lean manufacturing management- the relationship between practice and firm level financial performance

LEAN MANUFACTURING MANAGEMENT: THE RELATIONSHIP BETWEEN PRACTICE AND FIRM LEVEL FINANCIAL PERFORMANCE DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doc

LEAN MANUFACTURING MANAGEMENT: THE RELATIONSHIP BETWEEN

PRACTICE AND FIRM LEVEL FINANCIAL PERFORMANCE

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

By Eric Oscar Olsen, B.S., M.B.A., M.A

Professor William L Berry

Professor David A Collier Advisor

Graduate Program in Business Administration Professor Anil K Makhija

ABSTRACT

The relationship between lean manufacturing management practices and business financial performance is examined through the use of empirical surveys and archival accounting data from Compustat and stock return data from CRSP A sample frame

of small to medium sized discrete product and process manufacturing companies reporting participation in only one four-digit SIC was identified as the sample frame The five-year (1998-2002) financial performance for these companies was analyzed

at the operations and business levels using a median z-score comparing median firm performance with the median performance of a matched portfolio of firms

Operations measures included asset and employee productivity, gross margin ratio and two measures of aggregate cycle time Business measures included return on equity (ROE), sales growth, and stock return A web-based survey was used to

collect data on seven lean practices including just-in-time production management, statistical process control, total productive maintenance, group technology, employee involvement, supplier communication, and customer involvement Forty-two

responding firms were classified as being either lean or non-lean based on a cluster analysis of factor scores The results demonstrated that lean practices act as a

synergistic, mutually supportive set rather than linearly additive individual practices

in affecting operations financial performance Lean classification was associated with better total and cash-to-cash cycle times, but was not related to either better or worse asset or employee productivity Lean firms also tended to have narrower grow margins than non-lean firms With respect to business level performance, lean firms tend to have better ROE, but no relationship was found with respect to either stock return or sales growth Of all the lean practices tested only employee involvement demonstrated a significant relationship to business level performance Firms with high ROE tend to have high employee involvement A literature review topology is presented to demonstrate the need for studies combining empirical survey data and archival measures of performance Opportunities for future research are outlined

ivDedicated to Dawn, Kaitlin, and Skylar;

whose love and support has made all the difference

ACKNOWLEDGMENTS

A debt of gratitude is owed to my committee for allowing me to take the road “less traveled by.” I especially thank Peter Ward for guiding me down the path of lean

performance measurement, Bill Berry for pointing out the parallels between lean

management ideals and value investing, Dave Collier for his support of cycle time as a critical measure, and Anil Makhija for guidance in financial performance measurement

This work could not have been accomplished without the support of my peer doctoral students at the Fisher College of Business This is especially true of three individuals who put down their own work to help me with mine I thank Stefan Hocke for putting together a web site while I was trying to determine how to collect data without one, Sebastian Garcia-Dastugue for helping my daughter Kaitlin and I create an Access

database and putting up with our novice questions, and Maurice Reid for reviewing my work while studying for his own general exam

In regards to getting my survey done, I thank the Ohio State Center for Survey Research for their insights and assistance in collecting data I thank the Center for Excellence in Manufacturing Management for their sponsorship of my survey

A person could not ask for a better extended family I acknowledge the love and

influence of my father, John Olsen, who knows no fear when it comes to tackling a project and my mother, Alta, whose sense of humor made those projects bearable The Morgan Orchard represents the road less traveled for a previous generation Addie and Marshall Ritter provided a constant source of refuge and strength as well as a rigorous review of a draft manuscript

Finally, I would like to thank the friends, friends of friends, significant others, and kids who attended the weekly happy hours over the 5 years I was in the PhD program at Ohio State You are welcome to come over any Friday night

