HUMAN LEARNING: From Learning Curves to Learning Organizations

240 Pages·2000·8.19 MB·English

by  Ezey M. Dar-Ei (auth.)

HUMAN LEARNING: From Learning
Curves to Learning Organizations
INTERNATIONAL SERIES IN
OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Frederick S. Hillier, Series Editor
Stanford University
Saigal, R. / LINEAR PROGRAMMING.· A Modern Integrated Analysis
Nagumey, A. & Zhang, D. / PROJECTED DYNAMICAL SYSTEMS AND
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Padberg, M. & Rijal, M. / LOCATION, SCHEDULING, DESIGN AND
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Vanderbei, R. I LINEAR PROGRAMMING: Foundations and Extensions
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Gal, T. & Greenberg, H. / ADVANCES IN SENSITIVITY ANALYSIS AND
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Fang, S.-C., Rajasekera, J.R. & Tsao, H.-SJ. I ENTROPY OPTIMIZATION
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Yu, G. / OPERATIONS RESEARCH IN THE AIRLINE INDUSTRY
Ho, T.-H. & Tang, C. S. I PRODUCT VARIETY MANAGEMENT
EI-Taha, M. & Stidham, S. / SAMPLE-PATH ANALYSIS OF QUEUEING SYSTEMS
Miettinen, K. M. / NONLINEAR MULTIOBJECTIVE OPTIMIZATION
Chao, H. & Huntington, H. G. I DESIGNING COMPETITIVE ELECTRICITY MARKETS
Weglarz, J. / PROJECT SCHEDULING: Recent Models, Aigorithms & Applications
Sahin, I. & PoiatogIu, H. / QUALITY, WARRANTY AND PREVENTIVE MAINTENANCE
Tavares, L. V. I ADVANCED MODELS FOR PROJECT MANAGEMENT
Tayur, S., Ganeshan, R. & Magazine, M. I QUANTITATIVE MODELING FOR SUPPLY
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Weyant, J./ ENERGY AND ENVIRONMENTA L POLICY MODELING
Shanthikumar, J.G. & Sumita, UJAPPLIED PROBABILITY AND STOCHASTIC PROCESSES
Liu, B. & Esogbue, A.O. I DECISION CRITERIA AND OPTIMAL INVENTORY PROCESSES
Gal, Stewart & Hannel MULTICRITERIA DECISION MAKING: Advances in MCDM Models,
Algorithms, Theory, and Applications
Fox, B. L.! STRATEGIES FOR QUASI-MONTE CARLO
Hall, R.W. I HANDBOOKOFTRANSPORTATIONSCIENCE
HUMAN LEARNING: From Learning
Curves to Learning Organizations
by
Ezey M. Dar-EI
Faculty of Industrial Engineering and Management
Technion - Israel Institute of Technology
Haifa 32000, Israel
~.
"
Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication Data
Dar-El, E.
Human leaming: from leaming curves to leaming organizations / by Ezey M. Dar-El.
p.em --(International series in operations researeh & management scienee; 29)
Includes bibliographical references and index.
ISBN 978-1-4419-4997-4 ISBN 978-1-4757-3113-2 (eBook)
DOI 10.1007/978-1-4757-3113-2
1. Adult leaming. 2. Organizationalleaming. 1. Title. II.Series.
LC5225.1A2 D36 2000
153.1 '58--de21 00-058398
Copyright © 2000 by Springer Science+Business Media New York
Originally pubIished by Kluwer Academic PubIishers in 2000
Softcover reprint of the hardcover 1s t edition 2000
All rights reserved. No part of this publieation may be reproduced, stored in a retrieval
system or transmitted in any form or by any means, mechanical, photo-copying, recording,
or otherwise, without the prior written permission of the publisher, Springer Science+
Business Media, LLC.
Printed on acid-free paper.
To my wife
Evelyn
CONTENTS
PREFACE xiii
ACKNOWLEDGEMENT xv
1 INTRODUCTION TO HUMAN LEARNING 1
2 FACTORS THAT INFLUENCE THE LEARNING CURVE 9
2.1 Methods Improvement
2.2 Worker selection
2.3 Previous experience
2.4 Training
2.5 Motivation
2.6 Job complexity
2.7 Number ofrepetitions (or, cycles)
2.7.1 Does learning continue forever?
2.7.2 How many cycles to reach Time Standard
2.7.3 The size of job orders
2.8 Length of the task
2.9 Errors generated
2.10 Forgetting
2.11 Continuous improvement
3 SUMMARY OF LEARNING MODELS-NO FORGETTING 25
3.1 The Power model
3.1.1 Finding the total time to complete m cycles, 'Tm'
3.1.2 To find the average time to complete m cycles
3.2 The Cumulative Average power model
3.3 The Stanford B model
3.4 DeJong's learning model
3.5 Dar-El's modification ofDeJong's model
viii
3.6 The Dar-El/Ayas/Gilad Dual-Phase model Operations
3.6.1 Deterrnining the learning parameters
3.6.2 Calculating the production time for 'm' cycles
3.6.3 An example
3.7 The Bevisffowilliearning model
3.8 Other learning models
3.8.1 The Hancock Linear model
