModel sector
’Constants’ (actually
parameters)
Information links,
expressing dependencies
Auxiliary variables
Flow, here an inflow
Presented at Complexity Seminar in Lund, November 4-5, 2002 27
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System Dynamics: Stock-and-Flow System Dynamics: Stock-and-Flow DiagramsDiagrams
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
Presented at Complexity Seminar in Lund, November 4-5, 2002 28
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System Dynamics: Stock-and-Flow System Dynamics: Stock-and-Flow DiagramsDiagrams
System dynamic models typically contain physical processes, information flow, human aspects, soft factors, formation of perceptions and expectations and delays.
Formation of perception: soft factors (time to
perceive productivity), soft
relationships (formation of expectation)
Show model
Presented at Complexity Seminar in Lund, November 4-5, 2002 29
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System Dynamics: Modeling Perceptions and System Dynamics: Modeling Perceptions and DelaysDelays
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
Presented at Complexity Seminar in Lund, November 4-5, 2002 33
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Modeling PerceptionsModeling 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
manager’s 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
Presented at Complexity Seminar in Lund, November 4-5, 2002 34
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Structures and BehaviorStructures and Behavior
Events
Behavior
Structure
Issue Identification and Brainstorming
Historical Results and Patterns of Behavior
Simulation
Structure drives model behavior over time
Presented at Complexity Seminar in Lund, November 4-5, 2002 35
Agder University College
Basic Behavior Patterns
Diverging
Converging
S-Shaped
Oscillations
All behavior involving feedback is made up of combinations of these behavior patterns.
Feedback and BehaviorFeedback and Behavior
Feedback loops are linked to specific kinds of behavior
Presented at Complexity Seminar in Lund, November 4-5, 2002 36
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Diverging behaviorDiverging 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)
Presented at Complexity Seminar in Lund, November 4-5, 2002 37
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Converging behaviorConverging behavior
Created by negative feedback loops
Production gradually empties reservior, causing reservior pressure to drop and production to declineThe higher the quality gets, the more difficult it gets to increase the quality further
Presented at Complexity Seminar in Lund, November 4-5, 2002 38
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Oscillating behaviorOscillating 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).
Presented at Complexity Seminar in Lund, November 4-5, 2002 39
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S-shaped behaviorS-shaped behavior
Caused by shift in feedback loop dominance from a positive loop to a negative loop
In the first phase sales grow exponentially due to the word-of-mouth effect.
As the market gets saturated, sales decline.
Positive loopNegative loop
Phase 1 Phase 2
Presented at Complexity Seminar in Lund, November 4-5, 2002 40
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Types of System Dynamics Models:Types of System Dynamics Models:Managerial View of the EnterpriseManagerial View of the Enterprise
From25,000’
From10,000’
From1,000’
2 – 10 years Horizon1 – 2 years Horizon
Hours/Days/
Weeks/Months
Strategic
Tactical
Length of simulation runFrom Days To Years
Operations
Jump to Learning in Complex Domains
Presented at Complexity Seminar in Lund, November 4-5, 2002 41
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Development Development ComplexityComplexity
Decis
ion
D
ecis
ion
C
om
ple
xit
Com
ple
xit
yy
$$$Operational
Planning
$$Operational Simulator
$Operational Simulation
$$$$Tactical Planning
$$$Tactical
Simulator
$$Tactical
Simulation
$$$$$$Strategic Planning
$$$$Strategy Simulator
$$$Strategy
Simulation
Simulation Purpose/Use
ValueCommunication
{Designed for UseOnce or Twice}
ManagementTraining{Designed forPeriodic Use}
IntegratedDecisionSupport
{Designed forContinuous Use}
Levels of Management
Planning &Decision-making
Strategic(Planning)
{Long-term}
Tactical(Control)
{Medium-Term}
Operational(Execution)
{Short-term}
+
+
High
High
Low
Why Business Simulation?Why Business Simulation?
Objectives of business simulations
Presented at Complexity Seminar in Lund, November 4-5, 2002 42
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“C”-level
Department
Managers
Middle manager
s
Different types of business simulators for use at various levels of the organizational structure.
Integrated Decision-Support
Simulators
Training Simulators
Line Supervisors & Systems Operators
Customers
Suppliers
Value Communication
Simulators
Stakeholders
Varieties of business simulationsVarieties of business simulations
Presented at Complexity Seminar in Lund, November 4-5, 2002 43
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Deci
sion
D
eci
sion
C
om
ple
xit
y &
Ris
k C
om
ple
xit
y &
Ris
k M
ag
nit
ud
eM
ag
nit
ud
eIssues Domain
Strategic(Planning)
{Long-term}
Tactical(Control)
{Medium-Term}
Operational(Execution)
{Short-term}
Levels of Management
Planning &Decision-making
Issues DomainIssues Domain
Facility Planning, Risk Assessment
Corporate Planning & Strategy
Strategic Alliances
Emerging Markets & Tech.
E-Business
Change Mgt & Growth Strategies
Asset & Portfolio, Shareholder Value Mgt
Enterprise security & safety Supply & Value chain Mgt
Project Mgt
Inventory Control & Mgt
Production & Distribution Mgt
HR & Knowledge Mgt
Product & Marketing Strategy
Satisfaction Measurement
Capacity Adjustment
Process Analysis
Financial Analysis
Performance Measurements
Quality Measurement
Scheduling
Cost/Benefit & Yield Analysis
Cycle Time Analysis
Market Analysis & ForecastingLowLow
HighHigh
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Implementation ProcessImplementation ProcessImplementation ProcessImplementation Process
Knowledge TransferKnowledge TransferKnowledge TransferKnowledge Transfer
Knowledge Knowledge DeliveryDelivery
Knowledge Knowledge DeliveryDelivery
KnowledgeKnowledge PresentationPresentation
KnowledgeKnowledge PresentationPresentation
1 2 3 4 5 6 7 N (Weeks/Months)
System DiagnosisSystem Diagnosis Determine model use/
purpose Establish connection
btw model & everyday realities of managerial life
Determine how the model can be integrated into the political, cultural, & managerial values of the firm
Specify study objectives & define system boundary
Specify model assumptions & performance measures
Determine alternative scenarios to investigate
Search, discover, & interpret facts
Describe the system to be simulated & trace effects back to causes
DesignDesign & & Build Build SLESLE
Design the look & feel of the GUI
Build GUI – VB, DHTML / Java Script, Active-X, etc. programming
Design & build Active Server Pages (ASP) objects
Design & build database*
Integrate the simulation model with the GUI
Integrate audio & video files
Test the integrated simulation model for consistency and validity
Integrate application into client systems
Test application on different platforms
Model FormulationModel Formulation Define system components Identify & classify system
variables Specify experimental
design – initial system conditions, parameter values, reference modes, etc.
Create simulation model of the decision policies, information sources, and interactions of the system components
Prepare input data and parameters
Validate model structure and behavior
Formulate experimental conditions
Conduct initial policy test runs
Tune and optimize the model
Business Policy Business Policy AnalysisAnalysis
Simulate the model under different assumptions to generate the system behavior through time
Compare results with available knowledge about the actual system
Redesign organizational relationships and policies that can be altered in the actual system
Conduct enough scenario runs to evaluate all known alternatives
Recommend suitable line of action to be followed
Integrated Training / Decision Support Tool
Conceptual Model Simulation modelManagement decisions guided by knowledge
Mths/Yrs
1-12/1-5
Sustainable Knowledge
System System Update Update
Agreement Agreement to:to:
Conduct Conduct routine routine business business scenario scenario simulationssimulations
Create new Create new model model structures to structures to include include business business changeschanges
Extend the Extend the scope of model scope of model to address new to address new business issuesbusiness issues
KnowledgeKnowledge RepresentationRepresentation
KnowledgeKnowledge RepresentationRepresentation
Knowledge Knowledge MaintenanceMaintenance
Knowledge Knowledge MaintenanceMaintenance
KnowledgeKnowledgeExtractionExtraction
KnowledgeKnowledgeExtractionExtraction
Presented at Complexity Seminar in Lund, November 4-5, 2002 45
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The Decision CircleThe Decision Circle
Analyze
Implement Strategy Data collection
Business System
Analyze
Simulate
Compare/Evaluate
ModelBusiness
Model
Presented at Complexity Seminar in Lund, November 4-5, 2002 46
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Examples of Integrated Solutions:Examples of Integrated Solutions:SEM-BPS DatasetSEM-BPS Dataset
SEM-BPS dataset provides realistic input data to business simulations
Industry Specific Models
Enterprise-wide data
Simulated results
Reporting Templates
Enterprise Data Warehouse
29
Company specific Business Model
Build model
Tune & Optimize
Custo
mer N
eed
s
Distrib
utio
n
Mark
etin
g &
Sale
s
Financia
l
…...
Presented at Complexity Seminar in Lund, November 4-5, 2002 47
Agder University College
Examples of Integrated Solutions:Examples of Integrated Solutions:Data ManagerData 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).
Powersim Data Manager
Oracle ODBC Client
Studio Model
OracleServer
MappingDatabase
ODBC
ODBC
PSAPI
Desktop Computer
Database Server (Unix)
Uses a mapping database (implemented with MS Access) to link database queries/fields to Powersim variables.
Implemented in Visual Basic.
Presented at Complexity Seminar in Lund, November 4-5, 2002 48
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Examples of Integrated Solutions:Examples of Integrated Solutions:Web deliveryWeb delivery
• 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
• The interface is a mix of DHTML and JavaScript
• All communication between client and server is HTTP
Client
User InterfaceDHTML/JavaScript
Server
HTTP
OLE DB/ADO
UI-centric ObjectsServer installed DLL
Data-centric ObjectsServer installed DLL
ASP InterfaceServer Side VBScript
PS Model ObjectsServer installed DLL
Powersim EngineServer installed OCX
and model file
COM/DCOM
Presentation Tier
Business Tier
Representation Tier
Enterprise Databases
Presented at Complexity Seminar in Lund, November 4-5, 2002 49
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IssuesIssues
1. Characteristics of Enterprise Challenges
2. Dynamic Complexity – ”The Logic of Failure”
3. System Dynamics:Methods and Applications
4.Learning in Complex Domains
5. Organizational Learning
Single-loop learning Double-loop learning Virtual worlds and
double-loop learning
Presented at Complexity Seminar in Lund, November 4-5, 2002 50
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Single-loop LearningSingle-loop Learning
Reality domain
Decisions
PolicyMental model
of realitydomain
Information feedback
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Double-loop LearningDouble-loop Learning
Reality domain
Decisions
PolicyMental model
of realitydomain
Information feedback
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Virtual Worlds and Double-loop LearningVirtual Worlds and Double-loop Learning
Reality domain
Decisions Information feedback
Policy Mental models
Virtual world
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IssuesIssues
1. Characteristics of Enterprise Challenges
2. Dynamic Complexity – ”The Logic of Failure”
3. System Dynamics:Methods and Applications
4. Learning in Complex Domains
5.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 Organizational Learning: Fragmented KnowledgeKnowledge
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?nid=316) 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:Organizational Learning: Group Modeling and Knowledge CaptureGroup 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:Organizational Learning: Shared Knowledge and Memory of the FutureShared 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:Organizational Learning: Improving Mental ModelsImproving 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”