The Human Element
Buch, Englisch, 576 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 1162 g
ISBN: 978-0-470-69976-8
Verlag: Wiley
The human element is the principle cause of incidents and accidents in all technology industries; hence it is evident that an understanding of the interaction between humans and technology is crucial to the effective management of risk. Despite this, no tested model that explicitly and quantitatively includes the human element in risk prediction is currently available.
Managing Risk: the Human Element combines descriptive and explanatory text with theoretical and mathematical analysis, offering important new concepts that can be used to improve the management of risk, trend analysis and prediction, and hence affect the accident rate in technological industries. It uses examples of major accidents to identify common causal factors, or “echoes”, and argues that the use of specific experience parameters for each particular industry is vital to achieving a minimum error rate as defined by mathematical prediction. New ideas for the perception, calculation and prediction of risk are introduced, and safety management is covered in depth, including for rare events and “unknown” outcomes
- Discusses applications to multiple industries including nuclear, aviation, medical, shipping, chemical, industrial, railway, offshore oil and gas;
- Shows consistency between learning for large systems and technologies with the psychological models of learning from error correction at the personal level;
- Offers the expertise of key leading industry figures involved in safety work in the civil aviation and nuclear engineering industries;
- Incorporates numerous fascinating case studies of key technological accidents.
Managing Risk: the Human Element is an essential read for professional safety experts, human reliability experts and engineers in all technological industries, as well as risk analysts, corporate managers and statistical analysts. It is also of interest to professors, researchers and postgraduate students of reliability and safety engineering, and to experts in human performance.
“…congratulations on what appears to be, at a high level of review, a significant contribution to the literature…I have found much to be admired in (your) research” Mr. Joseph Fragola – Vice President of Valador Inc.
“The book is not only technically informative, but also attractive to all concerned readers and easy to be comprehended at various level of educational background. It is truly an excellent book ever written for the safety risk managers and analysis professionals in the engineering community, especially in the high reliability organizations…” Dr Feng Hsu, Head of Risk Assessment and Management, NASA Goddard Space Flight Center
“I admire your courage in confronting your theoretical ideas with such diverse, ecologically valid data, and your success in capturing a major trend in them….I should add that I find all this quite inspiring. …The idea that you need to find the right measure of accumulated experience and not just routinely used calendar time makes so much sense that it comes as a shock to realize that this is a new idea”, Professor Stellan Ohlsson, Professor of Psychology, University of Illinois at Chicago
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
About the Authors xiii
Preface xv
Acknowledgements xix
Definitions of Risk and Risk Management xxi
Introduction: The Art of Prediction and the Creation of Order 1
Risk and Risk Management 1
Defining Risk 2
Managing Risk: Our Purpose, Plan and Goals 4
Recent Tragic Outcomes 6
Power Blackouts, Space Shuttle Losses, Concorde Crashes, Chernobyl, Three Mile Island and More 6
How Events and Disasters Evolve in a Phased Development: The Human Element 8
Our Values at Risk: The Probable Improvement 10
Probably or Improbably Not 11
How this Book is Organised 12
References 14
Technical Summary 15
Defining the Past Probability 15
Predicting Future Risk: Sampling from the Jar of Life 16
A Possible Future: Defining the Posterior Probability 21
The Engineers Have an Answer: Reliability 22
Drawing from the Jar of Life: The Hazard Function and Species Extinction 23
Experiencing Failure: Engineering and Human Risk and Reliability 25
Experience Space 27
Managing Safely: Creating Order out of Disorder Using Safety Management Systems 29
Describing the Indescribable: Top-Down and Bottom-Up 30
What an Observer will Observe and the Depth of our Experience 31
References 33
1 The Universal Learning Curve 35
Predicting Tragedies, Accidents and Failures: Using the Learning Hypothesis 35
The Learning Hypothesis: The Market Place of Life 37
Learning in Homo-Technological Systems (HTSs): The Way a Human Learns 39
Evidence of Risk Reduction by Learning 41