VITA

February 25, 1957 Born – East Orange, New Jersey

Education

1979 B.S Forest Engineering, University of Maine at Orono

1987 M.B.A Virginia Polytechnic Institute and State University

Fisher College of Business

1999 – 2003 Graduate Teaching and Research Associate, The Ohio State University

2004 – Present Lecturer, California Polytechnic State University, San Luis

Obispo, California

Industry

1979– 1981 Engineer, Caterpillar Tractor Company, Peoria, Illinois

1982 – 1988 Engineering Manager, Litton Industries, Blacksburg,

Virginia

1988 – 1999 Engineering, Production, Education Manager, Hewlett

Packard/Agilent Technologies, San Jose, California

FIELDS OF STUDY Major Field: Business Administration

Concentration: Operations Management Minor Field: Management of Human Resources

TABLE OF CONTENTS

Page

Abstract………ii

Dedication……… iii

Acknowledgements……….iv

Vita……… v

List of Tables……… vi

List of Figures ……… vii

Chapters: 1 Introduction ……… 1

1.1 Research background ……… 1

1.2 Research problem ……… … 5

1.3 Research method ……… 7

1.4 Importance and contribution of research ……… 10

1.5 Organization of the dissertation ……… … 12

2 Literature review ……….… 13

2.0 Objectives and map of domain ……… … 13

2.1 Survey-perceptual (category 1) studies ……… … 21

2.1.0 Description and objectives ……… … 21

2.1.1 Lean practice measurement ……… 23

2.1.2 Lean operations performance measurement ……… … 25

2.1.3 Summary ……….… 28

2.2 Announcement-archival (category 2) studies ……….… 28

2.2.0 Description and objectives ……… … 28

2.2.1 Lean company identification ……… … 29

2.2.2 Lean financial performance measurement ……… … 32

2.2.2.1 Operations measures ……….… 32

2.2.2.2 Business measures ……… … 35

2.2.3 Methods of financial performance analysis ……… 38

2.2.3.1 Comparison formation ……… 38

2.2.3.2 Measures of central tendency ……….… 40

2.2.3.3 Comparison time span ……… 41

2.2.3.4 Methods of analysis ……… 42

2.2.4 Summary ……… 43

2.3 Survey-archival (category 3) studies ……… … 44

2.3.0 Description and objectives ……… … 44

2.3.1 Performance measurement ……….… 45

2.3.2 Practice measurement ……….… 46

2.3.3 Methods of analysis ……… 47

2.4 Summary and literature-based justification for study ……… 49

3 Research Propositions……… 51

3.0 Research propositions – objectives ……….… 51

3.1 Lean archetype versus operations financial performance (P1) … 53

3.1.1 Theoretical support ……….… 53

3.1.2 Empirical support ……… … 55

3.2 Individual lean practice versus operations financial performance (P2) ……… 58

3.2.1 Relationship to Simple Linear Combinations of Practices …….… 58

3.2.1.1 Theoretical Support ……… 58

3.2.1.2 Empirical Support ……… … 59

3.2.2 Relationship between individual practice and performance measures ……….… 60

3.3 Lean archetype versus business financial performance (P3) … … 62

3.3.1 Theoretical support ……….… 62

3.3.2 Empirical support ……… … 66

3.4 Individual lean practices versus business financial performance (P4) ……….… 68

3.4.1 Relationship to simple linear combinations of practices ………… 68

3.4.1.1 Theoretical support ……….… 68

3.4.1.2 Empirical support ……… … 69

3.4.2 Relationship between individual practice and performance measures ……….… 70

3.5 Lean operations versus business financial performance (P5) ….… 71 3.6 Summary ……….… 72

4 Methods ……… 73

4.0 Methods – objectives ……… 73

4.1 Sample frame ……… 77

4.1.0 Objectives ……… 77

4.1.1 Availability of archival financial data ……… 78

4.1.2 Business scope ……… 79

4.1.3 Size restriction ……… 80

4.1.4 Industry group ……… 80

4.1.5 Years of data available ……… 82

4.2 Financial performance variables ……… 82

4.2.0 Objectives ……… 82

4.2.1 Business financial performance variables ……… 85

4.2.1.1 Return on equity (ROE) ……… 86

4.2.1.2 Sales growth (SG) ……… 87

4.2.1.3 Stock return (SR) ……… 88

4.2.2 Operations financial performance variables ……… 90

4.2.2.1 Asset productivity – return on cash-adjusted assets (ROCA) …… 90

4.2.2.2 Employee productivity (EP) ……… 91

4.2.2.3 Gross margin ratio (GMR) ……… 92

4.2.2.4 Cycle time ……… 92

4.2.2.4.1 Cash-to-cash cycle time (CTC) ……… 94

4.2.2.4.2 Total cycle time (CTT) ……… 95

4.2.2.4.3 Inventory cycle time (CTI) ……… 95

4.2.2.4.4 Accounts receivable cycle time (CTR) ……… 96

4.2.2.4.5 Accounts payable cycle time (CTP) ……… 96

4.2.3 Financial performance median z-score calculation ……… 97

4.3 Lean manufacturing practice variables ……… 102

4.3.0 Objectives ……… 102

4.3.1 Survey instrument ……… 103

4.3.2 Data collection ……… 104

4.3.3 Survey sample validation ……… 108

4.3.3.1 Sample frame representativeness ……… 109

4.3.3.2 Response bias ……… 110

4.3.4 Practice construct formulation ……… 112

4.3.4.1 Factor analysis ……… 113

4.3.4.2 Construct reliability ……… 115

4.3.4.3 Factor measurement ……… 115

4.3.5 Lean archetype formation ……… 116

4.3.5.1 Cluster analysis ……… 117

4.3.5.1.1 Cluster results ……… 119

4.3.5.1.2 Cluster validation ……… 121

4.4 Relationship analysis ……… 127

4.4.0 Objective ……… 127

4.4.1 Relationship between lean archetypes and operations financial performance ……… 128

4.4.2 Relationship between individual lean practices and operations financial performance……… 128

4.4.3 Relationship between lean archetypes and business financial Performance ……… 129

4.4.4 Relationship between individual lean practices and business financial performance ……… 129

4.4.5 Relationship between lean operations and business financial Performance ……… 130

4.5 Summary ……… 131

5 Results ……… 134

5.0 Results – objectives and overview ……… 134

