THÔNG TIN TÀI LIỆU
Research Methods for Operations Management Second Edition Research Methods for Operations Management, second edition, is a toolkit of research approaches primarily for advanced students and beginner researchers, but also a reference book for any researcher in OM Many students begin their career in research limited by the one or few approaches taken by their department The concise, accessible overviews found here equip them with an understanding of a variety of methods and how to use them, enabling students to tailor their research project to their own strengths and goals The more seasoned researcher will find comprehensive descriptions and analyses on a wide variety of research approaches This updated and enhanced edition responds to the latest developments in OM, including the growing prominence of services and production of intangible products, and the increasing use of secondary data and of mixed approaches Alternative research approaches are included and explored to help with the early planning of research.This edition also includes expanded literature review and analysis to guide students towards the next steps in their reading, and more detailed step-by-step advice to tie theory with the researcher’s own practice Including contributions from an impressive range of the field’s leading thinkers in OM research, this is a guide that no-one embarking on an OM research project should be without Christer Karlsson is Professor of Innovation and Operations Management and Academic Director for the Competitiveness platform at Copenhagen Business School, Denmark He is also Professor at the European Institute for Advanced Studies in Management (EIASM) in Brussels, Belgium ‘This is an important book on an important subject – written by some of the best regarded experts in the field and overseen by Christer Karlsson, the undoubted master of the Operations Management research process.’ Nigel Slack, Emeritus Professor of Operations Management and Strategy at Warwick Business School, UK ‘I highly recommend this book, which is comprehensive in both its breadth and depth of operations management research approaches The authors provide everything from practical tips through to deep methodological and research process guidance, in a thoroughly readable and highly motivating style.’ Professor Danny Samson, University of Melbourne, Australia ‘Research Methods for Operations Management is a must read text With contributions from some of the leading scholars in operations management, this text is a great reference for researchers regardless of their experience.’ Professor Andy Neely, Head Institute for Manufacturing, Cambridge and President, European Operations Management Association Research Methods for Operations Management Second Edition Edited by Christer Karlsson First published 2009 Second Edition 2016 by Routledge Park Square, Milton Park, Abingdon, Oxon OX14 4RN And by Routledge 711 Third Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2009 Taylor & Francis, Inc.; individual chapters, the contributors © 2016 selection and editorial material, Christer Karlsson; individual chapters, the contributors The right of the editor to be identified as the author of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988 All rights reserved No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers Every effort has been made to contact copyright holders for their permission to reprint material in this book The publishers would be grateful to hear from any copyright holder who is not here acknowledged and will undertake to rectify any errors or omissions in future editions of this book Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data Names: Karlsson, Christer, editor Title: Research methods for operations management / edited by Christer Karlsson Other titles: Researching operations management Description: Second Edition | New York : Routledge, 2016 | Revised edition of Researching operations management, 2009 | Includes bibliographical references and index Identifiers: LCCN 2015046995| ISBN 9781138945418 (hardback) | ISBN 9781138945425 (pbk.) | ISBN 9781315671420 (ebook) Subjects: LCSH: Industrial management—Research | Production management—Research | Operations research Classification: LCC HD30.4 R478 2016 | DDC 658.5/7—dc23 LC record available at http://lccn.loc.gov/2015046995 ISBN: 978-1-138-94541-8 (hbk) ISBN: 978-1-138-94542-5 (pbk) ISBN: 978-1-315-67142-0 (ebk) Typeset in Bembo by Sunrise Setting Ltd, Brixham, UK Contents List of figures List of tables List of contributors Acknowledgements Introduction to the book ix x xi xv CHRISTER KARLSSON Chapter overview 1.1 Introduction 1.2 How to use the book 1.3 Plan of the book 1.4 Introduction to the second edition Research in operations management CHRISTER KARLSSON Chapter overview 2.1 Introduction 2.2 Research outputs and targets 12 2.3 Roles of the researcher 14 2.4 The research process 16 2.5 Research as contribution to knowledge 17 2.6 What to research for academia and practice 25 2.7 Research quality 30 2.8 Assessing research quality and contribution 31 2.9 Getting published 36 2.10 Research ethics and ethics for researchers 39 2.11 Summary 41 References 44 Further reading 45 vi Contents The research process 46 PÄR ÅHLSTRÖM Chapter overview 46 3.1 Contributing to knowledge 47 3.2 Choosing a research topic 54 3.3 Using literature to develop the research topic 57 3.4 Developing research questions 64 3.5 Considerations in choosing a research approach 68 3.6 Chapter summary: completing the research cycle 76 References 78 Surveys 79 CIPRIANO FORZA Chapter overview 79 4.1 Introduction 80 4.2 The survey research process 95 4.3 What is needed prior to survey research design? 