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Why systems thinking?

Why systems thinking?. Because our logical deduction mechanisms are trained to induct linearly, not cyclically We don’t see the feedback loops Consequently, we don’t comprehend the opportunities for reinforcement or the consequences of limitations/constraints

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Why systems thinking?

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  1. Why systems thinking? • Because our logical deduction mechanisms are trained to induct linearly, not cyclically • We don’t see the feedback loops • Consequently, we don’t comprehend the opportunities for reinforcement or the consequences of limitations/constraints • Forrester: every decision, every action is embedded in an information feedback loop Systems Presentation at Ole Miss

  2. More motivation • We are immersed in and victims of structures that we have little awareness of • Causes and their effects are often spatially and temporally separated • Today’s problems are yesterday’s solutions • To make good decisions we need to understand dynamic complexity, not detail complexity Systems Presentation at Ole Miss

  3. Still more motivation • The integration that comes from the application of information technology is creating complexity at a frenetic pace • Out of the complexity comes the potential for chaos and catastrophe Systems Presentation at Ole Miss

  4. Key Benefits of the ST • A deeper level of learning • Far better than a mere verbal description • A clear structural representation of the problem or process • A way to extract the behavioral implications from the structure and data • A “hands on” tool on which to conduct WHAT IF

  5. Senge’s Five Disciplines • Personal Mastery • because we need to be the very best we can be • Mental Models • because these are the basis of all decision-making • Shared Vision • because this galvanizes workers to pursue a common goal • Team Learning • because companies are organized into teams • Systems Thinking • because this is only tool for coping with complexity

  6. Reinforcement • What were those experiments with rats??? Systems Presentation at Ole Miss

  7. The Saga of Peoples Air • A totally different airline • Founded in 1980 to provide low-cost, high-quality airline service to travelers in the Eastern U.S. • Grew to nation’s 5th largest carrier • Brought in a host of innovative human resource policies • In 1986 lost $133M in first six months, and was taken over by Texas Air

  8. What brought PEOPLES down?? • Many explanations • Some blame Burr’s “soft” people-oriented management policies • Some blamed the unions • Others blamed the use of Americans’ Sabre Reservation system • Load management could offer a limited number of low-cost seats while others were “full coach”

  9. What variables to blame? • Fleet variables • Human resource variables • Competitive factors • Financial variables • Policy Levers

  10. Fleet variables • Planes • Capacity of aircraft • Routes • Scheduled flights • Competitor routes • Service hours per plane per day • Fuel efficiency

  11. Service personnel Aircraft personnel Maintenance personnel Hiring Training Turnover Morale Productivity Experience Team management Job rotation Stock ownership Temporaries Human resources

  12. Competitive Factors • Market size • Market segments • Reputation • Service quality • Competitor service quality • Fares “load Management” • Competitor fares

  13. Revenues Profit Cost of plane operations Cost of service operations Cost of marketing Wages Stock price Growth rate Debt Interest Rate Financial variables

  14. Policy Levers • Buying planes • Hiring people • Pricing • Marketing expenditures • Service scope

  15. Enormous detail complexity • We could build a model that contained all of this detail • Or we could use the systems archetypes to disentangle this parable of complexity

  16. Systems Thinking basics • Peruse relevant literature • Talk to people knowledgeable about the problem • List relevant variables • Describe causal interactions between variables • Fully delineate the causal diagram • Draw behavior over time graphs Systems Presentation at Ole Miss

  17. Examples • Itch--scratch • population and growth rate of population • revenues, sales force size, sales • inventory, order rate, desired inventory, Systems Presentation at Ole Miss

  18. A single-sector Exponential growth Model Consider a simple population with infinite resources--food, water, air, etc. Given, mortality information in terms of birth and death rates, what is this population likely to grow to by a certain time?