5.1 Lean versus operations financial performance (L-O) ……… 137

5.1.1 Variable description ……… 137

5.1.1.1 Lean practice ……… 137

5.1.1.2 Operations financial performance ……… 138

5.1.2 Analysis and results ……… 138

5.1.2.1 Lean archetype versus operations financial performance ………… 139

5.1.2.2 Individual lean practice versus operations financial Performance ……… 143

5.1.2.3 Lean versus operations performance summary ……… 144

5.2 Lean versus business financial performance (L-B) ……… 145

5.2.1 Variable description ……… 145

5.2.2 Analysis and results ……… 145

5.2.2.1 Lean archetype versus business financial performance ………… 145

5.2.2.2 Individual lean practice versus business financial performance … 148

5.2.3 Lean versus business performance summary ……… 150

5.3 Lean operations versus business financial performance (O-B) …… 152

5.3.1 Overview ……… 152

5.3.2 Analysis and results ……… 153

5.4 Summary ……… 160

6 Discussion ……… 161

6.0 Discussion – objectives and overview ……… 161

6.1 Lean archetype practice composition ……… 161

6.2 Lean versus operations financial performance (L-O) ……… 164

6.3 Lean versus business financial performance (L-B) ……… 170

6.4 Lean operations versus business financial performance (O-B) …… 173

6.5 Threats to validity ……… 174

6.5.0 Objectives ……… 174

6.5.1 Threats to statistical conclusion validity ……… 174

6.5.2 Threats to internal validity ……… 177

6.5.3 Threats to external validity ……… 180

6.6 Summary ……… 182

7 Conclusion ……… 184

7.1 Research objective ……… 184

7.2 Key findings ……… 186

7.3 Managerial implications ……… 190

7.3.0 Overview and objectives ……… 190

7.3.1 Lean implementation ……… 190

7.3.2 Absolute magnitude of lean effects ….……… 192

7.4 Future research ……… 194

Bibliography ……… 197

Appendices ……… 204

Appendix A - Complete listing of sample frame companies with 2001 data……… 204

Appendix B – Survey of manufacturing practices ……… 210

Appendix C - Example initial solicitation letter ……… 212

Appendix D - Example follow-up letter ……… 215

Appendix E - Sample of rankings and percentile performance rating viewable upon completion of the web based survey by respondents ………… 218

Appendix F - Full factor loading and factor score matrix for lean practices ……… 219

LIST OF TABLES

2.1 Practice and performance coverage of key studies ……….… 19

2.2 Other relevant features of practice performance studies ……… 20

2.3 Lean practices – frequency and significance in recent OM literature …… 22

2.4 Major practice constructs for lean operations ……….… 25

2.5 Performance measures – frequency and significance in recent OM literature ……… … 27

2.6 Performance measures used in archival data studies ……… … 35

3.1 First order mechanisms connecting lean “seven wastes” to operations financial measures ……….……… 58

3.2 Lean practices and financial performance measures ……… … 62

4.1 Research methods overview and highlights ……… … 74

4.2 Summary of descriptive characteristics for firms included in the sample frame Data is for fiscal year 2001 ……… 78

4.3 Financial performance measures ……… 85

4.4 Reference list of Compustat data descriptions ……… ……… 87

4.5 Descriptive statistics for sample frame financial performance pseudo z-scores ……… …… 100

4.6 Lean practices included in this study ……… 103

4.7 Descriptive statistics by survey response category ……… 108

4.8 Comparison of sample and sample frame statistics ……… 110

4.9 Self reported respondent titles sorted by frequency ……… 1124.10 Factor analysis items, loading, factor score weights and reliability ….…… 1144.11 Mean values for two and three cluster analysis solutions ……… 1204.12 Structure matrix for lean and non-lean cluster discriminant function …… 1254.13 Median z-scores for operations financial performance variables ………… 1265.1 Lean versus non-lean firm median z-score differences for the original

sample and resampled data with replacement ……… 1435.2 Lean cluster difference test results for business financial performance … 146

5.3 Comparisons between above and below competitive median business

financial performers with respect to lean practice level ……… 150

5.4 Survey sample data ranges versus non-respondent data pre- and

post-trimming ……… 1565.5 Business financial performance differences for lean and non-lean firms

predicted by lean financial signature models ……… 1565.6 Business financial performance differences for lean and non-lean firms by

lean financial signature models and probability cutoff level ……… 1565.7 Predicted differences in financial performance variables using Model 2

6.1 Threats to statistical conclusion validity … ….….….….….….….….…… 1756.2 Threats to internal validity ….….….….….….….….….….….….….…… 1776.3 Threats to external validity ….….….….….….….….….….….….….…… 180

4.5 Binary logit models predicting cluster membership including cash-to-cash

cycle time (a) or total cycle time (b) ….….….….….….……… 1275.1 Relationships between variables investigated ….….….….….….….….… 1375.2 Plots of lean (L) and non-lean (N) clusters’ operations financial

performance (i.e ROCA, EP, GMR) ….….….….….….….….….….….… 1425.3 Plots of lean (L) and non-lean (N) clusters’ cycle time operations

financial performance ….….….….….….….….….….….….….….….…… 1425.4 Plots of lean (L) and non-lean (N) clusters versus business financial

performance measures ….….….….….….….….….….….….….….….… 147

5.5 Binary logit model predicting above (Hi) or below (Lo) competitive

median ROE performance ….….….….….….….….….….….….….….… 1515.6 Plots of above (Hi) and below (Lo) competitive median ROE groups