97 4.4 How a survey should be designed 108 4.5 Pilot testing the questionnaire 125 4.6 Advancements in theory formalization and survey design 135 4.7 Survey execution 139 4.8 Data analysis and interpretation of results 141 4.9 Information that should be included in articles 148 4.10 Ethical issues in survey research 150 4.11 Summary 151 Bibliography 154 Case research CHRIS VOSS, MARK JOHNSON AND JAN GODSELL Chapter overview 165 5.1 Introduction 165 5.2 When to use case research 167 5.3 The research framework, constructs and questions 171 5.4 Choosing cases 174 5.5 Developing research instruments and protocols 177 5.6 Conducting the field research 179 5.7 Reliability and validity in case research 184 5.8 Data documentation and coding 185 5.9 Analysis 187 5.10 Conclusion 193 References 194 165 Contents Longitudinal field studies vii 198 PÄR ÅHLSTRÖM AND CHRISTER KARLSSON Chapter overview 198 6.1 Introduction to the longitudinal field study 199 6.2 Setting up the longitudinal field study 202 6.3 Collecting data in the longitudinal field study 210 6.4 Analysing longitudinal field data 214 6.5 Building theory from longitudinal field studies 220 6.6 Evaluating longitudinal field studies 223 6.7 Summary 228 Bibliography 229 Action research 233 PAUL COUGHLAN AND DAVID COGHLAN Chapter overview 233 7.1 Introduction 233 7.2 Action in operations 234 7.3 What is AR? 234 7.4 What is needed before selecting action research? 240 7.5 Designing the academic/thesis action research project 241 7.6 Implementing action research 244 7.7 Pre-step: context and purpose 247 7.8 Main steps 248 7.9 Meta learning 252 7.10 Generating theory through action research 255 7.11 Quality in action research 256 7.12 Action research skills 260 7.13 Writing an action research report or dissertation 262 7.14 Publishing/dissemination 263 7.15 Conclusions 264 References 264 Clinical management research CHRISTER KARLSSON Chapter overview 268 8.1 Introduction 269 8.2 Characteristics of clinical management research 269 8.3 Clinical research concept and history 272 8.4 Positioning the clinical approach to management research 273 8.5 How to clinical management research 276 8.6 The clinical research approach in summary 285 268 viii Contents 8.7 Contemporary published clinical operations management research 285 8.8 Conclusions: applicability, strengths and weaknesses 286 Bibliography 287 Modelling and simulation 290 J WILL M BERTRAND AND JAN C FRANSOO Chapter overview 290 9.1 Introduction 290 9.2 Origins and development of model-based research in operations management 291 9.3 Methodologies in quantitative modelling 298 9.4 How to conduct quantitative research in operations management 304 9.5 Relevance 325 9.6 Summary 326 Bibliography 327 Index 331 Figures 2.1 2.2 3.1 3.2 3.3 3.4 3.5 3.6 3.7 4.1 4.2 5.1 5.2 7.1 7.2 7.3 9.1 The logic of argument Building the research contribution Linking contributions to the maturity of knowledge Research topics are defined by practical problems and existing literature Research questions are motivated by practical problems and existing literature Research questions depend on maturity of knowledge Methodological fit – internal consistency between key elements of a research project The relationship between existing knowledge and research approach Completing the research cycle The survey research process Linkages between decisions in survey planning Different process paths in case research Example of causal network analysis The action research cycle Spiral of action research cycles Meta-cycle of action research Research model 20 26 53 56 65 67 69 70 77 95 111 169 189 247 252 253 302 Modelling and simulation 319 not surprising that the assumptions in operational research projects are seldom checked, because doing so would be very time consuming and costly due to the effort involved in collecting all the data needed for checking all the underlying model assumptions This explains why real-life operational process improvement projects seldom produce scientific knowledge about operational processes As stated before, quantitative empirical research must be designed to test the validity of quantitative theoretical models and quantitative theoretical problem solutions, with respect to real-life operational processes This is in line with the more general concept of theory-driven empirical research in OM (Handfield and Melnyk, 1998; Melnyk and Handfield, 1998) Model-driven empirical research takes advantage of the large body of axiomatic quantitative research in OM, and designs the empirical research accordingly Examples are the work by Fisher and Raman (1999), Inman (1999) and Zoryk-Schalla et al (2004) The essence of their work is validating either the conceptual model or the solution proposed by axiomatic research results Fisher and Raman (1999) analyse the accuracy of inventory records in retail and, using available models, assess the consequences of these inaccuracies on the results that have been obtained in the axiomatic studies Inman (1999) validates the assumptions commonly made in axiomatic research about the processing times and order arrival times in production systems ZorykSchalla et al (2004) analyse the decision modelling process in advanced planning software and compare the theoretical assessment to the empirical observations they make Their empirical observations are driven by hypotheses that are based on the theories developed earlier in primarily axiomatic research settings A major problem here is that real-life operational processes are all different, although there are structural similarities within classes of operational processes The similarities are often caused by the type of manufacturing technology used Well-known classes of operational processes are, for instance, the continuous flow shop (e.g assembly line) for high-volume production of similar products, and the job shop for low-volume production of a large variety of different products However, depending on the work organization, the information system used, the level of education of the work force and so on, different flow lines and different job shops may have different operational process characteristics and these characteristics may evolve over time Therefore, empirical quantitative research should aim at validating the basic assumptions about the operational processes and problem characteristics for well-defined classes of operational processes, underlying the theoretical models and problems From these observations we can derive the steps that need to be taken when doing empirical quantitative research 9.4.7.1 Identification of process or problem assumptions The first step is the identification of the basic assumptions regarding the operational process underlying the theoretical models or problems In the OM literature, we can distinguish different research streams