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Improving Performance through an Integrated Manufacturing Program KRISTY O. CUA (DECEASED) KATHLEEN E. McKONE-SWEET, BABSON COLLEGE ROGER G. SCHROEDER, UNIVERSITY OF MINNESOTA © 2006, ASQ

Every five to 10 years, a new manufacturing management program is introduced as the panacea for poor performance, although these programs have had mixed success. Many academics and practitioners believe that failures are due to the partial implementation of the programs and incompatible systems within the plant. Yet past research primarily considers manufacturing programs in isolation. In this article, the authors present a framework for integrating manufacturing programs, using the well-developed programs of total quality management (TQM), just-in-time (JIT), and total productive maintenance (TPM). These three programs are often considered components of the popular Lean manufacturing. The authors discuss the theoretical foundation for the positive impact of an integrated manufacturing program on manufacturing performance. They explore the theoretical relationships using structural equation modeling on a sample of 163 manufacturing plants. The authors’ analysis provides evidence of the need for integrating the manufacturing practices. In their framework, together the practices of TQM, JIT, and TPM exhibit a consistent positive effect on multiple dimensions of manufacturing performance. The findings demonstrate the importance of implementing manufacturing practices that belong to all three programs and of integrating new programs with existing practices. Key words: just-in-time, Lean manufacturing, manufacturing performance, total productive maintenance, total quality management

INTRODUCTION The global marketplace has led many companies to implement new manufacturing programs and organizational structures to enhance their competitive position. Among the many manufacturing programs, total quality management (TQM), just-in-time (JIT), and total productive maintenance (TPM) are often referred to as components of “world-class manufacturing” (Giffi, Roth, and Seal 1990; Schonberger 1986; Steinbacher and Steinbacher 1993; Schonberger 1996). Though there may be some differing notions of what constitutes world-class manufacturing, the cited authors and others recognize that continuous improvement to sustain competitive advantage and profitability is dependent upon the synthesis of several reinforcing world-class manufacturing programs. Lean manufacturing and Six Sigma, more recent programs, encompass aspects of these world-class manufacturing programs. While there are many success stories of TQM (Hendricks and Singhal 1997; Ward 1998; York and Miree 2004), JIT (Flynn, Sakakibara, and Schroeder 1995; Voss and Robinson 1987; Fullerton, McWattrsm, and Fawson 2003), and TPM (Maggard 1992; Robinson and Ginder 1995; Chan et al. 2005), there are also documented cases of their failure (Choi and Behling 1997; Safayeni et al. 1991; Giffi, Roth, and Seal 1990). The mixed success and failure of TQM, JIT, and TPM programs calls for a study to identify the specific practices that can lead to successful implementation. While there is evidence that suggests that their joint implementation provides a better chance for success

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Improving Performance through an Integrated Manufacturing Program (Garwood 1990; Vuppalapati, Figure 1 Framework of the effect of integrated manufacturing practices. Ahire, and Gupta 1995), there is limited research that provides Common practices a theoretical and systematic • Committed leadership investigation of this. Therefore, • Strategic planning this study aims to present a • Cross training theory that can explain the • Employee involvement successful joint implementa• Information and feedback tion of TQM, JIT, and TPM. The TQM techniques results will provide guidance • Process management for the implementation of • Cross-functional design Lean, Six Sigma, and future Manufacturing • Supplier management manufacturing improvement performance • Customer involvement Integrated programs. • Cost manufacturing In this article, the authors JIT techniques • Quality program present their framework for • Set-up reduction • Delivery integrating TQM, TPM, and JIT • Pull production • Flexibility and discuss the theoretical • JIT delivery foundation for the positive • Equipment layout impact of an integrated manu• Schedule adherence facturing program (IMP) on TPM techniques manufacturing performance. • Autonomous and planned Then they discuss the data used maintenance for their analysis and their • Technology emphasis method of analysis. Finally, • Proprietary equipment they explore the proposed reladevelopment tionships and discuss their results as they apply to the manufacturing environment. Specifically, they examworld-class manufacturing programs included in the ine TQM, JIT, and TPM within a single theoretical study as well as to future manufacturing initiatives. framework. Thus, they consider a set of IMPs that is a synthesis of the core practices of TQM, JIT, and TPM. Since these programs represent broad concepts and there is no consensus on a single definition for them, There are three important aspects of the authors’ IMP the authors use a literature review to classify the framework (see Figure 1): 1) the techniques and techniques and practices of TQM, JIT, and TPM. practices of TQM, JIT, and TPM; 2) the integration or The authors find that these integrated practices can fit of the programs; and 3) the effect of these intebe classified into two main components, namely the basic grated programs. In this section, the authors discuss techniques and the common practices. TQM, JIT, and each component of the framework and develop their TPM consist of fundamental practices that are unique to propositions. each program. These practices are generally process or technically oriented and are considered to be the basic techniques of TQM, JIT, and TPM. The three programs also have strategic- and human resource-oriented The authors seek to examine the relationships of propractices that support the implementation of their grams that are concurrently implemented within a

FRAMEWORK DEFINITION

The Techniques and Practices 46 QMJ VOL. 13, NO. 3/© 2006, ASQ

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Improving Performance through an Integrated Manufacturing Program Table 1 Commonly suggested TQM, JIT, and TQM practices or techniques. Framework Framework practice component or technique