for lean practice scores on SCM and EMP (Data) ….….….….….….…… 151

5.7 Binary logit model predicting cluster membership with total cycle time,

gross margin ratio, and their interaction ….….….….….….….….……… 1597.1 Proposed lean implementation model based on study results ….….….… 192

substantiating this relationship with respect to the broad set of operations commonly known as “lean manufacturing” practices Lean manufacturing may be considered as a synergistic set of integrated modern manufacturing management practices commonly classified under subsets of just-in-time (JIT), total quality management (TQM), total productive maintenance (TPM), and a collection of supportive human resource

management practices including teamwork and employee empowerment Lean

manufacturing encompasses such practices as employee involvement in problem solving, statistical process control (SPC), reengineering setups, cellular manufacturing, supplier information sharing and partnership, supply base rationalization, pull production, worker teams, integrated product design, in-house designed technology, and customer

requirements integration

The Toyota Motor Corporation offers a preeminent example of a successful lean

manufacturer (Bremner & Dawson, 2003; Drickhamer, 2004; Womack, Jones, & Roos, 1991) For 50 years, Toyota has continuously improved its version of lean practice under the banner “Toyota Production System (TPS).” TPS performance positions Toyota to rival DaimlerChrysler, Ford, and General Motors for world auto market domination A

recent BusinessWeek cover article points out that although other auto manufacturers may

surpass the auto maker’s individual performance measures, “no car company is as strong

as Toyota in so many areas….Its operating profit margin of 8% now dwarfs those of Detroit’s Big Three” (Bremner & Dawson., 2003) Not surprisingly, Toyota’s financial and market success relies largely on lean manufacturing management practices

Nevertheless, the proof that lean works for the broad spectrum of manufacturing firms is specious Even as practitioners attest that proof exists, studies by both operations

management (OM) and finance researchers have proven inconsistent in establishing a significant positive relationship between lean practices and archival business financial performance In all fairness, most research studies find a positive association with at least one or two financial measures Reductions in some form of inventory consistently occur in lean implementations Yet measures of return on assets (ROA), return on sales (ROS), return per employee, and profit margin prove inconsistent

The ambiguity of even “good” empirical research on this question results from three problems The first relates to the managerial perceptions of performance typically used

as the dependent variable in operations management empirical studies Managers assess

one or more of the following: performance relative to competitors, change in

performance, internal goal, and absolute performance for specific measures Surveys asking both practice and performance questions of the same manager run a high risk of encouraging biased responses Most studies of this nature find a positive relationship between lean practices and perceived financial performance Managers who are

responsible for, or involved in, implementing lean practices may tend to notice only those performance indicators that affirm their belief An independent source of performance data is required to enhance the validity of these research studies

The second problem occurs in accounting and finance studies of the practice-performance relationship These studies rely on more objective, archival sources of financial

performance data along with rigorous statistical analysis of that data These studies tend

to lack clarity in defining what they consider either a lean or a non-lean firm They depend on public announcements or other forms of self-identification as indicators of implementing specific practices Not surprisingly, these studies arrive at mixed results

The third problem concerns the practice of measuring lean practices individually, or in limited subsets, to gauge their relationship to performance This research maintains that lean practices act as a synergistic whole in affecting operations and financial

performance Therefore, it is not unusual that research studying the individual effects of JIT, TQM, or TPM as subsets of practices often fall short of accounting for significant changes in financial performance

The major contribution of this research is to more definitively assess the

practice-performance relationship by using both well-validated practice survey questions (for assessing the actual level of lean implementation) and archival performance data

Standard and Poor’s Compustat1 and the Chicago Graduate School of Business’ Center for Research in Security Prices (CRSP) 2 databases provide independent sources of

historical accounting, operations, and business financial performance data Thus, this research study uses the most applicable techniques from both research streams while overcoming major obstacles that each face separately

The managerial relevance for removing ambiguities from causal connection between practice and performance are undeniable The prescriptive usefulness of operations management research depends on the resultant ability to advise a manager, “Do this, and

your results will improve.” Lean practice implementation that can be shown to lead to

financial results that operations managers, accountants, and financial officers all

appreciate, can pave the way for more companies to “become lean.” An implied

connection exists between the focus of lean on reducing non-value adding activities (i.e waste reduction) to “make value flow” (Womack & Jones, 1996) and the criteria for good

1 “Standard & Poor's Compustat, a division of McGraw-Hill, Inc, is the premier supplier of financial

information and produces a variety of databases and software products for institutional investors, financial, and corporate clients…Compustat is a database of financial, statistical, and marketing information

providing more than 300 annual and 100 quarterly Income Statement, Balance Sheet, Statement of Cash Flows, and supplemental data items on more than 24,000 publicly held companies.” Wharton Research Data Services; http://wrds.wharton.upenn.edu/home/index.shtml; Oct 2003

standard and derived security data available for the NYSE, AMEX and Nasdaq Stock Market CRSP is a research center at the University of Chicago Graduate School of Business and maintains historical data spanning from December 1925 to the present CRSP's trademark unique issue identifiers tracks a

continuous history of securities and provides a seamless time-series examination of the issue's history.” Wharton Research Data Services; http://wrds.wharton.upenn.edu/home/index.shtml; Oct 2003

investment and management practice held by value investors, such as Warren Buffet (Graham, 1949; Graham & Dodd, 1934; Vick, 2001) Buffet uses a company’s long-term ability to maintain a high, stable return on equity as a primary indicator of the company’s ability to generate value for its shareholders (Vick, 2001)