that share common 320 J.Will M Bertrand and Jan C Fransoo assumptions about the operations process or operational decision problem For instance, there exists a research stream that is based on a queuing model view of the production process We call this a basic assumption 9.4.7.2 Identification of process and problem types The second step is that researchers should identify the type of operational process and the type of decision problem regarding this operational process to which the basic assumptions are assumed to apply For instance, it is assumed that decisions about the resource structure of a job shop production system should be based on a queuing model of the flow or orders along the work centres 9.4.7.3 Development of operational definitions of the process The third step is that operational, objective criteria must be developed for deciding whether or not a real-life operational process belongs to the class of operational processes considered (i.e a job shop) and for identifying the decision system in the operational process that represents the decision problem considered These criteria should be objective; that is, each researcher in OM using these criteria would come to the same decision regarding the process and the decision system 9.4.7.4 Hypotheses development The fourth step is to derive, from the basic assumptions, hypotheses regarding the behaviour of the operational process This behaviour refers to variables or phenomena that can be measured or observed at the operational process in an objective way The more different testable hypotheses derived from the basic assumptions are, the stronger the research is Hypotheses can often be developed based on insight from axiomatic research (either analytical or simulation-based) 9.4.7.5 Measurement development The fifth step is to develop an objective way to measurement or to make the observations This very crucial step requires documentation The reason for this is that, in operational process research, there exists no formalized construct for variables such as processing time, machine capacity, production output, production throughput time, etc., nor generally accepted ways of measuring these variables exist This illustrates the weak position of quantitative empirical research in OM The situation being as it is, empirical OM researchers must develop their own way of measuring and document this carefully This requires that the researcher knows how to identify the relevant characteristics of the operational process, and knows how to change or influence and measure the Modelling and simulation 321 relevant characteristics of the process.Thus, model-based empirical research cannot be done without a systematic approach for identifying and measuring reallife operational processes This is what is called the conceptual modelling of a system by Mitroff et al (1974) Conceptual models define the relevant variables of a system under study, the nature of their relationships and their measurements 9.4.7.6 Data collection The sixth step consists of applying the measurement and observation systems, collecting and documenting the resulting data A variety of data collection methods and sources can be used, including surveys, public economic or finance data (e.g Hendricks and Singhal, 2003), private company transactional data (e.g Fransoo and Wiers, 2006) or observational studies (such as time and motion studies) (e.g.Van Zelst et al., 2009) 9.4.7.7 Data analysis The seventh step is the processing of the data, which generally will include the use of statistical analysis Here special techniques are needed since the data are not the result of an experimental design where variables in the system can be manipulated at will, but result from observations on a real-life system that cannot be manipulated in an arbitrary way Sophisticated statistical techniques have been developed for this type of research in some branches of research in social sciences (e.g Herzog, 1996; Marcoulides and Schumacker, 1996) When developing the hypotheses regarding the behaviour of the operational process in step 4, it should be taken into account what type of behaviour can be expected of the process under the given real-life circumstances, within the time frame that the process can be observed; the hypotheses should be restricted to behaviour in the expected range and time frame For instance it makes no sense to develop the hypothesis that a job shop will have an average order throughput time of 60 weeks under a steady-state capacity utilization of 95 per cent, if a reliable measurement of the work order throughput time under a capacity utilization of 95 per cent requires that the process is measured for 10,000 years Thus, developing effective hypotheses and an efficient operational measurement system require that the researcher is quite familiar with the type of operational process and the type of decision problem concerned, and is very familiar with the statistical techniques available for analysis of field data 9.4.7.8 Interpretation of results Finally, the last step in quantitative empirical research consists of the interpretation of the research results related to the theoretical models or problems that gave rise to the hypotheses that were tested This step completes the validation 322 J.Will M Bertrand and Jan C Fransoo process and may result in confirmation of (parts of) the theoretical model in relation to the decision problem and in relation to the operational process considered, but may also lead to (partial) rejections and suggestions for improving the theoretical models 9.4.8 Demonstration of model-based empirical research Model-based empirical research pertains either to validating the basic assumptions underlying formal models of operational processes, or to checking the claims regarding performance that can be obtained, as predicted with the use of formal models In this section we will demonstrate both types of research in the form of model-based empirical research carried out on the execution and control of product development projects (Van Oorschot et al., 2005; Dragut and Bertrand, 2008) Product development projects consist of networks of design tasks A design task is the task to determine the technical product specifications that lead to the