Learning, Unlearning and Re-Learning Curves

Learning, Unlearning and Re-Learning Curves

by Alan Jones
Released September 2018
Publisher(s): Routledge
ISBN: 9781351661461

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Book description

Learning is an empirical phenomenon whereby people or organisations undergo a level of efficiency improvement with recurring tasks. Alan Jones pragmatic guide to this important element within estimating introduces two key learning curve models: Wright and Crawford and explains where, how and when to apply them.

Table of contents

  1. Cover
  2. Half Title
  3. Title
  4. Copyright
  5. Dedication
  6. Contents
  7. List of Figures
  8. List of Tables
  9. Foreword
  10. 1 Introduction and objectives
    1. 1.1 Why write this book? Who might find it useful? Why five volumes?
      1. 1.1.1 Why write this series? Who might find it useful?
      2. 1.1.2 Why five volumes?
    2. 1.2 Features you'll find in this book and others in this series
      1. 1.2.1 Chapter context
      2. 1.2.2 The lighter side (humour)
      3. 1.2.3 Quotations
      4. 1.2.4 Definitions
        1. 1.2.5 Discussions and explanations with a mathematical slant for Formula-philes
        2. 1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes
      5. 1.2.7 Caveat augur
      6. 1.2.8 Worked examples
      7. 1.2.9 Useful Microsoft Excel functions and facilities
      8. 1.2.10 References to authoritative sources
      9. 1.2.11 Chapter reviews
    3. 1.3 Overview of chapters in this volume
    4. 1.4 Elsewhere in the ‘Working Guide to Estimating & Forecasting’ series
      1. 1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling
      2. 1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff
      3. 1.4.3 Volume III: Best Fit Lines and Curves, and Some Mathe-Magical Transformations
      4. 1.4.4 Volume IV: Learning, Unlearning and Re-Learning Curves
      5. 1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models
    5. 1.5 Final thoughts and musings on this volume and series
    6. References
  11. 2 Quantity-based Learning Curves
    1. 2.1 A brief history of the Learning Curve as a formal relationship
    2. 2.2 Two basic Learning Curve models (Wright and Crawford)
      1. 2.2.1 Wright Cumulative Average Learning Curve
      2. 2.2.2 Crawford Unit Learning Curve
      3. 2.2.3 Wright and Crawford Learning Curves compared
      4. 2.2.4 What's so special about the doubling rule?
      5. 2.2.5 Learning Curve regression – What appears to be Wright, may in fact be wrong!
    3. 2.3 Variations on the basic Learning Curve models
      1. 2.3.1 DeJong Unit Learning Curve
      2. 2.3.2 DeJong-Wright Cumulative Average Hybrid Learning Curve
      3. 2.3.3 Stanford-B Unit Learning Curve
      4. 2.3.4 Stanford-Wright Cumulative Average Hybrid Learning Curve
      5. 2.3.5 S-Curve Unit Learning Curve
      6. 2.3.6 S-Curve-Wright Cumulative Average Hybrid Learning Curve
    4. 2.4 Where and when to apply learning and how much?
      1. 2.4.1 To what kind of task can a Learning Curve be applied?
      2. 2.4.2 Additive and non-additive properties of Learning Curves
      3. 2.4.3 Calibrating or measuring observed learning
      4. 2.4.4 What if we don't have any actuals? Rules of Thumb rates of learning
    5. 2.5 Changing the rate of learning – Breakpoints
      1. 2.5.1 Dealing with a breakpoint in a Unit Learning Curve calculation
      2. 2.5.2 Dealing with a breakpoint in a Cumulative Average Learning Curve calculation
    6. 2.6 Learning Curves: Stepping up and stepping down
      1. 2.6.1 Step-points in a Unit Learning Curve calculation
      2. 2.6.2 Step-points in a Cumulative Average Learning Curve calculation