TQM literature 1

2

3

4

5

JIT literature 6

7

TQM

Cross-functional product design

x

x

x

x

x

TQM

Process management

x

x

x

x

x

TQM

Supplier quality management

x

x

x

x

x

TQM

Customer involvement

x

x

x

x

JIT

Set-up time reduction

x

JIT

Pull system production

JIT

8

9 10 11 12 13 14 15 16 17 18 19 x

x

x

x

x

x

x

x

x x

x

x

x

x

x

x

x

x

x

x

x

x

JIT delivery by suppliers

x

x

x

x

x

JIT

Equipment layout

x

x

x

x

x

JIT

Daily schedule adherence

x

x

x

x

TPM

Autonomous and planned maintenance

x

x

x

x

x

TPM

Technology emphasis

x

x

x

x

x

Common

Committed leadership

x

Common

Strategic planning

x

x

Common

Cross-functional training

x

x

x

x

x

x

x

x

Common

Employee involvement

x

x

x

x

x

x

x

x

Common

Information and feedback

x

x

x

x

x

x

References:

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

x

x

x

Saraph, Benson, and Schroeder 1989 and Benson et al. NOT ON REF. LIST 1991 Flynn, Schroeder, and Sakakibara 1994 Powell 1995 Ahire, Golhar, and Waller 1996 Black and Porter 1996 Samson and Terziovski 1999 Mehra and Inman 1992 Davy et al. 1992 Sakakibara, Flynn, and Schroeder 1993

basic techniques and are common to the three programs. The authors, therefore, consider these common practices that reflect plant management strategic and human resource related initiatives as the common strategic- and human resource-oriented practices and, for brevity, they also refer to these as common practices. A summary of commonly mentioned TQM, JIT, and TQM practices or techniques from the literature is provided in Table 1.

x

TPM literature

x x

x

x

x

x

x

x

x

x

x x

x

x

11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

x

x

x

x x x

x

x

x

x

x

x

McLachlin 1997 Sakakibara et al. 1997 Ahmad 1998 Nakajima 1988 Takahashi and Osada 1990 Tsuchiya 1992 Steinbacher and Steinbacher 1993 McKone and Weiss 1999 McKone, Schroeder, and Cua 1999 Maier, Milling, and Hasenpusch 1998

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Cua

Based on the literature review summarized in Table 1, the practices common to all three programs include committed leadership, strategic planning, crossfunctional training, and employee involvement. These practices deal with the human and strategic aspects (or social aspects) of the programs and provide the foundation for many of the manufacturing practices. The use of information and feedback is explicitly cited in the literature as part of TQM and TPM programs,

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Improving Performance through an Integrated Manufacturing Program but not of JIT. The availability of information and feedback, however, is certainly important in a JIT production environment when each station in a chain of manufacturing processes is tightly linked with its previous and subsequent stations to determine production lot sizes and schedule. In addition, the practice or use of information and feedback is important to both communicating and facilitating strategic direction as well as to interacting with the human resources within an organization. Therefore, the authors consider the use of information and feedback a fifth practice that is common to TQM, JIT, and TPM. The unique practices from their literature review are classified as follows: TQM basic techniques are cross-functional product design, process management, supplier quality management, and customer involvement. JIT basic techniques are set-up time reduction, pull system production, JIT delivery by supplier, functional equipment layout, and daily schedule adherence. TPM basic techniques are autonomous and planned maintenance and technology emphasis. The authors also consider the use of proprietary equipment as a component of TPM, since it is seen as important to gaining competitive advantage. This is consistent with Hayes and Wheelwright’s (1984) characterization of firms that pursue a manufacturingbased competitive advantage, which include, among others, the anticipation of the potential of advanced technologies and the development of proprietary equipment. The authors provide a brief discussion of these practices here. Additional details of these practices and their assignment to the specific programs can be found in Cua, McKone, and Schroeder (2001) and Cua (2000). Further description of these practices and techniques will also be provided later in this article.

THE INTEGRATION While there are many practices in manufacturing management (Skinner 1996), the authors have chosen to investigate and relate TQM, JIT, and TPM for the following reasons: 1. They consist of a comprehensive set of practices and techniques involving both the social and technical

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or process-oriented aspects of manufacturing and emphasize continuous improvement (Schonberger 1986; Evans and Lindsay 1999). The authors’ framework shows that TQM, JIT, and TPM include unique basic techniques and strategic- and human resource-oriented practices that are common to the three programs. 2. TQM, JIT, and TPM are recognized world-class manufacturing programs (Schonberger 1986; 1996). Successful implementation of these programs is found to improve manufacturing performance and help companies gain a competitive edge (Inman and Mehra 1990; Hendricks and Singhal 1997; McKone and Weiss 1999). 3. There is empirical evidence that these programs are compatible. Flynn, Sakakibara, and Schroeder (1995) find that a set of common infrastructure practices form a strong foundation for the achievement of both JIT and TQM performance goals. They also demonstrate that TQM and JIT practices interact. In a study of the contextual factors that are related to TPM implementation, McKone, Schroeder, and Cua (1999) find that managerial contextual factors, such as the implementation level of TQM, JIT, and employee involvement, better explain the implementation level of TPM than environmental and organizational contextual factors. Most empirical studies of TQM, JIT, and TPM examine these programs in isolation; however, conceptual articles argue for the value of implementing complementary manufacturing practices. The systems framework supports the use of an integrated approach in examining the interrelated building blocks of a system (Gerwin 1976; Van de Ven and Ferry 1980), since the effect of a system is derived from the aggregate impact of its parts and not from the actions of its parts taken separately (Finnie 1997). Thus, to understand the combined effects of the interrelated manufacturing practices of TQM, JIT, and TPM, the common practices and basic techniques should be examined within a single framework. Given the theoretical evidence of covariation and the complementary nature of the practices of TQM, JIT, and TPM, the authors propose that the common

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Improving Performance through an Integrated Manufacturing Program practices and basic techniques of TQM, JIT, and TPM form an IMP. • Proposition 1: The common (strategic and human) practices and basic techniques of TQM, JIT, and TPM are dimensions of an IMP.