realization of pre-specified product functionality Design tasks are carried out by design engineers, often working together in teams managed by a project leader A design task essentially consists of a number of design activities, each activity referring to a specific design problem to be solved Complex design tasks require many design activities; simple design tasks only contain a few design activities The design activity can be considered the elementary unit for estimating the amount of time needed to carry out design tasks The larger the number of activities needed, the larger the amount of time needed for solving a design problem Research results from cognitive psychology (Reed, 1988; Best, 1995) suggest that the time needed by humans for solving problems can be modelled as a stochastic variable with a long-tailed distribution function that can be approximated by a negative exponential Assuming that the time needed to perform design activities are identically distributed, it follows that the time needed for performing a design task consisting of k activities, is Erlang distributed with shape factor k An important aspect of the planning and control of product development projects is the estimation of the time needed for performing design tasks, and the estimation of the moment in time at which design tasks will be completed In the company where this empirical research was carried out, the estimation of processing times and completion times was done by the engineers and their project leader In the company, each engineer worked simultaneously on two to three design tasks and was allocated a new design task after completing a task Upon allocation of a design task the engineer estimated the processing time of the new task, estimated the remaining processing time of the tasks on hand, added these numbers up and divided the result by the number of hours available per week for processing design tasks The resulting number is the estimate of the number of weeks that will elapse before completion of the new design task (The estimated lead time of the design task.) Modelling and simulation 323 During execution, an engineer has to decide which of the design tasks on hand to work on When performing a design task an engineer can detect new design activities, not identified in the initial estimation Moreover, when performing a design task an engineer may be interrupted by another engineer that requires information or specific support for his work The firm strongly encourages engineers to give priority to helping their colleagues out when specific information or support is needed that only they can provide Finally, when performing a design task, an engineer may encounter some difficulties and switch temporarily to another design task For this design tasks execution process, Dragut and Bertrand (2008) developed a formal model, based on queuing theory, for calculating the distribution function of the actual lead time of a design task, the actual lead time being defined as the time that elapses between the allocation of the design task to an engineer and the completion time of that task The model requires parameter values for: N(n), the number of planned activities in design task n, μ, the exponential distribution parameter of the design activity processing time, g, the exponential inter-arrival time of unplanned activities, and p, the probability that the engineer will be interrupted during execution of a design task The model was empirically validated using data collected in the company from 10 engineers working on an electronic product development project.The engineers made lead time estimates for the design tasks that were allocated to them over a period of 20 weeks After a design task was completed, the realized lead time was noted In total, data on 424 design tasks were collected with estimated lead times ranging from 1–8 weeks Design tasks with equal estimated lead times are comparable in the sense that they represent situations where the engineers perceive the same total number of planned design activities to before the newly allocated design task is completed Comparison of the realized lead times per group of design tasks with equal estimated lead times therefore provides information of the lead time distribution as a function of estimated number of planned design activities The data collected allows for the formation of eight such groups Table 9.2 shows the frequency diagrams of realized lead times for the group with estimated lead times of one week, three weeks and eight weeks How has validation of the model been done? First we remark that the model contains four parameters; for three of them, their value needs to be estimated from the empirical data.Thus, the empirical data have to be split up into a subset that is used for parameter estimation and a subset that is used for model validation Second, we remark that ‘Occam’s razor’ must be applied Occam’s razor refers to the rule that if one of two models, a complex one and a less complex one, have the same explanatory power, the less complex model should be adopted Therefore, validation should start with the simplest version of the model, and only proceed with a more complex one if the simpler version is rejected by the data 324 J.Will M Bertrand and Jan C Fransoo Table 9.2 Frequency diagrams of realized lead times for the group with estimated lead time of week, weeks and weeks Realized time Estimated lead time week weeks weeks 48 26 11 0 0 0 10 10 0 11 1 12 0 13 0 14 0 15 1 16 0 17 1 18 0 19 0 20 0 21 22 23 0 24 0 25 0 Van Oorschot et al (2005) already checked the validity of a very simple model that assumed that completion of design tasks requires processing of a number of design activities proportional with the estimated lead time, which assumed that no new design activities will emerge and that no interruptions will occur They used the empirical data of design tasks with one week estimated lead time to estimate two parameters: the number of design activities per week estimated lead time and the average processing time per design activity; they used the other seven data sets to validate this model The model was Modelling and simulation 325 rejected by a goodness-of-fit test at D = 0.01 Thus, investigating the validity of more complex