Evidence of Learning from Experience: Case Studies 42
Evidence of Learning in Economics 43
Evidence of Learning in Engineering and Architecture: The Costs of Mistakes 44
Learning in Technology: the Economics of Reducing Costs 46
Evidence of Learning Skill and Risk Reduction in the Medical Profession: Practice Makes Almost Perfect 48
Learning in HTSs: The Recent Data Still Agree 50
The Equations That Describe the Learning Curve 52
Zero Defects and Reality 54
Predicting Failures: The Human Bathtub 55
Experience Space: The Statistics of Managing Safety and of Observing Accidents 55
Predicting the Future Based on Past Experience: The Prior Ignorance 57
Future Events: the Way Forward Using Learning Probabilities 58
The Wisdom of Experience and Inevitability 59
The Last, First or Rare Event 59
Conclusions and Observations: Predicting Accidents 60
References 61
2 The Four Echoes 63
Power Blackouts, Space Shuttle Losses, Concorde Crashes, and the Chernobyl and Three Mile Island Accidents 63
The Combination of Events 64
The Problem Is the Human Element 65
The Four Echoes Share the Same Four Phases 66
The First Echo: Blackout of the Power Grid 67
Management’s Role 69
The First Echo: Findings 71
Error State Elimination 73
The Second Echo: Columbia/Challenger 75
The Results of the Inquiry: Prior Knowledge 76
The Second Echo: The Four Phases 79
Management’s Responsibility 80
Error State Elimination 82
The Third Echo: Concorde Tires and SUVs 83
Tire Failures: the Prior Knowledge 84
The Third Echo: The Four Phases 87
Management’s Responsibility 87
Error State Elimination 87
The Fourth Echo: Chernobyl 88
An Echo of Three Mile Island 88
The Consequences 92
Echoes of Three Mile Island 92
The Causes 93
Error State Elimination 94
The Fourth Echo: The Four Phases 95
Regulatory Environment and Practices 95
Case study: Regulation in Commercial Aviation 96
a) Regulations Development 96
b) Compliance Standards 97
c) Accident Investigation 97
Addressing Human Error 98
Management Responsibilities 99
Designing to Reduce Risk and the Role of Standards 99
Conclusion and Echoes: Predicting the Unpredictable 101
References 103
3 Predicting Rocket Risks and Refinery Explosions: Near Misses, Shuttle Safety and Anti-Missile Defence Systems Effectiveness 105
Learning from Near Misses and Prior Knowledge 105
Problems in Quantifying Risk: Predicting the Risk for the Next Shuttle Mission 107
Estimating a Possible Range of Likelihoods 112
Learning from Experience: Maturity Models for Future Space Mission Risk 114
Technology versus Technology 120
Missiles Risks over London: The German Doodlebug 121
Launching Missile Risk 124
The Number of Tests Required 126
Estimating the Risk of a Successful Attack and How Many Missiles We Must Fire 128
Uncertainty in the Risk of Failing to Intercept 128
What Risk Is There of a Missile Getting Through: Missing the Missile 131
Predicting the Risk of Industrial Accidents: The Texas City Refinery Explosion 132
From Lagging to Leading: Safety Analysis and Safety Culture 134
Missing Near Misses 137
What these Risk Estimates Tell Us: The Common Sense Echo 137
References 138
4 The Probability of Human Error: Learning in Technological Systems 141
What We Must Predict 141
The Probability Linked to the Rate of Errors 144
The Definition of Risk Exposure and the Level of Attainable Perfection 146
Comparison to Conventional Social Science and Engineering Failure and Outcome Rate Formulations 147
The Learning Probabilities and the PDFs 150
The Initial Failure Rate and its Variation with Experience 150
The ‘Best’ MERE Risk Values 153
Maximum and Minimum Likely Outcome Rates 155
Standard Engineering Reliability Models Compared to the MERE Result 155
Future Event Estimates: The Past Predicts the Future 157
Statistical Bayesian-Type Estimates: The Impact of Learning 158
Maximum and Minimum Likelihood 161
Comparison to Data: The Probability of Failure and Human Error 161
Comparison of the MERE Result to Human Reliability Analysis 164
Implications for Generalised Risk Prediction 168
Conclusions: The Probable Human Risk 170
References 171
5 Eliminating Mistakes: The Concept of Error States 173
A General Accident Theory: Error States and Safety Management 173
The Physics of Errors 174
The Learning Hypothesis and the General Accident Theory 176
Observing Outcomes 178
A Homage to Boltzmann: Information from the Grave 181
The Concept of Depth of Experience and the Theory of Error States 184
The Fundamental Postulates of Error State Theory 188
The Information in Error States: Establishing the Risk Distribution 189
The Exponential Distribution of Outcomes, Risk and Error States 192
The Total Number of Outcomes 193
The Observed Rate and the Minimum Number of Outcomes 195
Accumulated Experience Measures and Learning Rates 198
