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System Dynamics: System Dynamics: Uses, Benefits, PerspectivesUses, Benefits, Perspectives

Prof. Jose J. GonzalezAgder College & Powersim ASGrimstad, Norway

Agder College

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Content

•About System Dynamics (Presentation)•Modelling with Powersim Constructor (Demonstration)•Developing Internet Simulators with Powersim Metro (Demonstration)

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What is System Dynamics

•Deals with time-dependent behaviour of managed systems•Origins in servomechanisms engineering and management•Perspective based on information feedback and mutual or recursive causality to understand the dynamics of complex technological, biological, and social systems.

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Uses of System Dynamics

•Corporate planning and policy design•Public management and policy•Process development and optimisation•Dynamic decision making•Biological and medical modelling•Energy and the environment•Complex nonlinear dynamics•Theory development in the natural and social sciences

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System dynamic modelsEffort Perceived Remaining, Hiring and Scheduling

Net_hiring_RATE

Time_remaining

Workforce

Workforce_adj_time

Perc_task_compl_rate

Effort_perc_remaining

Perc_productivity

Desired_workforce Indicated_workforce

Willingness_to_Chng_Workforce

Physical process (flow of workforce)

Physical process (progress of project)

Information flow (Desired_workforce affects recruitment)

Perception (how far the project has proceeded)

Pressure as consequence of perceptions (delays in project affect desired workforce)

Soft variable

Feedback

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Example: Pollution Control(courtesy Prof. Graham Winch)

•Air pollution in Mexico City is amongst the worst in the world.•The authorities decided to limit vehicle use – every car has a colour-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.

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Example: Pollution Control (courtesy Prof. Graham Winch)

Rationing

PollutionTarget

Pollution

Car Usage

TargetPollution

TravelNeeds

Numberof Cars

Week-end Use&

Age Effect

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Example: Project management

•Large (even) huge time and costs overruns in large projects•Consistent experiences in different branches and in all countries•In Norway e.g.:

– Overruns of hundreds of millions in software

– Overruns of billions in building projects– Overruns of tens of billions in offshore– …time overruns of many months and up

to several years, incl. never-completed projects

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Example: (…Project management)

•Traditional methods for project management fail, because of

– Static approach– Failure to capture feedback– “Primacy of map over terrain”

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Example: (…Project management)

•Mapping software project management: Sector diagram

Human Resource

Management

Controlling Planning

SoftwareProduction

ScheduleTasks

Completed

EffortRemaining

WorkforceNeeded

WorkforceAvailable

ProgressStatus

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Example: (…Project management)

•Mapping software project management: Causal-loop diagram

Workforce[men]

Nethiring

[men/month]

+

-

(-)

Desired workforce

[men]

Task completion

rate[tasks/month]

+

Productivity[tasks/man/month]+

+

Tasks completed

[tasks]

+

Tasks remaining

[tasks]

-

Initialproject

size[tasks]+

Effortremaining

[man-months]

+

Productivity[tasks/man/

month]-

+

Scheduled completion time

[months]

-

(-)

Scheduledtime

remaining[months]

+Time

elapsed[months]

-

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Example: (…Project management)

•Mapping software project management: Stock-and-flow diagram

Human Resource Management Software Production – Manpower Allocation Sector

Some useful definitions

Ave_Daily_MP_pr_Staff

Total_WF

Total_WF

Experienced_WFAve_Daily_MP_pr_Staff

Daily_MP_for_TrainingDaily_MP_for_Training

Rework_MP_Needed_pr_Error

Detected_Errors

Fract_Effort_for_System_Testing

Workforce_Needed

Ceiling_New_Hirees

Assim_Rate_New_Empl

Ave_Assim_Delay

Ceiling_Tot_WF

Cum_Train_Man_days

Daily_MP_for_TrainingFraction_WF_ExpFull_time_Equiv_WF

Full_time_Equiv_Exp_WF

Exp_Empl_Transfer_Rate

Hiring_Delay

Hiring_Rate

Most_New_Hirees_pr_Exp_Staff

New_Empl_Transfer_Rate

Exp_Empl_Quit_Rate

Transfer_Rate_Out_Project

Transfer_Delay_People_Out

Trainers_pr_New_Employee

Experienced_WF

Workforce_Gap

New_Workforce

Workforce_Sought

Rate_of_Perc_RW_MP

Adj_in_Plan_Fract_of_MP_for_QA

Ave_Daily_MP_pr_Staff

Act_Fract_of_MP_for_QA

Cum_Dev_Man_days

Rate_of_Dev_Man_days

Cum_QA_Man_days

Cum_Rework_Man_days

Cum_Man_days_Expended

Des_Error_Correct_Rate

Desired_Rework_Delay

Daily_MP_Avail_after_Training

Daily_MP_for_Dev_Test

Daily_MP_for_QA

Daily_MP_Alloc_for_Rework

Daily_MP_for_Softw_ProdPerc_Rework_MP_Needed_pr_Error

Time_to_Adj_Perc_Rework_MP

Total_Daily_Manpower

Pcent_Job_Act_Worked

Ave_Employment_Time

Sched_Pressure

Quality_Objective

Plan_Fract_of_MP_for_QA

Total_WF

Team_Size_at_Start

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Example: (…Project management)

•Why are large projects difficult to manage?