1.2 Research Problem

This study addresses the general question, “Do lean manufacturing management practices improve financial performance?” This question subsumes several preliminary questions concerning what types and implementation levels of practices constitute “lean practice” and financial performance As mentioned earlier, this study views lean as a synergistic set of mutually supportive and integrated management practices The research confirms and measures the relative composition of the lean practice set by identifying lean and non-lean archetypes within a well-defined sample frame of manufacturing companies Specifically, this research examines whether lean practices as a set, or as individual, specific lean practices as reflected in survey data are related to two levels of sustained financial performance (the operations and the business level) At the operations level, Compustat data is used to assess performance by measuring five-year median asset and employee productivity, gross profit margin, and total cycle time At the business level, return on equity (ROE) and sales growth are measured using Compustat data and stock return performance is assessed using CRSP data

The study tests several research propositions to systematically analyze the question of whether lean practices affect financial performance These propositions are structured by

articulating the problem as a need to understand the dyadic relationships between three ideas: lean practice, operations financial performance, and business financial

performance (Figure 1.1) The first to be examined is the relationship between lean practice and either operations financial performance (L-O) or business financial

performance (L-B) Lean practice is tested in two ways for each practice-performance relationship: both as a mutually supportive set of practices and as individual practices Next, the relationship between operations and business financial performance is tested (O-B) This scheme results in the following abbreviated research propositions:

Proposition 1: Lean archetypes tend to have better operations financial performance than non-lean archetypes

Proposition 2.1: The extent of lean practice implementation as measured in simple linear combinations is not associated with operations financial performance

Proposition 2.2: The implementation levels of all lean practices are positively associated with all measures of operations performance

Proposition 3: Lean archetypes tend to have better business financial performance than non-lean archetypes

Proposition 4.1: The extent of lean practice implementation as measured in simple linear combinations is not associated with business financial performance

Proposition 4.2: Implementation levels of all lean practices are positively associated with all measures of operations performance

Proposition 5: Categorization as a lean archetype based on operations financial

performance is positively associated with business financial performance

Research Frame

Lean Practices

BusinessFinancial ($)Performance

OperationsFinancial ($)Performance

“L-O”

“O-B”

“L-B”

ManufacturingCompanies

Figure 1.1: Research frame and relationship diagram

1.3 Research Method

The approach taken to study the practice-performance problem combines the best aspects

of two research streams From the operations management empirical research stream the study derives its survey methodology and well-defined, validated multi-item constructs to measure the extent of practice implementation in sample companies From accounting and finance research, it assumes a rigorous statistical approach to analysis and

measurement of financial performance Combining perceptual practice measurement

from surveys with archival financial data enables this research to make substantive claims with respect to its findings’ validity

The research method included first adopting a sample frame of manufacturing companies

in four broad industry categories To enhance the generalizability of the conclusions, the categories range from discrete product to process industries A strict selection criterion required that the companies report participation in only one four-digit standard industry classification Along with a size restriction of fewer than 9000 employees, this criterion served to ensure that survey responses were applicable to the performance of the firm as a whole A high-level company executive for each sample firm completed a survey

instrument with 36 questions (Appendix B) covering eight lean practices previously validated in the research literature The practice data were factor analyzed and used to identify lean and non-lean firms through cluster analysis

Archival financial data from 1998 to 2002 was used to measure long-term performance

A robust, relative measure of financial performance was developed for each metric

financial measure used in the study Measures were developed by identifying a

comparison portfolio of companies in the sample frame and calculating the difference between the median 5-year performance for the sample firm and that of its matched comparison portfolio The relationships between lean and non-lean firms were compared with respect to operations financial and business financial performance using non-

parametric statistics and logistic regression As accounting and financial data is prone to

implementation without making performance value judgments since financial

performance is measured independently from the practice survey The second advantage

is the availability of a wide range of longitudinal financial performance data through Compustat that allows for a comprehensive financial picture of each sample firm A broad set of financial ratios can be analyzed and divided into more specific measures to better understand underlying mechanisms The availability of a non-survey source of performance measures also allows the survey to be shorter and ostensibly reduces

respondent fatigue and increases the response rate from busy executives

The use of Compustat represents a significant opportunity for future research into the lean practice-performance relationship Longitudinal data is available on over 24,000 firms Specifically as regards the current sample companies in this research, follow-up studies can examine future performance based on annually updated archival data without the necessity of a future survey Implementation timing and sequence issues can be examined in a follow-up study and provide answers to questions such as, “What is the best practice set for sustainability of results?” In addition, the opportunity is presented to expand lean performance research into other manufacturing and service industries using the techniques developed in this study

1.4 Importance and Contribution of Research

Aside from establishing a more definitive relationship between lean practices and

archival financial performance, several lesser, but nonetheless unique, contributions of this study are notable from both a content and research methods perspective:

1 Previous empirical OM practice survey studies have not looked at business

financial performance in the form of return on equity, sales growth, and stock return This is the first study to make the connection from broadly measured lean practice constructs to these measures of business level financial performance