models was justified Dragut and Bertrand (2008) used the Kolmogorov-Smirnov goodnessof-fit test to investigate the validity of more complex models The Kolmogorov-Smirnov test finds the greatest discrepancy between the empirical and the theoretical cumulative distribution function, which is called the D-statistic The D-statistic is compared with the critical D-statistic for the sample size They first investigated a model with arrival of new design activities during execution of design tasks, but without interruption of design tasks The empirical data with estimated lead time of one week was used to estimate the three parameter values N (n ), m and J, resulting in the values N (n ) = 4L with L the estimated lead time in weeks, m = hours and g = 0.66 arrivals per week The model was validated on the other seven data sets The Kolmogorov-Smirnov tests showed that the model only agreed with the data set of two weeks estimated lead time For all other sets the model was rejected Thus, the introduction of the effect of interruptions in the model seems to be justified Using again the one week estimated lead time, the data set for parameter estimation resulted in N (n ) = 4L , m = hours, g = one week and p = 0.15 Testing this model on the other seven data sets showed that the null hypothesis that the empirical distribution is in accordance with the model could not be rejected The scientific contribution in this example of model-based empirical research is twofold First, the empirical data seem to corroborate the queuing model assumptions that have been used to develop the model that relates the realized design task lead time to the parameter values of the model Second, the results of the successive tests with the different models indicate the relevance of taking into account arrival of unplanned design activities and work interruptions for explaining the distribution of the realized design task lead times Often, model-based empirical research not only leads to corroboration or refutation of postulated formal models, but also inspires the researchers to new ideas or new hypotheses In the example we discuss here, studying the empirical data in the light of the test results inspired the researchers to include one other effect into the model For design tasks with estimated lead time larger than three weeks, the existence of a fixed delay, proportional with the estimated lead time, was postulated The fixed lead time dependent delay was justified by the fact that every now and then engineers were faced with the need to work on a completely new design task due to the reshuffling of the development project The resulting model was not only accepted in all cases, but also showed a very good fit from the probability density function point of view For details, refer to Dragut and Bertrand (2008) 9.5 Relevance In OM, relevance is generally justified by referring to real-life situations to which the model or problem might apply Assessing relevance has had a long history in the operations research journals The main debate addresses the 326 J.Will M Bertrand and Jan C Fransoo so-called ‘gaps’ between operations research theory and practice, bringing forward two issues: Why researchers not address more practically relevant problems, in terms of complexity, design and definitions? Why practitioners not make more use of all available tools and results that have been developed by the operations research community? In this chapter, we will not go into this debate, but refer to other work, such as Corbett and Van Wassenhove (1993), Ormerod (1997) and Reisman and Kirschnick (1995) An important observation in these articles is that progress in operations research seems to develop along a line that Reisman and Kirschnick denote as ‘ripple research’ With this, they refer to research that is conducted on small extensions of previous axiomatic research, and thus cannot bridge the gap that according to these articles apparently exists between the results of axiomatic research and the real-life need of decision makers It should be noted that in some areas, e.g allocation theory and inventory theory, series of small extensions have led to very useful models that have been applied in business practice on a large scale The relevance issue cannot be seen apart from the fact that mathematics, statistics and computer science not (yet) provide us with sufficiently powerful methods of analysis to address problems that come close to the complexity that is observed in most real-life operational processes The type of models studied in operations research is therefore restricted to those models that allow the researcher to analysis and to make scientific claims.This leads to the fact that for the axiomatic research the relevance criterion (with regard to the validity of the model versus reality) is usually applied very lightly In many cases, relevance is motivated by referring to earlier articles addressing similar issues, or by referring to general trends in the industry, rather than tying the relevance to actual observations in reality.The model is considered ‘acceptably relevant’ if the modelled problem can be recognized, possibly as an aspect-model of reality We would like to add an important criterion for relevance, apart from the validity issue.This is the question whether the solution of the model assists managers in making decisions in the real world This is the case if the aspect-model-based solution covers the most important part of the solution, and the context factors (not included in the model) are less relevant to the actual solution 9.6 Summary The use of modelling and simulation for researching OM issues was discussed OM was defined and the quantitative modelling process that underlies the modelling and simulation research approach was characterized How modelling and simulation evolved out of scientific management and developed into a distinctive research approach having specific strength and Modelling and simulation 327 weaknesses relative to other OM research approaches – such as survey research, case research, action research and operational research as a special instance of action research – was described Inventory control, forecasting, mathematical optimization and queuing theory were mentioned as examples