THE EFFECT OF INTEGRATED MANUFACTURING PROGRAMS TQM, JIT, and TPM programs have the common objective of making a production system more efficient and effective through continuous improvement and elimination of waste and, therefore, should improve manufacturing performance when implemented together. The different emphases of TQM, JIT, and TPM on waste reduction and elimination are complementary and together the practices and techniques should help reduce nonvalue-added activities and process variability. Therefore, it can be expected that successful implementation of these practices and techniques will be associated with good performance. At the same time, while the implementation of the basic techniques of TQM, JIT, and TPM may be directly related to process improvement, their implementation requires supporting mechanisms. A piecemeal approach to the implementation of TQM, JIT, and TPM can lead to failure. The institution of common strategic- and human resource-oriented practices is needed for the successful implementation of the basic techniques. Moreover, the common practices enable the development of one of the most important resources, human capital, which is the impetus for sustaining flexibility, continuous learning, and improvement. Sociotechnical systems theory (Davis 1990; Cherns 1990) provides a theoretical foundation for exploring the impact of an integrated program on performance. According to sociotechnical systems theory the joint optimization of manufacturing practices that are socially and technically oriented should lead to good performance (Emery 1990). For example, Rehder (1989) argues for the importance of building manufacturing competitiveness upon the integration and coordination of strategy, structure, culture, and

human resource subsystems within a complex, changing environment. He shows that the concept of a balanced sociotechnical system is reflected in all subsystems of successful Japanese transplants. The authors have already identified the manufacturing practices as forming the common practices that are human- and strategic-oriented and the basic techniques that are unique to each of the three programs (see Figure 1). They, therefore, expect that high-performing manufacturing plants will have a high level of implementation of both the social (common human- and strategic-oriented practices) and the technical (the basic techniques) of both the social and technical systems. Thus, according to sociotechnical theory, the authors propose that the integration of the manufacturing practices and techniques (the IMP) is positively associated with the level of manufacturing performance. • Proposition 2: Integration of the common practices and basic techniques of TQM, JIT, and TPM is positively associated with the basic dimensions of manufacturing performance — cost efficiency, quality, delivery, and flexibility.

METHOD OF ANALYSIS Since the authors are interested in the multivariate relation among a group of practices believed to fit together, it is appropriate to adopt a holistic perspective for modeling the integration of the manufacturing practices and techniques (Venkatraman and Prescott 1990). Modeling fit as covariation is one approach for understanding holistic fit. The objective of this approach is to determine a pattern of covariation or internal consistency among a set of underlying theoretically related variables (Venkatraman 1989). This approach is useful for examining fit or integration of several concurrent dimensions of a factor that are deemed insufficient in describing a system when taken separately. When fit or integration is modeled as covariation, the recommended method of analysis is exploratory or confirmatory factor analysis (Venkatraman and Grant 1986). To explicitly model the structure of a

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Improving Performance through an Integrated Manufacturing Program Figure 2 Models of effect of practices on performance. Common Practices

(A) Integration Model

1

e

1

e

(B) Direct Model

e

TQM Techniques 1

Common Practices

Integration of Practices

JIT Techniques 1

e

e

Performance 1

e

1 Perf_Meas

TPM Techniques 1

e

second-order factor signifying the integration of firstorder factors, the authors take a factor analysis approach using structural equation modeling. They also extend the second-order factor model to multiple structural models relating the integration of practices to the four basic manufacturing performance measures: cost, quality, delivery, and flexibility. The integration model (see Figure 2a) is compared with a direct model (see Figure 2b) where there is a direct relationship between common practices and basic techniques with performance. To provide a comprehensive model assessment the authors use different types of fit indices—absolute, relative, and parsimonious. They choose indices that are more appropriate for small samples. Table 2 lists the seven indices that they use and the rule-of-thumb fit criteria. The authors also examine the residuals to alleviate some of the problems associated with using fit indices. When the residuals are small, the model is clearly good no matter what the chi-square test or fit indices seem to imply (Hu and Bentler 1995). The authors, therefore, assess model fit by using a combination of the indices discussed previously and by examining residuals. They also make inferences from the model on the basis of significant path coefficients and determine whether they are consistent with theoretical predictions.

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1

TQM Techniques

1

Performance JIT Techniques

e

1

e 1

1

e

1 Perf_Meas

TPM Techniques

1

e

e

DATABASE AND MEASUREMENT MODEL The data used for empirical examination of the propositions were collected as part of an ongoing world-class manufacturing (WCM) study being conducted by a team of researchers at several universities throughout the world. The first round, which was collected in 1994, consisted of 43 plants in the United States (Flynn, Schroeder, and Sakakibara 1994; 1995). The second round of data collection, which was used for this study, was conducted in 1997 and completed in 1998. By the late 1990s, most manufacturing plants had adopted some components of TQM, JIT, and TPM programs and had achieved benefits, or experienced failures, from the programs. The second round of the WCM data, therefore, enables one to evaluate well-established programs and analyze various levels of integration among the programs. The WCM database provides valid data to test one’s framework and propositions. Even though the data from the authors’ study were collected from 1997 to 1998, they believe that the relationships between constructs have not changed. While plants today may have achieved different levels of attainment on the various practices, the relationships among practices should be stable over time.