2 A contribution to methods in OM research is the use of Compustat’s standard industry classification code report to identify companies that participate in only one four-digit SIC code This approach provides an alternative to the plant-level data restriction imposed by previous OM practice studies Being able to make the claim that practice survey data applies to all the manufacturing operations of a company, the lean practice effects can then be traced through a company’s

financial records

3 The development of a normalized measure based on the median performance of comparison portfolios of companies is a major contribution to this study It successfully controlled for both industry and size effects that would have limited the ability to identify practice effects in a relatively small sample size This

technique in combination with the single SIC code identification feature in

Compustat provides a vehicle for more research into the company-level effects of

OM practices

4 This study utilizes cycle time as a key metric in evaluating lean performance It offers two aggregate measures of cycle time (total and cash-to-cash) that view cycle time beyond the bounds of inventory, the metric traditionally imposed in

OM research Accounts payable, accounts receivable, and inventory are

combined to provide a companywide, cross-functional view of cycle time

performance

5 Finally, this study uses an interesting mechanism to improve survey response rate Potential respondents were provided with a relative ranking of their position in the sample frame based on a preliminary analysis of the Compustat data Letters to the respondents informed them of their company’s ranks with respect to several individual and summated operations and business measures The respondents were encouraged to respond in order to help the researcher “understand how [their] manufacturing and operation practices contribute to [their] performance.”

An added incentive allowed respondents to view rankings for all potential

respondents in the sample frame by visiting the website and completing a survey This mechanism created a unique “curiosity hook” that may be helpful in

improving the response rate in future OM survey research Such incentives may also move some of the practitioner value derived from academic research to the

front of the process, thereby improving the partnership between researchers and businesses

1.5 Organization of the Dissertation

The remainder of the dissertation follows in six chapters Chapter 2 reviews recent OM and financial and accounting studies relevant to the practice-performance question Chapter 3 develops a series of research propositions based on the literature Chapter 4 describes the research methods used to test and analyze the positions Chapter 5

discusses the results and potential threats to their validity and generalizability Chapter 6 presents conclusions and offers directions for future research into the relationship

between lean manufacturing management practices and financial performance

CHAPTER 2

LITERATURE REVIEW

2.0 Objectives and Map of Domain

This study aims to capture and quantify the relationship between lean manufacturing management practices and financial performance at both the operations and business levels The literature review examines how past studies addressed the practice-

performance connection, and will show that:

1 The practice-performance connection has not yet been sufficiently established, especially regarding financial performance

2 Recent operations management (OM) studies have developed a reliable and valid set of constructs for measuring lean practices

3 Analysis methods used in financial and accounting literature, along with the practice constructs developed in the OM studies, offer a means for more

accurately describing and testing the practice-performance connection

The relevant domain for empirical research into the practice-performance relationship can

be classified into three general categories, which, broadly considered, comprise the three relevant quadrants of a two-by-two matrix (Figure 2.1) This study uses the matrix axes’

to specify lean practice usage (vertical axis) and the source of dependent performance

variable data (horizontal axis) Either studies are categorized as using empirical surveys

or some form of public announcement as a determinant of lean practice usage and are further categorized according to the performance variable’s data source (i.e survey-based manager perceptions or archival data sources) Other aspects of each study, such as unit and method of analysis, correlate with their corresponding matrix categories

The first category (survey-perceptual) includes cross-sectional, survey-based studies that describe, categorize, and measure multiple manufacturing management practices They include an analysis of the relationship between practices and between practice and

operations performance as measured by managers’ perceptions of performance This is typically measured relative to competitors’ performance or, internally, to the company’s own past performance or established goals Having evolved with the development of empirical research in OM over the last ten years, these studies use multi-item constructs that have demonstrated high validity and reliability in capturing and describing

management practices However, performance variables in survey-perceptual literature, which lack definitive connection to archival forms of accounting and financial data, comprise the category’s major shortcoming

The second literature category (announcement-archival) includes studies that use archival data to measure operations or business financial performance Typically, these studies use public announcement data in the form of press releases or annual reports to designate practice implementation dates or a survey that asks respondents to self-classify their company as either practice or non-practice This category includes accounting and

by many names and vary in definition and degree of implementation as well as

exclusivity Howton, Higgins, and Biggart (2000) alone use six different terms to

identify companies implementing JIT Claiming a practice “has been implemented” is not equivalent to measuring the extent of its practice usage Descriptive studies in the

OM literature (Category 1) testify to this lack of uniformity in mix and extent of practice implementation

The third, and final literature category (survey-archival), hybridizes the first two These studies used a survey instrument to collect practice information from subject companies along with archival sources of performance data to measure the practice-performance relationship These studies’ main deficiency results from the limited capability of their survey instruments to reliably capture a full range of lean practices In addition, none of the Category 3 studies examines the relationship of practices to business level

performance such as return on equity (ROE), earnings per share, or stock return Studies such as Upton (1998), which use perceived performance measures and announcement-

based indicators of practice usage, are less persuasive and are considered irrelevant to this literature review