of influential results produced by modelling and simulation research, and the major breakthroughs in industry relative to the contribution of modelling and simulation were positioned Modelling and simulation were characterized as descriptive or prescriptive axiomatic research and identified in their positions in the OM research model of Mitroff et al (1974) Guidelines on how to conduct qualitative model-based research, both for analytical research and for simulation research, were given and the guidelines for both types were demonstrated with an example Finally, guidelines for doing model-based empirical research were given and this research approach was also demonstrated with an example Bibliography Aarts, E H and Lenstra, J K (1997) Local Search in Combinatorial Optimization New York, NY: Wiley Ackoff, R L (1957) A comparison of OR in USA and Great Britain Operational Research Quarterly, 8: 88–100 Ackoff, R L (1979) The future of operational research is past Journal of the Operational Research Society, 30: 93–104 Ackoff, R L and Sasieni, M W (1968) Fundamentals of Operations Research New York, NY: Wiley Albin, S L (1984) Approximating a point process by a renewal process, II: Super- position arrival processes to queues Operations Research, 32(5): 1,133–1,162 Altiok,T (1997) Performance Analysis of Manufacturing Systems NewYork, NY: Springer-Verlag Amundson, S D (1998) Relationships between theory-driven empirical research in operations management and other disciplines Journal of Operations Management, 16: 341–359 Ball, M O., Magnanti, T L., Monma, C L and Nemhauser, G L (eds) (1995) Network Routing Amsterdam, the Netherlands: North-Holland Bertrand, J W M and Fransoo, J C (2002) Operations management research methodologies using quantitative modeling International Journal of Operations and Production Management, 22(2): 241–264 Bertrand, J W M and van Ooijen, H P G (2002) Workload based order release and productivity: A missing link Production Planning and Control, 13(7): 665–678 Bertrand, J W M and Wortmann, J C (1981) Production Control and Information Systems for Component-manufacturing Shops Amsterdam, the Netherlands: Elsevier Best, J B (1995) Cognitive Psychology St Paul: West Publishing Company Blocher, J D., Garrett, R W and Schmenner, R W (1999) Throughput time reduction: Taking one’s medicine Production and Operations Management, 8: 357–373 Box, G E P and Jenkins, G M (1976) Time Series Analysis: Forecasting and Control, 2nd edn San Francisco, CA: Holden-Day 328 J.Will M Bertrand and Jan C Fransoo Buss, A H., Lawrence, S R and Kropp D H (1994),Volume and capacity inter-action in facility design IIE Transactions, 26(4): 36–49 Buzacott, J A and Shantikumar, J G (1993) Stochastic Models of Manufacturing Systems New York, NY: Prentice Hall, Englewood Cliffs Cheng, C H., Feiring, B R and Cheng, T C E (1994) The cutting stock problem: A survey International Journal of Production Economics, 36: 291–305 Corbett, C J and Van Wassenhove, L N.(1993) The natural drift (What happened to operational research?) Operations Research, 41: 625–640 De Kok, A G and Graves, S C (eds) (2003) Supply Chain Management: Design, Coordination and Operation, Handbooks in Operations Research and Management Science, Vol 11 Amsterdam, the Netherlands: North-Holland DeHoratius, N and Raman, A (2000) Retail performance improvement through appropriate store manager incentive design: An empirical analysis Working Paper Boston: Harvard Business School Dragut, A B and Bertrand, J W M (2008) A representation model for the solving time distribution of a set of design tasks in new product development (NPD) European Journal of Operational Research, 189: 1,217–1,233 Fine, C H (1998) Clockspeed: Winning Industry Control in the Age of Temporary Advantage Reading, UK: Perseus Books Fisher, M L and Raman, A (1999) Tutorial: Retail operations Presented at INFORMS Fall Meeting Philadelphia: INFORMS, Linthicum Forrester, J W (1961) Industrial Dynamics Cambridge, MA: MIT Press Fransoo, J C and Wiers, V C S (2006) Action variety of planners: Cognitive load and requisite variety Journal of Operations Management, 24(6) 813–821 Graves, S C., Rinnooy Kan, A H G and Zipkin, P H (eds) (1993) Logistics of Production and Inventory Amsterdam, the Netherlands: North-Holland Handfield, R B and Melnyk, S A (1998) The scientific theory-building process: A primer using the case of TQM Journal of Operations Management, 16: 321–339 Hax, A C and Meal, H C (1975) Hierarchical integration of production planning and scheduling In: Geisler, M A (ed.) Logistics, 53–69 Amsterdam, the Netherlands: North-Holland Hendricks, K B and Singhal,V R (2003) The effect of supply chain glitches on shareholder wealth Journal of Operations Management, 21(5): 501–522 Herzog T (1996) Research Methods in the Social Sciences New York, NY: Harper Collins Holmström, J (1994) The relationship between speed and productivity in industry networks: A study of industrial statistics International Journal of Production Economics, 34: 91–97 Holt, C C., Modigliani, F., Muth, F and Simon, H A (1960) Planning Production Inventories and Workforce New York, NY: Prentice-Hall, Englewood Cliffs Hopp,W J and Spearman, M L (2001) Factory Physics Boston, MA: Irwin/McGraw Hill Inman, R R (1999) Empirical evaluation of exponential and independence assumptions in queuing models of manufacturing systems Production and Operations Management, 8: 409–432 Keys, P (1991) Operational Research and Systems: The Systemic Nature of Operational Research New York, NY: Plenum Press Modelling and simulation 329 Kleijnen, J P C and Van Groenendaal, W (1992) Simulation: A Statistical Perspective Chichester, UK: Wiley Kleinrock, L (1975) Queuing Systems:Vol 1:Theory New York, NY: Wiley Lambrecht, M R., Ivens, P L and Vandaele, N J (1998) ACLIPS: A capacity and lead time integrated procedure for scheduling Management Science, 44(11): 1,548–1,561 Law, A M and Kelton,W D (2000) Simulation Modeling and Analysis, 3rd edn New York, NY: McGraw-Hill Lenstra, J K., De Paepe,W E., Sgall, J., Sitters, R A and Stougie, L (2001) Computer-aided complexity classification of dial-a-ride problems Working Paper Eindhoven, Germany: Department of Mathematics and Computing Science, Technische Universiteit Eindhoven