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Improving Performance through an Integrated Manufacturing Program Table 2 Fit indices. Index type

Fit index

Fit criteria

Source

Absolute

Root mean square residual (RMSR)

<0.05

Bryne 1998

Relative

Comparative fit index (CFI) Incremental fit index (IFI)

>0.90 >0.90

Bentler 1990 Hull, Lehn, and Tedlie 1991

Parsimonious

Parsimonious normed fit index (PNFI)

Delta greater than 0.06 indicates significant model differences. Indices in range of 0.50 not unexpected with good fit.

Mulaik et al. 1989 Williams and Holohan 1994

Bozdogan’s Consistent Akaike Information Criterion (CAIC)

Smaller value is better fit

Maruyama 1997

Target coefficient (T)

>0.90

Marsh and Hocevar 1985

Normed chi-square

<3.0 good fit <5.0 adequate fit

Carmines and McIver 1981 Joreskog 1970; Wheaton et al. 1977; Marsh and Hocevar 1985

plants in the database. This relatively high response rate was assured by communicating with the plants personally and by promising that they would receive a plant profile for comparison with other plants. Table 3 provides the distribution of the 163 plants used in this study according to country and industry. It would be interesting to consider country and industry as contextual variables. Because of a limited sample size, however, the authors do not explore differences between countries and industries. They instead control for possible differences in scale scores and performance measures by standardizing by country and industry. This allows them to have a sufficient dataset to conduct structural equation modeling.

Table 3 Number of plants in the database classified by country and industry. Number of plants Industry Country

Total

Electronics

Machinery

Auto suppliers

Germany

9

11

13

33

Italy

11

13

10

34

Japan

17

14

15

46

United Kingdom

7

6

7

20

United States

10

10

10

30

Total

54

54

55

163

The WCM database consists of data from manufacturing plants located in five countries (Germany, Italy, Japan, the United Kingdom, and the United States) and in three industries (automotive suppliers, electronics, and machinery). A stratified design was used to select an approximately equal number of plants in each country and industry combination. Two-thirds of the plants contacted joined the study, resulting in usable responses from 163 manufacturing

Integrated Practices The items in the WCM database that can be used to measure the 17 integrated manufacturing practices form multi-item scales and were answered by indicating the extent to which an informant agrees or disagrees with the statement provided using a five-point Likert scale: strongly agree (5), agree (4), neutral (3), disagree (2), and strongly disagree (1). Items that are reverse worded are reverse scored to maintain the same measurement format. The items for the scales were carefully developed from a thorough review of relevant

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Improving Performance through an Integrated Manufacturing Program literature and were subjected to rigorous pretest; hence, the authors can be assured of an acceptable degree of content validity. See the Appendix for details of the items used to measure the specific manufacturing practices and techniques and Cua (2000) for a comprehensive description of the survey development. To further ensure that the items of a scale are internally consistent across informants the authors conducted item analysis for each scale and modified the scale when necessary. They then assessed the psychometric properties of the resulting manufacturing practice measures using a confirmatory factor analysis approach (Cua 2000). Unidimensionality, convergent, and discriminant validity were assessed by evaluating the factor loadings, overall model fit, and correlation between factors. All models examined have satisfactory fit, factor loadings are significant and greater than 0.45, and there is no evidence of cross-loading of an item on factors that it is not intended to measure. All pairwise correlations between common practices, TQM techniques, JIT techniques, and TPM techniques are significantly different from 1 and therefore satisfy tests of discriminant validity. Construct reliabilities, as assessed using Fornell and Larcker’s (1981) measure for reliability, are greater than 0.7. The constructs can therefore be measured with an acceptable degree of reliability and validity. The measurement items for the integrated practices are shown in the Appendix.

Performance Measures The authors seek to relate the implementation of the IMP to the performance of a manufacturing plant. While there are many performance measures, it is important to recognize that some “order-winning criteria” are not within the responsibility of manufacturing (Hill 1985). The authors, therefore, use manufacturing performance to refer to performance outcomes that are relevant at the plant level of an organization and are within manufacturing’s jurisdiction. The most common approach in the literature is to use cost, quality, delivery, and flexibility as the four

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basic dimensions of manufacturing performance, which can be traced to the work of Skinner (1969). The authors consider the more common performance outcomes within the dimensions of cost, quality, delivery, and flexibility that are the primary responsibility of manufacturing, and these include cost efficiency (low unit cost), quality (conformance quality), delivery (on-time delivery), and flexibility (volume flexibility). For the performance measures, the plant manager was asked to evaluate plant performance relative to its competitors. The manufacturing performance measures are shown in the Appendix. For all of the authors’ analyses, they will consider the impact of an IMP on four measures—cost, quality, delivery, and flexibility.

RESULTS AND DISCUSSION The focus of this section is the empirical analysis using structural equation modeling. The authors evaluate the fit of IMPs and the effect of these IMPs on performance using the world-class manufacturing database. Ideally, they would have tested the fit of IMPs with a data set that was distinct from that which developed and tested their measurement model. The sample size, however, limited their ability to do so. Therefore, the results are exploratory and provide an initial explanation of the nature of integrated practices and its impact on performance.