The Average Rate 200
Analogy and Predictions: Statistical Error Theory and Learning Model Equivalence 201
The Influence of Safety Management and Regulations: Imposing Order on Disorder 201
The Risk of Losing a Ship 203
Distribution Functions 205
The Most Probable and Minimum Error Rate 208
Learning Rates and Experience Intervals: The Universal Learning Curve 209
Reducing the Risk of a Fatal Aircraft Accident: the Influence of Skill and Experience 212
Conclusions: A New Approach 215
References 216
6 Risk Assessment: Dynamic Events and Financial Risks 219
Future Loss Rate Prediction: Ships and Tsunamis 221
Predicted Insurance Rates for Shipping Losses: Historical Losses 224
The Premium Equations 225
Financial Risk: Dynamic Loss and Premium Investments 226
Numerical Example 227
Overall Estimates of Shipping Loss Fraction and Insurance Inspections 228
The Loss Ratio: Deriving the Industrial Damage Curves 229
Making Investment Decisions: Information Drawing from the Jar of Life 231
Information Entropy and Minimum Risk 232
Progress and Learning in Manufacturing 233
Innovation in Technology for the Least Product Price and Cost: Reductions During Technological Learning 234
Cost Reduction in Manufacturing and Production: Empirical Elasticity ‘Power Laws’ and Learning Rates 235
A New General Formulation for Unit Cost Reduction in Competitive Markets: the Minimum Cost According to a Black-Scholes Formulation 237
Universal Learning Curve: Comparison to the Usual Economic Power Laws 240
The Learning Rate b-Value ‘Elasticity’ Exponent Evaluated 242
Equivalent Average Total Cost b-Value Elasticity 244
Profit Optimisation to Exceed Development Cost 246
The Data Validate the Learning Theory 247
a) Aircraft Manufacturing Costs Estimate Case 247
b) Photovoltaic Case 248
c) Air Conditioners Case 250
d) Ethanol Prices Case 251
e) Windpower Case 252
f) Gas Turbine Power Case 253
g) The Progress Curve for Manufacturing 254
Non-Dimensional UPC and Market Share 256
Conclusions: Learning to Improve and Turning Risks into Profits 259
References 260
7 Safety and Risk Management Systems: the Fifth Echoes 263
Safety Management Systems: Creating Order Out of Disorder 263
Workplace Safety: The Four Rights, Four Wrongs and Four Musts 264
Acceptable Risk: Designing for Failure and Managing for Success 265
Managing and Risk Matrices 269
Organisational Factors and Learning 272
A Practical ‘Safety Culture’ Example: The Fifth Echo 273
Safety Culture and Safety Surveys: The Learning Paradox 278
Never Happening Again: Perfect Learning 280
Half a World Apart: Copying the Same Factors 281
Using a Bucket: Errors in Mixing at the JCO Plant 283
Using a Bucket: Errors in Mixing at the Kean Canyon Explosives Plant 284
The Prediction and Management of Major Hazards: Learning from SMS Failures 286
Learning Environments and Safety Cultures: The Desiderata of Desires 289
Safety Performance Measures: Indicators and Balanced Scorecards 291
Safety and Performance Indicators: Measuring the Good 292
Human Error Rates Passing Red Lights, Runway Incursions and Near Misses 293
Risk Informed Regulation and Degrees of Goodness: How Green is Green? 294
Modelling and Predicting Event Rates and Learning Curves Using Accumulated Experience 297
Using the Past to Predict the Future: How Good is Good? 299
Reportable Events 300
Scrams and Unplanned Shutdowns 301
Common-Cause Events and Latent Errors 303
Performance Improvement: Case-by-Case 304
Lack of Risk Reduction: Medical Adverse Events and Deaths 305
New Data: Sentinel Events, Deaths and Blood Work 308
Medication Errors in Health Care 313
Organisational Learning and Safety Culture: the ‘H-Factor’ 316
Risk Indicator Data Analysis: A Case Study 319
Meeting the Need to Measure Safety Culture: the Hard and the Soft Elements 321
Creating Order from Disorder 324
References 324
8 Risk Perception: Searching for the Truth Among all the Numbers 329
Perceptions and Predicting the Future: Risk Acceptance and Risk Avoidance 329
Fear of the Unknown: The Success Journey into What We Do or Do Not Accept 333
A Possible Explanation of Risk Perception: Comparisons of Road and Rail Transport 334
How Do We Judge the Risk? 337
Linking Complexity, Order, Information Entropy and Human Actions 338
Response Times, Learning Data and the Universal Laws of Practice 341
The Number and Distribution of Outcomes: Comparison to Data 343
Risk Perception: Railways 345
Risk Perception: Coal Mining 348
Risk Perception: Nuclear Power in Japan 349
Risk Perception: Rare Events and Risk Rankings 352
Predicting the Future Number of Outcomes 354
A Worked Example: Searching out and Analysing Data for Oil Spills 354
Typical Worksheet 358
Plotting the Data 358
Fitting a Learning Curve 358
Challenging Zero Defects 359
