Alan Jones

Learning, Unlearning and Re-Learning Curves

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 304
• Erscheinungstermin: 31. Januar 2025
• Englisch
• Abmessung: 234mm x 156mm
• ISBN-13: 9781032948508
• ISBN-10: 1032948507
• Artikelnr.: 71646314

Autorenporträt

Alan R. Jones is Principal Consultant at Estimata Limited, aconsultancy service specialising in Estimating Skills Training. He is a Certified Cost Estimator/Analyst (US) and Certified Cost Engineer (CCE) (UK). Prior to setting up his own business, he enjoyed a 40-year career in the UK aerospace and defence industry as an estimatorAlan is a Fellow of the Association of Cost Engineers and a member of the International Cost Estimating and Analysis Association. Historically (some four decades ago), Alan was a graduate in Mathematics from Imperial College of Science and Technology in London, and was an MBA Prize-winner at the Henley Management College.

Inhaltsangabe

Volume IV Table of Contents
1 Introduction and Objectives
1.1 Why write this book? Who might find it useful? Why Five Volumes?
1.1.1 Why write this series? Who might find it useful?
1.1.2 Why Five Volumes?
1.2 Features you'll find in this book and others in this series
1.2.1 Chapter Context
1.2.2 The Lighter Side (humour)
1.2.3 Quotations
1.2.4 Definitions
1.2.5 Discussions and Explanations with a Mathematical Slant for Formula-philes
1.2.6 Discussions and Explanations without a Mathematical Slant for Formula-phobes
1.2.7 Caveat Augur
1.2.8 Worked Examples
1.2.9 Useful Microsoft Excel Functions and Facilities
1.2.10 References to Authoritative Sources
1.2.11 Chapter Reviews
1.3 Overview of Chapters in this Volume
1.4 Elsewhere in the 'Working Guide to Estimating & Forecasting' Series
1.4.1 Volume I: Principles
Process and Practice of Professional Number Juggling
1.4.2 Volume II: Probability
Statistics and other Frightening Stuff
1.4.3 Volume III: Best Fit Lines & Curves
and some Mathe-Magical Transformations
1.4.4 Volume IV: Learning
Unlearning and Re-Learning Curves
1.4.5 Volume V: Risk
Opportunity
Uncertainty and Other Random Models
1.5 Final Thoughts and Musings on this Volume and Series
References
2 Quantity-based Learning Curves
2.1 A Brief History of the Learning Curve as a Formal Relationship
2.2 Two Basic Learning Curve Models (Wright and Crawford)
2.2.1 Wright Cumulative Average Learning Curve
2.2.2 Crawford Unit Learning Curve
2.2.3 Wright and Crawford Learning Curves Compared
2.2.4 What's So Special about the Doubling Rule?
2.2.5 Learning Curve Regression - What appears to be Wright
may in fact be Wrong!
2.3 Variations on the Basic Learning Curve Models
2.3.1 DeJong Unit Learning Curve
2.3.2 DeJong-Wright Cumulative Average Hybrid Learning Curve
2.3.3 Stanford-B Unit Learning Curve
2.3.4 Stanford-Wright Cumulative Average Hybrid Learning Curve
2.3.5 S-Curve Unit Learning Curve
2.3.6 S-Curve-Wright Cumulative Average Hybrid Learning Curve
2.4 Where and When to Apply Learning and How Much?
2.4.1 To What Kind of Task can a Learning Curve be applied?
2.4.2 Additive and Non-Additive Properties of Learning Curves
2.4.3 Calibrating or Measuring Observed Learning
2.4.4 What if we don't have any actuals? Rules of Thumb Rates of Learning
2.5 Changing the Rate of Learning - Breakpoints
2.5.1 Dealing with a Breakpoint in a Unit Learning Curve Calculation
2.5.2 Dealing with a Breakpoint in a Cumulative Average Learning Curve Calculation
2.6 Learning Curves: Stepping Up and Stepping Down
2.6.1 Step-points in a Unit Learning Curve Calculation
2.6.2 Step-points in a Cumulative Average Learning Curve Calculation
2.7 Cumulative Values of Crawford Unit Learning Curves
2.7.1 Conway-Schultz Cumulative Approximation
2.7.2 Jones Cumulative Approximation
2.7.3 Cumulative Approximation Formulae Compared
2.7.4 Batch or Lot Averages
2.7.5 Profiling Recurring Hours or Costs - the Quick Way
2.8 Chapter Review
References
3 Unit Learning Curve - Cost Driver Segmentation
3.1 Learning Curve Cost Driver Studies - What Others Have Said
3.1.1 Loud and Clear
3.1.2 Jefferson's Pie
3.2 Cost Driver Changes and Breakpoints
3.2.1 Output Rate: Driver or Consequence of Learning?
3.2.2 End-of-Line Effects on Learning
3.3 Segmentation Approach to Unit Learning
3.3.1 Stopping and Starting from Where We Left Off
3.3.2 What If We Invest More or Less Up-front?
3.3.3 Rate Affected Learning Revisited
3.3.4 Parallel v Serial Working
3.3.5 Calibrating the Cost Driver Segment Contributions
3.4 Chapter Review
References
4 Unlearning and Re-Learning Techniques
4.1 Reasons to Forget
4.2 Anderlohr's Technique
4.3 An Alternative Simplified Retrograde Technique (Not Recommended)
4.4 Segmentation Technique
4.5 Comparison of Re-Learning Techniques
4.6 Calibrating the Level of Learning Lost
4.6.1 Calibrating the Level of Learning Lost Using the Segmentation Technique
4.6.2 Calibrating the Level of Learning Lost Using the Anderlohr Technique
4.7 Chapter Review
References
5 Equivalent Unit Learning
5.1 The Problems with Traditional Unit Learning Curves
5.2 Development of the Equivalent Unit Learning Theory
5.2.1 EUL Confidence and Prediction Intervals
5.3 Equivalent Unit Learning and Breakpoints
5.4 Double-Bunking Data for Early Debunking of Breakpoints
5.5 Equivalent Unit Learning and Achievement Mortgaging (Progress Optimism Bias)
5.6 Using Equivalent Unit Learning as a Top-down Validation
5.7 Benefits of Using Equivalent Unit Learning
5.8 Chapter Review
References
6 Multi-Variant Learning
6.1 Multi-Variant Learning Curves
6.1.1 Option 1: Ignore Differences (ID)
6.1.2 Option 2: Fixed Factors (FF)
6.1.3 Option 3: Total Separation (TS)
6.1.4 Option 4: Proportional Representation (PR)
6.2 Multi-Variant Learning Curve Model Calibration
6.2.1 Calibration with the ID Approach
6.2.2 Calibration with the FF Approach
6.2.3 Calibration with the TS Approach
6.2.4 Calibration with the PR Approach
6.2.5 Comparison of Results
6.3 Cross-Product Organisational Learning Curve Models
6.4 Chapter Review
References
7 Time-Based Learning Curves
7.1 Time-Performance Learning Curve
7.2 Bevis-Towill Time-Constant Model
7.3 Cross-Product Organisational Learning Curve Models Revisited
7.4 Chapter Review
References
8 The Cost Impact of Collaborative Working
8.1 Collaborative Development Costs with Equal Workshare Partners
8.2 The Collaborative Development with Unequal Workshare Partners
8.3 Production Cost Implications of Collaborative Working
8.4 Chapter Review
References
Glossary of Estimating Terms