THE INTEGRATION The authors’ literature review supports the close interrelation among the IMPs. They propose that the common (strategic and human) practices and basic techniques of TQM, JIT, and TPM are dimensions of an IMP. To empirically test the fit or integration of these practices and techniques within a single framework, the authors use a second-order factor model to represent the integration of manufacturing practices associated with TQM, JIT, and TPM. The second-order factor model has a satisfactory fit (NChisq=2.19, CFI=0.89, IFI=0.89, PNFI=0.69). The loadings of the first-order factors on the second-order factor are high

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Improving Performance through an Integrated Manufacturing Program Figure 3 Integration’s effect on cost efficiency. .51

e

.26

e

Strat_Pln X_Train .61

e

.71 .51

.45

e

Model Fit Statistics

Comm_Lead

.67 .78 .71

Emp_Involv

Chisq = 281.42 Df = 131 NChisq = 2.15 RMR = .06 CFI = .88 IFI = .88 CAIC = 525.17 PNFI = .68 Standardized Residuals < 2.00

.99 Common Practices e

.50

e

Info_Feed .63

e

Proc_Mgmt

e

X_Design

e

Supp_Mgmt

.55

.74 .41 .39

e

1.00 .80

.64 .62

.94 TQM Techniques e

.13

Cust_Inv Integration of Practices

.56

e

Pull_Prod JIT_Delv .47

e

Cost Efficiency

.73 .68 .71

Equip_Lay

e

.75 .52

.54

e

.36

Setup_Red .27

e

.97

1.00 .56

Sked_Adh

e

Maintain

1.00

Cost_Eff

JIT Techniques

e

e .89

.50

e

.75

.51

.72 .48

e

Tech_Emp

e

Prop_Eqp

.24

.69 .49

.79 TPM Techniques e

and significant at the 0.01 level, indicating that there is a common underlying thread among the practices. Examination of the modification indices in the authors’ original second-order factor model does not reveal problems of cross-loading of practices on factors that they are not conceptually identified to represent. Furthermore, their model fits better than a single

All parameter estimates are standardized and are significant at the 0.01 level.

first-order factor model because their model’s consistent akaike information criterion (CAIC) and standardized residuals are smaller and its parsimonious normed fit index (PNFI) value is significantly larger by 0.06. There is statistical evidence that the IMPs can be modeled as having several factors, but at the same time these factors co-vary and form a single higher

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Improving Performance through an Integrated Manufacturing Program order factor. Therefore, this finding is consistent with the dimensions of the IMPs coaligning to form a single factor. These results strengthen the rationale for using this framework of IMPs in classifying the practices into the four dimensions of common strategic- and human resource-oriented practices and the TQM, JIT, and TPM techniques. The practices of TQM, JIT, and TPM together form an integrated approach to implementation.

EFFECT OF INTEGRATED MANUFACTURING PRACTICES Plant managers are interested in the implementation of manufacturing best practices because they are often prescribed as a panacea for improving performance. The authors, however, believe that the practices being implemented should be compatible and directed toward consistent improvement goals. They propose that the integration of the common practices and basic techniques of TQM, JIT, and TPM is positively associated with the basic dimensions of manufacturing performance—cost efficiency, quality, delivery, and flexibility. The authors find that the combined higher level of implementation of manufacturing practices is positively associated with manufacturing performance. They show the model where cost efficiency is the performance measure in Figure 3 and the results for the other performance variables in Table 4. The link between integration of practices and cost efficiency is positive and significant at the 0.01 level with a path coefficient of 0.36. When the performance factor of cost efficiency is replaced by the other three measures of performance, similar significant positive relations hold between the integration of practices and performance factors. The path coefficients between integration of practices and performance are 0.37, 0.35, and 0.31 for performance measured by conformance quality, on-time delivery, and volume flexibility, respectively (see Table 4). To further test their model, the integration model was compared to a model that directly relates the

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first-order factors of practices to performance (for example, Venkatraman 1990; Segars, Grover, and Teng 1998). The direct model (as in Figure 2b) does not fit as well as the integration model. Thus, the authors accept the integration model for its congruence with their conceptual formulation of the relationship between practices and performance. When the common practices and basic techniques are combined as the integration of practices, their joint effect on performance is positive and significant. This is in line with Milgrom and Roberts’ (1995) notion of complementarity, that doing more of one thing increases the return of doing more of another. Thus, the authors believe that the integration model is a better representation of the relationship between practices and performance, and find supporting evidence that an IMP leads to improved performance.

CONCLUSIONS Past studies either examine manufacturing programs in isolation or interrelate them at an aggregate level of analysis. In this study the authors provide a careful and systematic development and analysis of a single framework for understanding the interrelated worldclass manufacturing practices of TQM, JIT, and TPM through literature review and empirical large-sample data analysis. Investigation of TQM, JIT, and TPM simultaneously and at the practice level enables a more detailed examination while disentangling the confusion on what constitutes the practices of these programs. This study provides conceptual and empirical evidence on the coalignment of IMPs, encouraging managers to plan and implement manufacturing practices with a systemic view of the production environment. Manufacturing programs should not be implemented in piecemeal fashion. The results of this study highlight the importance of coordinating both the social and technical aspects of operations. The institution of common practices can facilitate the implementation of basic techniques and alleviate the problems often encountered with the adoption of new manufacturing practices.