Table 2.1 lists the literature review’s primary studies and the categories into which they fall The table uses four broad practice areas that are typically included in lean operations management, namely just-in-time (JIT), total quality management (TQM), total

productive maintenance (TPM), and infrastructure or common practices Practices

described in the articles cited are placed in appropriate corresponding lean practice areas For example, setup time reduction is classified as a JIT practice, and customer focus or involvement is classified as being primarily TQM The practice areas are not necessarily exclusive A major contention in the literature is that each lean practice, being

synergistic and mutually supportive, is difficult to isolate in its resulting impact on

performance (Shah, 2002; Ward, Bickford, & Leong, 1996) Flynn, Sakakibara, and Schroeder (1995) used the concept of “infrastructure” or “common” practices for both JIT and TQM to circumvent the problem of practice classification while recognizing the interdependencies of lean practices Table 2.1 depicts the high coverage of lean practice topics in survey-perceptual studies that do not use corresponding archival data to analyze financial performance Announcement-archival studies, measuring financial operations and business performance with archival data, prove deficient in the limited, binomial nature of the practice usage designation To explore the breadth of studies available, Table 2.1 purposefully uses minimal criteria for practice coverage The studies by

Fullerton and McWatters (2001) and Fullerton, McWatters, and Fawson (2003) ask only ten survey questions to capture the extent of implementation across all lean practice

areas In contrast, Cua, McKone, and Schroeder (2001) use 17 factors derived from a survey containing 69 questions Table 2.1 further indicates whether a study’s analysis set controls for the firm’s industry and size, both of which may significantly influence actual performance levels

Table 2.2 summarizes other relevant features of studies in this literature review including unit of analysis, sample size, industry coverage, primary analysis method, survey type, and time span of the data analysis The survey type column differentiates studies that examine performance over time (longitudinal) from cross-sectional studies The use of longitudinal studies coincides with availability of archival data and is restricted to

Category 2 and 3 studies The following sections describe each literature category in turn and focus on areas where this research study leverages or improves upon past research

Perceptual Archival Category 1: Survey-Perceptual Category 3: Survey-Archival

Shah & Ward (2003) Shah (2002) Cua, et al (2001)

Koufteros et al (1998) Sakakkibara, et al (1997) Flynn, et al (1995) Inman & Mehra (1993)

Category 2: Announcement-Archival

Biggart & Gareya (2002) Boyd, et al (2002) Kinney & Wempe (2002)

Easton & Jarrell (1998) Balakrishnan, et al (1996) Hendricks & Singhal (1996) Chang & Lee (1995) Hudson & Nanda (1995) Billesbach & Hayen (1994)

Source of Performance (dependent) Variables

Operations Performance

Business

struc- ture

Notes: Cells with X designate that the column topic is addressed at least to a minimal degree.

Category 3: Studies with limited practice scope and archival measures of operations and business financial performance Category 2: Studies with public or self-identified nominal practice usage and archival measures of financial performance

Archival Performance Data Controls

Category 1: Studies with multiple lean practices and non-archival measures of performance

Lean Practice Coverage Areas

Table 2.1: Practice and performance coverage of key studies

Unit of analysis

Sample size Industry coverage Primary analysis method

Survey

type (1) Time span (2)

Category 1: Studies with multiple lean practices and non-archival measures of performance

Shah 2002 Plant 271 Mfg SIC's 34, 35, 36, 37, 38 Structural equation modeling C 0

Cua, et al 2001 Plant 163 Electronics, machinery, transportation parts Dircriminant analysis C 0

Claycomb et al 1999 Firm 200 All mfg Structural equation modeling C 3 years Samson & Terziovski 1999 Mfg site 1024 All mfg Multiple regression C 0

Koufteros et al 1998 Plant 244 Mfg SIC's 34, 35, 36, 37 Structural equation modeling C 0

Sakakkibara, et al 1997 Plant 41 Electronics, machinery, transportation parts Canonical correlation C 0

Flynn, et al 1995 Plant 42 Electronics, machinery, transportation parts Hierarchical regression C 0

Category 2: Studies with public or self-identified nominal practice usage and archival measures of financial performance

Kinney & Wempe 2002 Firm 201 Mfg, wholesale, retail Wilcoxon ranked sign test L -3,-2, -1 vs 1,2,3 Howton, et al 2000 Firm 97 All mfg Event study - t-test L -200 to -11 vs 0 days Easton & Jarrell 1998 Firm 108 Mfg and service Wilcoxon ranked sum test C 5 years Balakrishnan, et al 1996 Firm 46 All mfg ANOVA L -3,-2, -1 vs 1,2,3 Hendricks & Singhal 1996 Firm 91 All industries Event study - Wilcoxon L -200 to -11 vs 0 days

Hudson & Nanda 1995 Firm 55 SIC's 22 - 59 Path model L -4 to +4 Billesbach & Hayen 1994 Firm 28 All mfg Wilcoxon ranked sign test L -5,-6 vs 5,6

Category 3: Studies with limited practice scope and archival measures of operations and business financial performance

Fullerton, et al 2003 Firm 212 All mfg Hierarchical regression C 0

Callen, et al 2000 Plant 100 Automotive parts and electronics Multiple regression C 0

Note: 1 C = cross-sectional, L = longitudinal

2 Stated in years unless otherwise specified Zero or aggregate time period reported for cross-sectional studies Longitudinal studies report comparison time periods for adoption period equal to zero.

