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 VARIATIONAL INEQUALITIES WITH APPLICATIONS Padberg, M. & Rijal, M. / LOCATION, SCHEDULING, DESIGN AND INTEGER PROGRAMMING Vanderbei, R. I LINEAR PROGRAMMING: Foundations and Extensions JaiswaI, N.K. / MILITARY OPERATIONS RESEARCH: Quantitative Decision Making Gal, T. & Greenberg, H. / ADVANCES IN SENSITIVITY ANALYSIS AND PARAMETRIC PROGRAMMING Prabhu, N.U. / FOUNDATIONSOFQUEUEING THEORY Fang, S.-C., Rajasekera, J.R. & Tsao, H.-SJ. I ENTROPY OPTIMIZATION AND MATHEMATICAL PROGRAMMING 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 CHAIN MANAGEMENT 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