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Improving Performance through an Integrated Manufacturing Program Table 4 Results of integration effects on performance models. Path coefficients

Cost efficiency

Conform quality

On-time delivery

Volume flexibility

COMMON → Comm_Lead

0.71

0.72

0.72

0.72

COMMON → Strat_Pln

0.51

0.52

0.51

0.51

COMMON → X_Train

0.67

0.66

0.78

0.78

COMMON → Emp_Invol

0.78

0.78

0.67

0.67

COMMON → Info_Feed

0.71

0.70

0.71

0.71

TQM TECH → Proc_Mgmt

0.80

0.79

0.80

0.79

TQM TECH → X_Design

0.74

0.75

0.75

0.75

TQM TECH → Supp_Mgmt

0.64

0.64

0.64

0.64

TQM TECH → Cust_Inv

0.62

0.62

0.62

0.62

JIT TECH → Setup_Red

0.75

0.75

0.75

0.75

JIT TECH → Pull_Prod

0.52

0.52

0.52

0.52

JIT TECH → JIT_Delv

0.73

0.74

0.68

0.68

JIT TECH → Equip_Lay

0.68

0.68

0.73

0.74

JIT TECH → Sked_Adh

0.71

0.71

0.71

0.71

TPM TECH → Maintain

0.72

0.71

0.71

0.71

TPM TECH → Tech_Emp

0.69

0.70

0.70

0.70

TPM TECH → Prop_Eqp

0.49

0.50

0.49

0.49

INTEGRATION → COMMON

1.00

1.00

1.00

0.99

INTEGRATION → TQM TECH

0.97

0.98

0.98

0.98

INTEGRATION → JIT TECH

0.75

0.74

0.75

0.74

INTEGRATION → TPM TECH

0.89

0.88

0.88

0.88

INTEGRATION → PERF

0.36

0.37

0.35

0.31

Cost efficiency

Conform quality

On-time delivery

Volume flexibility

281.42

285.16

270.88

282.48

Degrees of freedom

131

131

131

131

Normed chi-square

2.15

2.18

2.07

2.16

RMR

0.06

0.06

0.06

0.06

CFI

0.88

0.88

0.89

0.88

IFI

0.88

0.88

0.89

0.88

CAIC

525.17

528.91

514.63

526.23

PNFI

0.68

0.68

0.69

0.68

< 2.00

< 2.00

< 2.00

< 2.00

0.89

0.92

0.90

0.91

All parameter estimates are standardized and are significant at the 0.01 level.

Model fit statistics Chi-square

Standardized residuals Target coefficient for comparing with direct model

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Improving Performance through an Integrated Manufacturing Program The authors’ research has developed and explored empirically an IMP model and found that there is evidence of the integration of the practices of TQM, JIT, and TPM. Together these practices exhibit a consistent positive effect on multiple dimensions of manufacturing performance and provide an explanation of variation in performance. Their findings demonstrate the importance of implementing the practices belonging to the three programs of TQM, JIT, and TPM. While the practices are closely related, each component of the IMP cannot stand alone and represents a different aspect of an improvement initiative aimed toward product, process, and equipment development. These results provide guidance for managers who wish to introduce new programs or initiatives into a manufacturing environment. New programs must be coordinated with existing programs so that they add to rather than detract from current practices. In addition, it is necessary to consider both the technical and the human and strategic aspects of programs. Together, these aspects lead to improved performance. The authors believe that some managers have intuitively internalized these findings by implementing Lean, Six Sigma, and now the combined Lean and Six Sigma programs. This research demonstrates that managers are moving in the right direction, when they strive for integration of JIT, TQM, TPM, and similar programs such as Lean, Six Sigma, and Lean Six Sigma. This research challenges past research that evaluates manufacturing programs in isolation. Future research should attempt to confirm the authors’ IMP and also determine the nature of the specific relation between the practices that are found to be significant differentiators of performance in this study. REFERENCES Ahire, S. L., D. Y. Golhar, and M. A. Waller. 1996. Development and validation of TQM implementation constructs. Decision Sciences 27, no. 1: 23-56. Ahmad, S. 1998. The relationship between JIT managerial practice and JIT infrastructure: Implications for plant performance. Ph.D. diss., Carlson School of Management, University of Minnesota. Benson, P. G., J. V. Saraph, and R. G. Schroeder. 1991. The effects of organizational context on quality management: An empirical investigation. Management Science 37, no. 9: 1107-1124.

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Maruyama, G. 1997. Basics of structural equation modeling . Thousand Oaks, Calif.: Sage Publications. McKone, K. E., R. G. Schroeder, and K. O. Cua. 1999. Total productive maintenance: A contextual view. Journal of Operations Management 17, no. 2: 123-144. McKone, K. E., and E. N. Weiss. 1999. Total productive maintenance: Bridging the gap between practice and research. Production and Operations Management 7, no. 4: 335-351. McLachlin, R. 1997. Management initiatives and just-in-time manufacturing. Journal of Operations Management 15, no. 4: 271-292. Mehra, S., and R. A. Inman. 1992. Determining the critical elements of just-in-time implementation. Decision Sciences 23, no. 1: 160-174. Milgrom, P., and J. Roberts. 1995. Complementarities and fit: Strategy, structure, and organizational change in manufacturing. Journal of Accounting and Economics 19, no. 2-3: 179-208. Mulaik, S. A., L. R. James, J. Van Alstine, N. Bennett, S. Lind, et al. 1989. Evaluation of goodness-of-fit for structural equation models. Psychological Bulletin 105, no. 3: 430-445. Nakajima, S. 1988. Introduction to TPM . Cambridge, Mass.: Productivity Press. Ohno, T. 1988. Toyota production system: Beyond large-scale production. Cambridge, Mass.: Productivity Press. Powell, T. C. 1995. Total quality management as competitive advantage: A review and empirical study. Strategic Management Journal 16, no. 1: 15-27. Rehder, R. R. 1989. Japanese transplants: In search of a balanced and broader perspective. Columbia Journal of World Business 24, no. 4: 17-28. Robinson, C. J., and A. P. Ginder. 1995. Implementing TPM: The North American experience. Portland, Ore: Productivity Press. Safayeni, F., L. Purdy, R. van Engelen, and S. Pal. 1991. Difficulties of just-in-time implementation: A classification scheme. International Journal of Operations & Production Management 11, no. 7: 27-36. Sakakibara, S., B. B. Flynn, and R. G. Schroeder. 1993. A framework and measurement instrument for just-in-time manufacturing. Production and Operations Management 2, no. 3: 177-194. Sakakibara, S., B. B. Flynn, R. G. Schroeder, and W. T. Morris. 1997. The impact of just-in-time manufacturing and its infrastructure on manufacturing performance. Management Science 43, no. 9: 1246-1257. Samson, D., and M. Terziovski. 1999. The relationship between total quality management practices and operational performance. Journal of Operations Management 17, no. 4: 393-409.