– Complexity (structural and dynamical)– Feedback– Delayed perceptions– Nonlinearities

•E.g. In what proportion should one allocate available software engineers to coding, testing and rework?

– Answer depends on productivity parameters, project status, etc.

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Example: (…Project management)

•Often behaviour is counter-intuitive•E.g.:Trying to get project on schedule

Experienced Workforce

NewWorkforce

+

Productivity

+

+

Hiring

+

In Training

+

–

Experienced Workforce

NewWorkforce

+

Productivity

+

+

Hiring

+

+

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Example: (…Project management)

•Software Project Management model provides insights and lets you test robust strategies

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Evolving Uses of System Dynamics

•In recent times system dynamics has emerged as a powerful tool for organisational learning:

– Knowledge capture– Better utilisation of knowledge– “Double-loop learning”

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‘Model’ of Knowledge in a Reality Domain

Partially erroneous knowledge

Networks utilising fragmented knowledge

Fragmented, individual knowledge Ideal

knowledge

Hayek

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Growth of Knowledge Recruitment

Increased individual knowledge

Feedback (correction of misperceptions)

Larger and stronger networks

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Single-loop Learning and Acting in Dynamic Complex Domains

Reality domain

Decisions

PolicyMental model

of realitydomain

Information feedback

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Double-loop Learning and Acting in Dynamic Complex Domains

Reality domain

Decisions

PolicyMental model

of realitydomain

Information feedback

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The Logic of Failure

• Research by Dörner et al. about thinking, decision-making and acting in complex domains

• Most people fail and the behaviour patterns are (quite) ‘universal’

• … but a few master complexity

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The Logic of Failure

• The “Tanaland” Scenario

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The Logic of Failure

• Typical outcome

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The Logic of Failure

• Exceptional outcome

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The Logic of Failure

• Typical (“linear”) reasoning: Killing of apes will lead to greater harvests

Apes

Harvests-

-

Hunting

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The Logic of Failure

• Untypical thinking in causal networks

Apes

Harvests-

-

Hunting

-Insects

-

Hungryleopards

Cattle- -

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The Logic of Failure

• “The Logic of Failure” based on 20+ years research:– Dörner, Dietrich: Die Logik des Mißlingens.

Reinbek: Rowohlt, 1989.

– Dörner, Dietrich: (1996). The logic of failure: Why things go wrong and what we can do to make them right. 1st American ed. New York: Metropolitan Books, 1996.

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Thinking and Acting in a Reality Domain

Reality domain

Decisions Information feedback

Policy Mental models

Real world•“One shot policy”

Unknown structureMany componentsHigh degree of couplingDynamicsFeedbackDelaysNot reproducible

Decisions in the real world•Implementation problems•Inconsistency•Group psychology (game playing)•Short-time gain decisive

Feedback in the real world•Incomplete or lacking•Ambiguous•Delayed•Biased, masked or erroneous

Real world•Late updating of mental models

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Enhanced Insight via Virtual Worlds

Reality domain

Decisions Information feedback

Policy Mental models

Virtual world

Feedback in the real world•Incomplete or lacking•Ambiguous•Delayed•Biased, masked or erroneous

Decisions in the virtual world•Perfect implementation•Consistency•No group psychological processes•Insight and learning aremain goals•Long-time aspects

Feedback in the virtual world•Concentrated, complete•Unambiguous information•Immediate response•Accurate and correct

Decisions in the real world•Implementation problems•Inconsistency•Group psychology (game playing)•Short-time gain decisive

Real + virtual world•Strategy•Structure•“What-if” analysis•Long-time consequences via simulation•Sensitivity analysis

Real + virtual world•“Causal loop analysis”•Mapping of feedback•Disciplined reasoning•Group learning

•Unknown structure•Many components•High degree of coupling•Dynamics•Feedback•Delays•Not reproducible

•Known structure•Open for insight•GUI with graduated complexity•Reproducible experiments

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Benefits of System Dynamics

SD Characteristic Organisation concerns

System viewpoint Organisation challenge

Feedback analysis Consequence of actions

Dynamic modelling Knowledge gain, concern withfuture

Simulation Testing ideas

Optimisation Robustness against uncertainty

Transparency of causal-loop diagram& simulation model

Organisational learning

Fast simulation What-if analysis