Comparison of Oil Spills to Other Industries 362
Predicting the Future: the Probability and Number of Spills 364
Observations on this Oil Spill Case 365
Knowing What We Do Not Know: Fear and Managing the Risk of the Unknown 365
White and Black Paradoxes: Known Knowns and Unknown Unknowns 367
The Probability of the Unknowns: Learning from What We Know 368
The Existence of the Unknown: Failures in High Reliability Systems 370
The Power of Experience: Facing Down the Fear of the Unknown 371
Terrorism, Disasters and Pandemics: Real, Acceptable and Imaginary Risks 373
Estimating Personal Risk of Death: Pandemics and Infectious Diseases 374
Sabotage: Vulnerabilities, Critical Systems and the Reliability of Security Systems 377
What Is the Risk? 378
The Four Quadrants: Implications of Risk for Safety Management Systems 378
References 380
9 I Must Be Learning 383
Where We Have Come From 383
What We Have Learned 384
What We Have Shown 388
Legal, Professional and Corporate Implications for the Individual 389
Just Give Me the Facts 391
Where We are Going 392
Reference 393
Nomenclature 395
Appendices: 401
Appendix A: The ‘Human Bathtub’: Predicting the Future Risk 403
The Differential Formulation for the Number of Outcomes 405
The Future Probability 406
Insufficient Learning 408
Appendix B: The Most Risk, or Maximum Likelihood, for the Outcome (Failure or Error) Rate while Learning 411
The Most or Least Likely Outcome Rate 411
The Maximum and Minimum Risk: The Two Solutions 412
Low Rates and Rare Events 413
The Limits of Maximum and Minimum Risk: The Two Solutions 414
Common Sense: The Most Risk at the Least Experience and the Least Risk as the First Outcome Decreases with Experience 414
Typical Trends in Our Most Likely Risk 415
The Distribution with Depth of Experience 417
References 418
Appendix C: Transcripts of the Four Echoes 419
Power Blackout, Columbia Space Shuttle loss, Concorde Crash and Chernobyl Accident 419
The Combination of Events 419
The Four Echoes Share the Same Four Phases 420
Appendix. Blackout Chronology and the Dialog from Midday 14 August 2003 420
The Second Echo: Columbia/Challenger 432
Appendix: Shuttle Dialog and Transcripts 433
The Third Echo: Concorde Tires and SUVs 435
Appendix: Dialog for the Concorde Crash 436
The Fourth Echo: TMI/Chernobyl 439
Appendix: Chronology and Transcripts of the Chernobyl Reactor Unit 4 Accident 439
Conclusion and Echoes: Predicting the Unpredictable 444
Appendix D: The Four Phases: Fuel Leak Leading to Gliding a Jet in to Land without any Engine Power 447
The Bare Facts and the Sequence 447
The Four Phases 449
Flight Crew Actions 455
Initial Recognition of the Fuel Loss (04:38–05:33) 455
Crew Reaction to the Fuel Imbalance Advisory (05:33–05:45) 456
Crew Reaction to the Continued Fuel Loss (05:45–06:10) 458
Crew Reaction to the (Two) Engine Failures 460
References 463
Appendix E: The Four Phases of a Midair Collision 465
The Bare Facts 465
The Four Phases 465
References 469
Appendix F: Risk From the Number of Outcomes We Observe: How Many are There? 471
The Number of Outcomes: The Hypergeometric Distribution 472
Few Outcomes and many Non-Outcomes: The Binomial and Poisson Distributions 475
The Number of Outcomes: In the Limit 478
The Perfect Learning Limit: Learning from Non-Outcomes 479
The Relative Change in Risk When Operating Multiple Sites 481
References 482
Appendix G: Mixing in a Tank: The D.D. Williamson Vessel Explosion 483
Errors in Mixing in a Tank at the Caramel Factory: The Facts 483
The Prior Knowledge 484
Another Echo 488
References 490
Appendix H: Never Happening Again 491
The Risk of an Echo, or of a Repeat Event 491
The Matching Probability for an Echo 493
The Impact of Learning and Experience on Managing the Risk of Repeat Events 494
The Theory of Evidence: Belief and Risk Equivalence 496
References 497
Appendix I: A Heuristic Organisational Risk Stability Criterion 499
Order and Disorder in Physical and Management Systems 499
Stability Criterion 500
References 502
Appendix J: New Laws of Practice for Learning and Error Correction 505
Individual Learning and Practice 505
Comparison to Error Reduction Data 506
Comparison to Response Time Data and the Consistent Law of Practice 509
Reconciling the Laws 511
Conclusions 512
References 513
Appendix K: Predicting Rocket Launch Reliability – Case Study 515
Summary 515
Theory of Rocket Reliability 515
a) Unknown Total Number of Launches and Failures 516
b) Known Total Number of Launches and Failures 517
Results 518
Measures of Experience 519
Comparsion to World Data 520
Predicting the Probability of Failure 521
Statistical Estimates of the Failure Probability for the Very ‘Next’ Launch 523
Independent Validation of the MERE Launch Failure Curve 525
Observations 526
References 526
Index 527