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Improving Performance through an Integrated Manufacturing Program Saraph, J. V., P. G. Benson, and R. G. Schroeder. 1989. An

Wheaton, B., B. Muthén, D. F. Alwin, and G. F. Summers.

instrument for measuring the critical factors of quality manage-

1977. Assessing reliability and stability in panel models.

ment. Decision Sciences 20: 810-829. Schonberger, R. J. 1986. World-class manufacturing: The lessons

of simplicity applied. New York: Free Press. Schonberger, R. J. 1996. World-class manufacturing: The next

decade: Building power, strength, and value. New York: Free Press. Segars, A. H., V. Grover, and J. T. C. Teng 1998. Strategic information systems planning: Planning system dimensions, internal coalignment, and implications for planning effectiveness. Decision Sciences 29, no. 2: 303-345. Skinner, W. 1969. Manufacturing — missing link in corporate strategy. Harvard Business Review 50: 136-145.

Sociological Methodology , ed. D. R. Heise. San Francisco: Jossey-Bass: 84-136. Williams, L. J., and P. J. Holahan. 1994. Parsimony based fit indices for multiple indicator models: Do they work? Structural

Equation Modeling 1, no. 2: 161-189. York, K. M., and C. E. Miree. 2004. Causation or covariation: An empirical re-examination of the link between TQM and financial performance. Journal of Operations Management 22, no. 3: 291. BIOGRAPHIES Kristy O. Cua received her doctorate from the Carlson School of Management at the University of Minnesota. Her dissertation

Steinbacher, H. R., and N. L. Steinbacher. 1993. TPM for

research developed a theory of integrated manufacturing prac-

America: What it is and why you need it. Cambridge, Mass.:

tices relating total quality management, just-in-time, and total

Productivity Press. Takahashi, Y., and T. Osada. 1990. TPM: Total productive

maintenance. Tokyo, Japan: Asian Productivity Organization. Tsuchiya, S. 1992. Quality maintenance: Zero defects through

equipment management. Cambridge, Mass.: Productivity Press.

productive maintenance. Her publications appear in the Journal

of Operations Management. Kathleen E. McKone-Sweet is an associate professor of operations management at Babson College. She is the author of publications that have appeared in both academic and practitioner journals such as Journal of Operations Management, Production

Van de Ven, A. H., and D. L. Ferry. 1980. Measuring and

Operations Management, European Journal of Operations

assessing organizations. New York: John Wiley and Sons.

Research, and Supply Chain Management Review. Most of her

Venkatraman, N. 1989. The concept of fit in strategy research: Toward verbal and statistical correspondence. Academy of

Management Review 14, no. 3: 423-444.

research has focused on world-class manufacturing programs — TPM, TQM, and JIT — and their impact on performance. Her recent research, however, considers supply and demand chain management issues. Prior to her academic career, McKone-Sweet

Venkatraman, N. 1990. Performance implications of strategic

worked at Procter & Gamble. She received her bachelor’s degree

coalignment: A methodological perspective. The Journal of

and master’s degree in engineering from Cornell University, and

Management Studies 27, no. 1: 19-41.

her MBA and doctorate from the Darden Graduate School of

Venkatraman, N., and J. H. Grant. 1986. Construct measurement in strategy research: A critique and proposal. Academy of

Management Review 11: 71-87. Venkatraman, N., and J. E. Prescott. 1990. Environment-strategy coalignment: An empirical test of its performance implications.

Strategic Management Journal 11, no. 1: 1-23.

Business, University of Virgina. She can be reached by e-mail at [email protected] . Roger G. Schroeder holds the Donaldson Chair in Operations Management at the Curtis L. Carlson School of Management, University of Minnesota. He is also the co-director of the Juran Center for Leadership in Quality and has earned an appointment as a Distinguished Teaching Professor. He received his bachelor’s

Voss, C. A., and S. J. Robinson. 1987. Application of just-in-

degree in industrial engineering with high distinction from the

time manufacturing techniques in the United Kingdom.

University of Minnesota, MSIE University of Minnesota, and his

International Journal of Operations & Production Management

doctorate from Northwestern University. Prior to joining the

7, no. 4: 46-52.