Table 2.2: Other relevant features of practice performance studies

2.1: Survey-Perceptual (Category 1) Studies

2.1.0 Description and objectives

Survey-perceptual (Category 1) studies include those that use multiple lean practices and perceptual measures of performance Many studies seek to test the empirical relationship between manufacturing management practices and operations performance In the last ten years, empirical research into lean manufacturing has grown appreciably in its ability

to effectively describe and measure lean practices Studies by Davy, White, Merritt, and Gritzmacher (1992), Flynn et al (1995); and Flynn, Schroeder, and Sakakibara (1994) began building and testing multi-item survey constructs for lean practices While it is clear that OM studies continue to evolve, they nonetheless show a degree of convergence around a set of interrelated practices that operate under the heading of “lean.” Over the last several years consensus has grown over what constitutes lean practices, as codified in the Toyota Production System (Monden, 1983; Ono, 1988) and popularized by

researchers such as Womack, Jones, and Roos (1990) and Schonberger (1986) Lean practices can be thought of as a broad set of mutually supportive practices that fall into one of four broad areas: JIT, TQM, TPM, and infrastructure Table 2.3 provides a list of lean practices organized into the areas under which they primarily fall, and indicate the specific studies cited in this literature review that highlight these practices The studies cited here are relatively recent, reflecting the convergence in practice inclusion and measurement, cover a broad range of practices simultaneously, and relate practice to operations performance A truly lean company uses most or all of the practices to some degree (Callen, Fader, & Krinsky, 2000) Additionally, certain “infrastructure” practices

such as information feedback, management support, employee involvement, and strategic planning are thought to contribute significantly to the success of a lean implementation

and may even be considered prerequisite practices (Cua et al., 2001; Flynn et al., 1995;

Sakakibara, Flynn, Schroeder, & Morris, 1997) This section highlights the

survey-archival studies’ strength in describing the extent of lean practice usage and weakness in

capturing performance

Ca

ory 1 Studie s

Sh Cua, et alFuller

ton &

McWatters Claycomb,

et al

Samson &

T ziovski

Koufteros e

t al

Sakakkibara , et a l Flynn, et

alCateg

ory 3 Studies

Fullerton &

McWatte rs

Callen, et al Mac Duffi e

Freq 2002 2001 2001 1999 1999 1998 1997 1995 2003 2000 1995

Internally oriented lean practices

Just in time production methods

JIT scheduling, level schedules daily schedule adherence, stable

cycle schedules, uniform work load, market-paced final

assembly, JIT sales, schedule flexibility 9 C* C* C* C* C C* C* C* C*

Setup time reduction, reengineering setups, setup reduction

Employee involvement in problem solving

Information, analysis, and feedback, problem solving groups,

quality circles, work teams, shop floor problem solving, employee

General, JIT, or cross-functional training Flexibility of workers'

skill Multi-function employees Job rotation. 6 C* C* C* C* C* C*

Other items associated with lean

Cross-functional, integrated product design 4 C* C* X C*

Committed leadership, management support 3 C* X* X*

Strategic planning, manufacturing strategy 3 C* X C*

Notes:

Practices divided into major categories and appended with wording used in the studies.

C* = Practice found to be significantly (<0.05) related to performance in combination with the set or a subset of other practices.

-C* = Practice found to be significantly related to performance in combination with the set or a subset of other practices, but in unexpected direction.

C = Practice tested, but not found to be significantly related to performance in combination with the set or a subset of other practices.

X* = Practice found to be significantly (<0.05) related to performance.

X = Practice tested, but not found to be significantly related to performance.

SPC tools to monitor quality - product and process quality

improvement, quality and process management, quality improvement

efforts

Group technology to enhance the flow of products - cellular

manufacturing, equipment layout, focused factory

Communication with suppliers - dependable suppliers, integrated

supplier networks, supplier quality management, supplier relations

JIT delivery by suppliers - JIT purchasing, JIT supplier relationship,

%JIT purchasing

Customer involvement - focus

Total productive maintenance - autonomous, planned, preventative

Table 2.3: Lean practices – frequency and significance in recent OM literature

2.1.1 Lean Practice Measurement

A lack of verifiable, archival measures of performance remains the main deficiency of the survey-perceptual studies Category 1 studies have a common methods bias in that survey respondents generally are asked to report on both the extent of practice

implementation and the improvement or relative competitive performance that results from those practices3 Table 2.3 suggests that these studies indicate a significant level of perceived performance improvement from lean practices By momentarily suspending concern about common methods bias, a strong case emerges for predictive validity for the constructs used to measure practice implementation in these studies The content

coverage seems quite complete since most studies cover multiple practices Eight major constructs for lean practices—five internally oriented and three externally oriented—stand out in recent studies (Table 2.4)

These eight prominent practices correspond to the seven found relevant in Shah’s 2002 study of lean configuration along with the additional practice of total production

maintenance The breakdown into external and internal practices and the wording that describes the individual practice constructs is borrowed from Shah (2002) This set of practices corresponds to the more comprehensive set of practices used by Cua et al (2001) These eight lean practices comprise the set of lean practices that this study’s survey instrument aims to extract Choosing these eight practices to measure the extent

3 See Flynn, et al (1995) and Sakakibara, et al (1997) for notable exceptions where multiple respondents are used to cross validate responses and minimize the effects of common method bias