3.8.2 The Pegels model
3.8.3 The Dar-ElIAltman model
3.8.4 Yet other learning models
3.9 The learning models in review
4 DETERMINING THE POWER CURVE LEARNING 57
PARAMETERS
4.1 Deterrnining parameters from actual data
4.2 Predicting parameter values without prior experience
4.2.1 Predicting the learning constant ob'
4.2.2 Predicting the performance time for the first cycle
4.2.3 Reviewing the methodology for predicting 'b' and 'tl'
values
4.3 Resuscitating DeJong's learning curve model
4.4 Predicting the number of repetitions to reach standard
4.5 Assessing previous experience
5 A SUMMARY OF LEARNING MODELS WITH 77
FORGETTING
5.1 Background to the "L-F-R" process
5.2 How should L-F-R characteristics be measured?
5.3 Factors that influence the "L-F-R" phenomenon
5.3.1 The break (or interruption) length
5.3.2 Previous experience
5.3.3 Job complexity
5.3.4 The work engaged in during the break period
5.3.5 The cycle time ofthe task
5.3.6 The relearning curve
5.3.7 When relearning is a single observation point
5.4 Modeling the "L-F-R" phenomenon
5.4.1 Modeling "L-F-R" for multiple repetitions
5.4.2 Proposal for a "L-F-R" model
5.4.3 An example
5.4.4 A summary
IX
6 APPLICATIONS (WITH AND WITHOUT FORGETTING) 99
6.1 LC in time & motion study
6.2 LC in assembly line design
6.2.1 The optimal number of stations under learning
6.2.2 Minimizing the makespan for assembly (production)
lines under learning
6.2.3 LC with mixed-model assembly lines
6.2.4 LC in assembly lines for new products
6.2.5 Optimizing "cycle time: number of stations" under
learning constraints
6.3 LC with long cycle time tasks
6.3.1 A model for learning behavior in long cycle time tasks
6.3.2 Estimating the learning constant ob' under conditions
of forgetting
6.3.3 Predicting performance times for long cycle times
6.4 LC in batch size, or lot size production
6.5 LC in a Group Technology (GT) environment
6.6 Learning in a nT environment
6.7 'Speed-Accuracy' in a learning environment
6.7.1 Speed-Accuracy via Buck & Cheng (1993)
6.7.2 Speed-Accuracy via Vollichman (1993)
6.7.3 Discussion
6.8 Learning curves in machining operations
7 COST MODELS FOR OPTIMAL TRAINING SCHEDULES 135
7.1 Near optimal training schedules via experimentation: a military
application
7.1.1 The problem description
7.1.2 The pilot study
7.1.3 The study objectives
7.1.4 The experimental plan
7.1.5 Partial experimental results
7.1.6 Improving the efficiency of the training schedule
7.2 Developing optimal training schedules
7.2.1 Introduction
7.2.2 The cost function
7.2.3 Defining the optimization problem
7.2.4 Analytical solution of a simplified model
7.2.5 Empirical determination of 'g' and 'h'
7.2.6 An illustrative example
7.2.7 Conclusion
x
8 LEARNING IN THE PLANT: THE BEGINNINGS OF 159
ORGANIZATIONAL LEARNING
8.1 Learning at the firm level: the progress function
8.2 Learning with new processes and products
8.2.1 Ferdinand K. Levy: Adaptation in the production
process
8.2.2 Denis Towill (and co-authors): Industrial dynamics
family of learning curve models
8.3 Learning at the firm level and econometric models
8.4 Learning in aggregate and capacity planning
8.5 Learning in artificial intelligence
8.6 The impact on Quality and TIT
8.6.1 Continuous improvements in the manufacture of
semiconductors
8.6.2 The affect of quality factors on learning
9 LEARNING ORGANIZATIONS 185
9.1 Does 'Learning Organization' differ from 'Organizational
Learning'?
9.2 A primer on Learning Organizations
9.2.1 Learning Organization applications
9.3 Linking individual and plant learning to Learning Organizations
9.4 Teams and team operation
9.4.1 Work teams - past and present
9.4.2 Single loop vs. double loop learning in teams
9.5 Developing into a Learning Organization
9.5.1 Developing teams for the Learning Organization
9.5.2 Structure ofthe teams
10 A SUMMARY AND FUTURE RESEARCH ON HUMAN 211
LEARNING
10.1 Individuallearning
10.1.1 Experience
10.1.2 The Learning-Forgetting-Relearning (L-F-R)
phenomenon .
10.1.3 Estimating learning parameters
10.1.4 Learning with long cycle timesnarge products
10.1.5 Optimal training schedules
xi
10.2 Teamlcrew/group learning
10.3 Plant learning
10.4 The Learning Organization
REFERENCES 217
INDEX 233