University of Minnesota, he taught at the U.S. Naval

Vuppalapati, K., S. L. Ahire, and T. Gupta. 1995. JIT and TQM: A case of joint implementation, International Journal of

Operations & Production Management 15, no. 5: 84-94.

Postgraduate School, Monterey, Calif., and was an analyst for the office of the Assistant Secretary of Defense. Schroeder’s current research interests include operations strategy, quality improvement, and high-performance manufacturing. He has

Ward, J. A. 1998. TQM and the year 2000 crisis. Information

consulted with many public and private organizations and

Systems Management 15, no. 2: 60-63.

serves on numerous editorial boards.

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APPENDIX: Measurements Used Section 1: Measurement of manufacturing performance dimensions Manufacturing Performance Dimensions

Please circle the number that indicates your opinion about how your plant compares to its competition in your industry, on a global basis. 5 = Superior or better than average, 4 = Better than average, 3 = Average or equal to the competition, 2 = Below average, 1 = Poor or low end of the industry. (P1) Quality of product conformance 5 4 3 2 1 (P2) Unit cost of manufacturing 5 4 3 2 1 (P3) Delivery performance (on-time delivery) 5 4 3 2 1 (P4) Flexibility to change volume 5 4 3 2 1

Section 2: Measurement of common practices Committed leadership

All major department heads within our plant accept their responsibility for quality. Plant management provides personal leadership for quality products and quality improvement. All major department heads within our plant work toward encouraging just-in-time production. Our top management strongly encourages employee involvement in the production process. Plant management creates and communicates a vision focused on quality improvements. Plant management is personally involved in quality improvement projects.

Strategic planning

Our plant has a formal strategic planning process which results in a written mission, long-range goals, and strategies for implementation. Plant management is not included in the formal strategic planning process. It is conducted at higher levels in the corporation. The plant has a strategic plan which is put in writing. Plant management routinely reviews and updates a long-range strategic plan. The plant has an informal strategy which is not very well defined.

Cross-functional training

Employees receive training to perform multiple tasks. Employees at this plant learn how to perform a variety of tasks/jobs. Employees are cross-trained at this plant so that they can fill in for others if necessary. At this plant, employees only learn how to do one job/task.

Employee involvement

During problem-solving sessions, we make an effort to get all team members’ opinions and ideas before making a decision. Our plant forms teams to solve problems. In the past three years, many problems have been solved through small group sessions. Problem-solving teams have helped improve manufacturing processes at this plant. Employee teams are encouraged to try to solve their problems as much as possible.

Information & feedback

Charts showing defect rates are posted on the shop floor. Charts showing schedule compliance are posted on the shop floor. Charts plotting the frequency of machine breakdowns are posted on the shop floor. Information on quality performance is readily available to employees. Information on productivity is readily available to employees.

Section 3: Measurement of basic techniques Measurement of TQM basic techniques Process management

A large percentage of the equipment or processes on the shop floor are currently under statistical quality control. We make extensive use of statistical techniques to reduce variance in processes. We use charts to determine whether our manufacturing processes are in control. We monitor our processes using statistical process control.

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APPENDIX, continued Cross-functional product design

Direct labor employees are involved to a great extent (on teams or consulted) before introducing new products or making product changes. Manufacturing engineers are involved to a great extent before the introduction of new products. There is little involvement of manufacturing and quality people in the early design of products, before they reach the plant. We work in teams, with members from a variety of areas (marketing, manufacturing, and so on) to introduce new products.

Supplier quality management

Quality is our no. 1 one criterion in selecting suppliers. We use mostly suppliers that we have certified. Our suppliers are certified, or qualified, for quality.

Customer involvement

We frequently are in close contact with our customers. Our customers give us feedback on quality and delivery performance. We strive to be highly responsive to our customers’ needs. We regularly survey our customers’ requirements. Measurement of JIT basic techniques

Set-up time reduction

We are aggressively working to lower set-up times in our plant. We have low set-up times of equipment in our plant. Our crews practice set ups to reduce the time required. Our workers are trained to reduce set-up time.

Pull system production

Suppliers fill our kanban containers rather than filling purchase orders. Our suppliers deliver to us in kanban containers, without the use of separate packaging. We use a kanban pull system for production control. We use kanban squares, containers, or signals for production control.

JIT delivery by suppliers

Our suppliers deliver to us on a just-in-time basis. Our suppliers deliver to us on short notice. We can depend upon on-time delivery from our suppliers.

Equipment layout

We have laid out the shop floor so that processes and machines are in close proximity to each other. Our machines are grouped according to the product family to which they are dedicated. The layout of the shop floor facilitates low inventories and fast throughput. Our processes are located close together so that material handling and part storage are minimized.

Schedule adherence

We usually meet the production schedule each day. Our daily schedule is reasonable to complete on time. We usually complete our daily schedule as planned. Measurement of TPM basic techniques

Autonomous and planned maintenance

We dedicate a portion of every day solely to maintenance. We emphasize good maintenance as a strategy for achieving quality and schedule compliance. We have a separate shift, or part of a shift, reserved each day for maintenance activities. Our maintenance department focuses on helping machine operators perform their own preventive maintenance.

Technology emphasis

Our plant stays on the leading edge of new technology in our industry. We are constantly thinking of the next generation of technology. We are a leader in the effective use of new process technology. We search for continuing learning and improvement after installation of the equipment.

Proprietary equipment development

We actively develop proprietary equipment. We rely on vendors for most of our equipment. We have equipment that is protected by the firm’s patents. Proprietary equipment helps us gain a competitive advantage.

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