Lieberman, M B and Demeester, L (1999) Inventory reduction and productivity growth: Linkages in the Japanese automotive industry Management Science, 45: 466–485 Marcoulides, G A and Schumacker, R E (eds) (1996) Advanced Structural Equation Modeling: Issues and Techniques New York, NY: Lawrence Erlbaum Melnyk, S A and Handfield, R B (1998) Preface: May you live in interesting times:The emergence of theory-driven empirical research Journal of Operations Management, 16: 311–319 Mendelson, H and Pillai, R R (1998) Clockspeed and information response: Evidence from the information technology industry Information Systems Research, 9: 415–433 Meredith, J R., Raturi, A., Amoako-Gyampah, K and Kaplan, B (1989) Alternative research paradigms in operations Journal of Operations Management, 8: 297–326 Mitroff, I I., Betz, F., Pondy, L R and Sagasti, F (1974) On managing science in the systems age: Two schemas for the study of science as a whole systems phenomenon Interfaces, 4(3): 46–58 Muth, J F (1986) Search theory and the manufacturing progress function Management Science, 32(8): 948–962 O’Leary-Kelly, S W and Vokurka, R W (1998) The empirical assessment of construct validity Journal of Operations Management, 16: 387–405 Ormerod, R J (1997) An observation on publication habits based on the analysis of MS/OR journals OMEGA, 25: 599–603 Reed, S K (1988) Cognition: Theory and Applications Pacific Grove, CA: Brooks/ Cole Publishing Company Reisman, A and Kirschnick, F (1994) The devolution of OR/MS: Implications from a statistical content analysis of papers in flagship journals Operations Research, 42: 577–588 Reisman, A and Kirschnick, F (1995) Research strategies used by OR/MS workers as shown by an analysis of papers in flagship journals Operations Research, 43: 731–740 Schmenner, R W (1988) The merit of making things fast Sloan Management Review, 30(1): 11–17 Schmenner, R W and Swink, M L (1998) On theory in operations management Journal of Operations Management, 17: 97–113 Schonberger, R (1982) Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity New York, NY: Free Press 330 J.Will M Bertrand and Jan C Fransoo Silver, E A., Pyke, D F and Peterson, R (1998) Inventory Management and Production Planning and Scheduling, 3rd edn New York, NY: Wiley Sterman, J D (2000) Business Dynamics: Systems Thinking for a Complex World Boston, MA: Irwin/ McGraw-Hill Suri, R., Sanders, J L and Kamath, N (1993) Performance evaluation of production networks In: Graves S C., Rinnooij Kan, A H G and Zipkin P H (eds) Handbook in Operations Research and Management Science: Vol 4, Logistics of Production and Inventory, Chapter Amsterdam, the Netherlands: North Holland Taylor, F W (1911) The Principles of Scientific Management New York, NY: Harper & Row (Reprinted as part of a collection: Scientific Management, Harper & Row, 1947.) Van Oorschot, K E., Bertrand, J W M and Rutte, C G (2005) Field studies into the dynamics of product development tasks International Journal of Production and Operations Management, 25(8): 720–739 van Zelst, S., van Donselaar, K H., van Woensel, T., Broekmeulen, R A C M and Fransoo, J C (2009) Logistics drivers for shelf stacking in grocery retail stores: Potential for efficiency improvement International Journal of Production Economics, 121(2): 620–632 Wacker, J G (1998) A definition of theory: Research guidelines for different theorybuilding research methods in operations management Journal of Operations Management, 16: 361–385 Wiendahl, H P (1987) Belastungsorientierte Fertigungssteuerung: Grundlagen,Verfahren- saufbau, Realisierung Munich, Germany: Hanser Wight, O W (1974) Production and Inventory Management in the Computer Age Boston, MA: CBI Wright, T P (1936) Factors affecting the cost of airplanes Journal of Aeronautical Sciences, 3: 122–128 Yelle, L E (1979) The learning curve: Historic review and comprehensive survey Decision Sciences, 10: 302–328 Yin, R.W (1994) Case Study Research: Design and Methods, 2nd edn.Thousand Oaks, CA: SAGE Publications Zipkin, P H (2000) Foundations of Inventory Management London, UK: McGraw-Hill Zoryk-Schalla, A J., Fransoo, J C and De Kok, A G (2004) Modeling the planning process in advanced planning systems Information and Management, 42: 75–87 Index abduction 20–1 academia 7–8, 14, 25 access 72–3, 74–5, 77, 176, 179, 200, 202, 205–7,240, 242–3, 248, 280, 284, 287 Ackoff, R 296, 299 action research 14, 28, 54, 62, 70–1, 73–5, 233–67, 269, 272–6, 282–3, 285–6; characteristics 236–9; constructing 248–9; critical themes 235; data collection 238, 245, 256–7, 259; definition 234–5; design of project 241–4; dissertations 262–3; evaluating action 251–2; implementation 244–52; meta learning 252–5; origins 239; philosophy of 235–6; planning action 249–50; positioning 240–1; positivist science contrast 238–9, 255, 256; publication 263–4; quality 238–9, 256–60, 264; skills 260–2; taking action 250–1; theory generation 255–6, 264 Ahire, S.L 133–5, 137 Åhlström, Pär 174, 176, 198–232 analytical research 15, 23–4, 42, 274–6, 280, 283, 305–14, 316–18, 327 Anderson, J.C 82–3, 97–8, 100, 140 argument 20–1 Argyris, C 253, 257 Aristotle 18 arrays 187, 189–90, 206 axiomatic research 290, 291, 298, 299–300, 301–20, 326, 327 Bagozzi, R.P 83, 132–3, 145–6 Barnes, D 87, 94 Becker, H.S 210 benchmarking 135, 176, 199 Bertrand, J Will M 290–330 bias 41, 110, 115–18, 126–8, 137–40, 142, 148–9, 174–5, 178–9, 183, 204, 226 bibliographies 60–1 Boyer, K.K 91, 94, 102, 109, 117–18, 122, 172, 180 Brannick, T 247, 252–3 Brown, K 184 Burrell, G 53, 71 business schools 11, 15, 292–4 Carmines, E.G 129–31 case research 21, 62–3, 70–1, 74–5, 165– 97, 273–6, 327; choosing cases 174–7, 194; coding 186–7, 194; conducting field research 179–84; conducting interviews 180–4; constructs 170–1; data analysis 187–93, 194; data collection 180–4; documentation 185–6, 192; generalizability 174–5, 176, 189, 275; and longitudinal field studies 198–9, 201, 205, 207, 210, 222–3, 228; reliability and validity 184–5; research framework 171–4; research instruments 177–9; sampling 175–7; when to use 167–71 causal networks 171, 184–5, 188–9 cause and effect 23, 25, 49, 175, 183, 200, 218, 228, 237 change: action research 234, 236–7, 241, 243; empirical research 301; longitudinal field studies 