    7. 2.7 Cumulative values of Crawford Unit Learning Curves
      1. 2.7.1 Conway-Schultz Cumulative approximation
      2. 2.7.2 Jones Cumulative approximation
      3. 2.7.3 Cumulative approximation formulae compared
      4. 2.7.4 Batch or Lot Averages
      5. 2.7.5 Profiling recurring hours or costs – The quick way
    8. 2.8 Chapter review
    9. References
  12. 3 Unit Learning Curve – Cost Driver Segmentation
    1. 3.1 Learning Curve Cost Driver studies – What others have said
      1. 3.1.1 Loud and clear
      2. 3.1.2 Jefferson's Pie
    2. 3.2 Cost Driver changes and breakpoints
      1. 3.2.1 Output rate: Driver or consequence of learning?
      2. 3.2.2 End-of-line effects on learning
    3. 3.3 Segmentation Approach to Unit learning
      1. 3.3.1 Stopping and starting from where we left off
      2. 3.3.2 What if we invest more or less up-front?
      3. 3.3.3 Rate affected learning revisited
      4. 3.3.4 Parallel v serial working
      5. 3.3.5 Calibrating the Cost Driver segment contributions
    4. 3.4 Chapter review
    5. References
  13. 4 Unlearning and re-learning techniques
    1. 4.1 Reasons to forget
    2. 4.2 Anderlohr's technique
    3. 4.3 An alternative Simplified Retrograde Technique (not recommended)
    4. 4.4 Segmentation Technique
    5. 4.5 Comparison of re-learning techniques
    6. 4.6 Calibrating the level of learning lost
      1. 4.6.1 Calibrating the level of learning lost using the Segmentation Technique
      2. 4.6.2 Calibrating the level of learning lost using the Anderlohr technique
    7. 4.7 Chapter review
    8. References
  14. 5 Equivalent Unit Learning
    1. 5.1 The problems with traditional Unit Learning Curves
    2. 5.2 Development of the Equivalent Unit Learning theory
      1. 5.2.1 EUL confidence and prediction intervals
    3. 5.3 Equivalent Unit Learning and breakpoints
    4. 5.4 Double-Bunking data for early debunking of breakpoints
    5. 5.5 Equivalent Unit Learning and achievement mortgaging (progress optimism bias)
    6. 5.6 Using Equivalent Unit Learning as a top-down validation
    7. 5.7 Benefits of using Equivalent Unit Learning
    8. 5.8 Chapter review
    9. References
  15. 6 Multi-variant learning
    1. 6.1 Multi-variant Learning Curves
      1. 6.1.1 Option 1: Ignore Differences (ID)
      2. 6.1.2 Option 2: Fixed Factors (FF)
      3. 6.1.3 Option 3: Total Separation (TS)
      4. 6.1.4 Option 4: Proportional Representation (PR)
    2. 6.2 Multi-variant Learning Curve model calibration
      1. 6.2.1 Calibration with the ID approach
      2. 6.2.2 Calibration with the FF approach
      3. 6.2.3 Calibration with the TS approach
      4. 6.2.4 Calibration with the PR approach
      5. 6.2.5 Comparison of results
    3. 6.3 Cross-product organisational Learning Curve models
    4. 6.4 Chapter review
    5. References
  16. 7 Time-based Learning Curves
    1. 7.1 Time-Performance Learning Curve
    2. 7.2 Bevis-Towill Time-Constant model
    3. 7.3 Cross-product organisational Learning Curve models revisited
    4. 7.4 Chapter review
    5. References
  17. 8 The cost impact of collaborative working
    1. 8.1 Collaborative development costs with equal workshare partners
    2. 8.2 The collaborative development with unequal workshare partners
    3. 8.3 Production cost implications of collaborative working
    4. 8.4 Chapter review
    5. References
  18. Glossary of estimating and forecasting terms
  19. Legend for Microsoft Excel Worked Example Tables in Greyscale
  20. Index

Product information

  • Title: Learning, Unlearning and Re-Learning Curves
  • Author(s): Alan Jones
  • Release date: September 2018
  • Publisher(s): Routledge
  • ISBN: 9781351661461