  19. Exponentially growing population model • In 1900 there were just 1.65 billion people on the planet. Today, there are more than 6 billion people on the planet. Every year there are .04 births per capita and .028 deaths per capita. • The .04 births p er capita shall be referred to as a parameter called BIRTH RATE NORMAL Systems Presentation at Ole Miss

  20. Experiments with growth models • Models with only one rate and one state • Average lifetime death rates • cohorts • Models in which the exiting rate is not a function of its adjacent state • Including effects from other variables • ratios and table functions

  21. What do we have in terms of loops? • A growth loop certainly (reinforcing) • The airline, unlike WonderTech was investing in its capital equipment infrastructure • It was buying planes to accommodate the growth • A balancing loop

  22. What archetypes? • LIMITS TO GROWTH • SHIFTING THE BURDEN • Erodiing goals (standards) • The combination of these produces a third archetype • The Growth and Underinvestment Archetype • This was first seen in the WonderTech Scenario

  23. The Simplified Structure--p. 133

  24. The Simulation Structure--Reinforcing Loop

  25. The Simulation

  26. From Causal Diagram to Schematic (Stock & Flow) Diagram • Some simple causal models • Some associated schematic models • Some rules Systems Presentation at Ole Miss

  27. Can you construct the schematic model for this Causal model? Systems Presentation at Ole Miss

  28. We know what that is Systems Presentation at Ole Miss

  29. How about this one? Systems Presentation at Ole Miss

  30. We know what it is Systems Presentation at Ole Miss

  31. Some rules • There are two types of causal links in causal models • Information • Flow • Information proceeds from stocks and parameters toward rates where it is used to control flows • Flow edges proceed from rates to states (stocks) in the causal diagram always Systems Presentation at Ole Miss

  32. Loops • In any loop involving a pair of quantities/edges, • one quantity must be a rate • the other a state or stock, • one edge must be a flow edge • the other an information edge Systems Presentation at Ole Miss

  33. CONSISTENCY • All of the edges directed toward a quantity are of the same type • All of the edges directed away from a quantity are of the same type Systems Presentation at Ole Miss

  34. Rates and their edges Systems Presentation at Ole Miss

  35. Parameters and their edges Systems Presentation at Ole Miss

  36. Stocks and their edges Systems Presentation at Ole Miss

  37. Auxiliaries and their edges Systems Presentation at Ole Miss

  38. Outputs and their edges Systems Presentation at Ole Miss

  39. STEP 1: Identify parameters • Parameters have no edges directed toward them Systems Presentation at Ole Miss

  40. STEP 2: Identify the edges directed from parameters • These are information edges always Systems Presentation at Ole Miss

  41. STEP 3: By consistency identify as many other edge types as you can Systems Presentation at Ole Miss

  42. STEP 4: Look for loops involving a pair of quantities only • Use the rules identified above Systems Presentation at Ole Miss

  43. System Dynamics Software • STELLA and I think • High Performance Systems, Inc. • best fit for K-12 education • Vensim • Ventana systems, Inc. • Free from downloading off their web site: www.vensim.com • Robust--including parametric data fitting and optimization • best fit for higher education • Powersim • What Arthur Andersen is using

  44. What is system dynamics • A way to characterize systems as stocks and flows between stocks • Stocks are variables that accumulate the affects of other variables • Rates are variables the control the flows of material into andout of stocks • Auxiliaries are variables the modify information as it is passed from stocks to rates

  45. A DEMO

  46. Nature’s Templates: the Archetypes • Structures of which we are unaware hold us prisoner • The swimmer scenario • Certain patterns of structure occur again and again: called ARCHETYPES

  47. We are creating a “language” • reinforcing feedback and balancing feedback are like the nouns and verbs • systems archetypes are the basic sentences • Behavior patterns appear again in all disciplines--biology, psychology, family therapy, economics, political science, ecology and management • Can result in the unification of knowledge across all fields

  48. Recurring behavior patterns • Do we know how to recognize them? • Do we know how to describe them? • Do we know how to prescribe cures for them? • The ARCHETYPES describe these recurring behavior patterns

  49. The ARCHETYPES • provide leverage points, intervention junctures at which substantial change can be brought about • put the systems perspective into practice • About a dozen systems ARCHETYPES have been identified • All ARCHETYPES are made up of the systems building blocks: reinforcing processes, balancing processes, delays

  50. Before attacking the ARCHETYPES we need to understand simple structures • the reinforcing feedback loop • the balancing feedback loop • THE DEMO • Pages 520-525 in Austin/Burns--your handout

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