198, 199, 201–8, 214, 215–16, 219, 221–2, 226, 228–9 Choi, T.Y 134, 172, 173 citation searches 58 332 Index clinical research 70–1, 73–5,181, 206, 225, 242, 268–89 coding 24, 142, 169, 185–7, 216–21, 224–6, 229, 283 Coghlan, David 233–67 Cohen, J 106 collaboration 123, 234–6, 239–41, 244–5, 248–9, 250, 258–61, 264, 271, 273–4, 286 Collins, R.S 90, 110, 124–5 conceptual definitions 47, 98–9, 105 conceptual modelling 49, 62, 95, 98, 302–3, 306, 307, 308, 311, 313, 319, 321 concurrent validity 134 confirmatory surveys see theory-testing surveys consent 39–40, 179 construct validity 30–1, 91, 94, 294, 318; case research 184–5, 192; surveys 132–4, 135 constructivism 29 constructs 24–5, 31, 47–8, 52, 62–4, 67, 98, 100–102, 104–8, 129–30, 132–7, 168, 171, 174, 190–3, 221 content validity 91, 98, 99, 105–8, 130, 132, 135, 142, contingency theory 51 convergent validity 107, 133, 136–7, 184 Corbett, L.M 81 Corbin, J 186 Cordon, C 90, 110, 124–5 core action research 246, 247–8, 250–4, 257–8, 262, 264 corroboration 227 Coughlan, Paul 233–67 creative thinking 28, 191, 222 criterion-related validity 134–5, 326 critical incidents 187, 208–9, 281–3, 287 Cronbach’s alpha 131, 135, 136–7 cross-sectional research 138–9, 199 data collection: action research 238, 245, 256–7, 259; case research 180–4; empirical research 319; longitudinal field studies 200, 202, 206–8, 210–14, 215, 218–20, 223–4, 226, 228–9; surveys 80–3, 85, 87, 92–5, 97, 100–2, 104–5, 107, 108, 110–11, 114, 115–18, 122–3, 129, 131, 135, 137–9, 149–53 data input 110, 116, 122, 129 data reduction 96, 142, 187, 188, 214, 225, 229 databases 58, 112, 128–9, 217–18, 224, 229 datum 215 decision rules 208–9, 215, 229, 310–11 deduction 20–1, 83 definitions: conceptual 47, 98–9, 105; operational 32, 95, 102–8, 320 descriptive research 15, 16, 23, 42, 66–7, 76; axiomatic 303, 305–9, 327; clinical management research 270, 280, 281; empirical 291, 300–1; surveys 80, 83–4, 96, 100–1, 142–3, 148, 150 design tasks 322–5 diagnosis 253–4, 280–1 Dillman, D.A 115, 116, 123, 125, 127 discriminant validity 99, 106, 133–4, 136–7, 184 displays 187–8, 190, 219, 221 dissertations 254, 262–3, 278–9 documents 210, 212–15, 280 Dragut, A.B 323, 325 Dreyfus, P 130, 133–5, 137 Eisenhardt, K.167, 181, 184, 187, 190, 193, 222 electronic surveys 116–17 empirical research 80, 84, 87–90, 141, 168, 297–9, 300–4, 318–25 errors 41, 59, 91–2, 109, 113–14, 122, 123, 127, 128–30, 139, 142, 145–6, 148, 314 ethics 9, 39–41, 44, 150–1, 237–8 ethnographic research 167, 179, 200, 272 Europe/European Union 39–40, 83, 166, 290 evidence 20, 27, 50–1 experiments 23, 191, 222, 313, 316, 318 explorative research 15, 23, 24, 27, 29, 42, 66–7, 70–1: case research 167–8, 170, 273; surveys 80–1, 82–3, 84–5, 95–8, 100–1, 110, 135, 145, 148–50 external validity 30–1, 127, 148, 174–5, 185, 192 face validity 98–9, 105–6, 110, 131 Filippini, R 83, 90 Fisher, M.L 319 Flynn, B.B 88, 90, 119, 123–4 Index Forrester, J.W 295–6, 300–1 Forza, Cipriano 79–164 Fowler, F.J Jr 126–7 Fransoo, Jan C 290–330 generalizability 29, 43, 284, 287; case research 174–5, 176, 189, 275; longitudinal field studies 204, 222, 226, 227–8, 229; surveys 85, 100, 112, 113–14, 126, 142, 149, 153 Goldstein, S.M 92, 146 Graebner, M.E 193 graphical techniques 143, 219, 221, 223, 229 ‘grounded theory’ 24, 166 Gummesson, E 236–9 Hair, J.F Jr 119 Hill, C.A 87, 89, 141 Huberman, M 27–8, 49, 171, 175–6, 187–8, 190, 214, 217 Husseini, M.S.M 83–4 Hyer, N.L 168–9, 172, 180, 184 hypotheses 21, 25, 28, 48, 65–7; axiomatic research 312; case research 168–9, 190–1; clinical management research 272, 273, 276, 283, 284; empirical research 319–21; surveys 83, 87, 95, 101–3, 107–8, 109–10, 124, 138, 142, 145–7, 149–50 idealized problems 290, 292–4, 295, 299, 307 induction 20–1, 83, 168 industrial dynamics 295–6, 300–1 inference 20, 21–2, 93, 102, 113, 129, 148, 206, 208, 218, 262, 280, 282 innovation 83, 183, 202, 205, 207, 216, 229, 237, 243, 247 internal validity 30–1, 109, 148, 153, 184–5, 190, 192, 222, 283 interviews 14, 15–16, 74–5, 238, 273, 280–3; case research 166, 167, 177–83, 186–7; longitudinal field studies 200, 202, 209–10, 212–13, 215, 223, 226, 228; surveys 110, 113, 115–18, 125, 129, 139 Jacobs, J 91, 112, 116–18 Japanese manufacturing techniques 174, 297 333 Jayaram, J 173, 174 job shop production systems 304–5, 308–10, 313–15, 319–21 journal keeping 75, 262 journals 13, 34–5, 36–9, 41, 43, 47, 52, 58, 88–9, 137, 142, 148, 166, 262, 263, 276, 285 justice 40, 44 Karlsson, Christer 1–6, 97, 174, 186; clinical management research 268–89; longitudinal field studies 198–232; research in operations management 7–45 Ketokivi, M.A 88, 92, 93, 104, 137, 138–9 Keys, P 299 Kim, S.W 82, 108 Kirschnick, F 302, 326 Klassen, R.D 91, 116–18 laws 41 Lawshe, C.H 105–6 lean production 65, 98, 137, 166, 201–3, 205, 207–9, 216, 271, 279 learning 103, 234, 236, 244–5, 252–5, 260, 261–4 learning curve 295–6 Leonard-Barton, D 167, 175, 178, 180, 182, 201–4, 206, 208 Levin, M 238–9, 256 Lewin, Kurt 236, 239, 246 literature review 17, 37, 38, 57–64, 97, 104 longitudinal field studies 12, 67, 70–1, 73, 75, 172–3, 174–5, 178, 187, 198–232, 269, 282–5; access to data 205–7; as clinical research 206, 225; and cross-sectional research 199; data analysis 214–20, 229; data collection 200, 202, 206–8, 210–14, 215, 218–20, 223–4, 226, 228–9; demands on researcher 204–5, 209–10, 224, 226; evaluation of theory 223–8, 229; framework for data gathering 207–9, 228; generalizability 204, 222, 226, 227–8, 229; in operations management 200–1; as real-time study 199–200; research question 199, 201, 202–4, 206–8, 215, 218, 225, 228; setting up ...Research Methods for Operations Management Second Edition Research Methods for Operations Management, second edition, is a toolkit of research approaches primarily for advanced students... to a strategy for operations and for how the operations help the firm to compete in the market Operations systems are designed concurrently with the products and services that the operations system... experience.’ Professor Andy Neely, Head Institute for Manufacturing, Cambridge and President, European Operations Management Association Research Methods for Operations Management Second Edition Edited
Ngày đăng: 28/07/2020, 00:18
Xem thêm: