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



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



IssuesIssues

1.Characteristics of Enterprise Challenges

2. Dynamic Complexity:”The Logic of Failure”




Examples of Enterprise Challenges

Analysis Main Conclusions



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)



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.



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



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



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



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)



IssuesIssues


2.Dynamic Complexity – ”The Logic of Failure”




About Dynamic Complexity

The Logic of Failure



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”)



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



IssuesIssues






About System Dynamics

Model development Modeling perceptions

& delays Structure and

behavior Types of system

dynamics models Integrated Solutions



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



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.



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).



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



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




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




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



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



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



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



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



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)



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



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).



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



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



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



“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



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



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



The Decision CircleThe Decision Circle

Analyze

Implement Strategy Data collection

Business System

Analyze

Simulate

Compare/Evaluate

ModelBusiness

Model



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

…...



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.



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



IssuesIssues



3. System Dynamics:Methods and Applications

4.Learning in Complex Domains


Single-loop learning Double-loop learning Virtual worlds and

double-loop learning



Single-loop LearningSingle-loop Learning

Reality domain

Decisions

PolicyMental model

of realitydomain

Information feedback



Double-loop LearningDouble-loop Learning

Reality domain

Decisions

PolicyMental model

of realitydomain

Information feedback



Virtual Worlds and Double-loop LearningVirtual Worlds and Double-loop Learning

Reality domain

Decisions Information feedback

Policy Mental models

Virtual world



IssuesIssues



3. System Dynamics:Methods and Applications


5.Organizational Learning

Fragmentation of Knowledge

Group Modeling and Knowledge Capture

Shared knowledge ”Memory of the Future” Improving Mental

Models



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.»



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.



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:



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”

applying system dynamics to manage dynamic complexity

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