Factors affecting supplier quality performance

JOURNAL OF

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Journal of Operations Management 15 (1997) 243-269

Factors affecting supplier quality performance L.B. Forker * Boston University, School of Management, Operations Management Department, Boston, MA 02215, USA

Received 24 January 1996; accepted 1 October 1996

Abstract The interest among practitioners and researchers in quality management and other factors that may affect quality performance reflects the understanding that a firm's output (i.e., performance) can be only as good as the quality of its inputs. However, studies of the quality management-quality performance relationship have led to mixed results regarding the existence of a positive correlation between the two. The results of a survey of 348 aerospace component manufacturers are examined here to provide new insights into factors that affect supplier quality performance. In this study, the inconsistent association between practice and performance is accounted for by considering the process view of quality management. Process management links quality management with process optimization to address both effectiveness and efficiency concerns. Performance is also affected by transaction-specific investments (asset specificity) in the buyer/supplier relationship that lead to poorer component quality and higher transaction costs. Asset specificity and organizational efficiency at implementing Total Quality Management hold great promise for resolving the mixed practice-performance findings in the quality management literature. © 1997 Elsevier Science B.V. Keywords: Quality; Productivity; Empirical research; Purchasing

1. Introduction Since at least the mid-1980s, global competition and domestic economic pressures have pushed American firms to become more efficient about assuring product quality. The traditional approach of relying solely on inspection to catch and correct product defects has given way to a process orientation of quality management that features prevention and the coupling of quality and productivity (Pall, 1987). Process management links quality management with process optimization to assure both effectiveness and efficiency (Pall, 1987). Quality management includes the variety of planning, assurance, and control activities that contribute to the formulation and implementation of a firm's quality policy and that focus on achieving conformance of process output to requirements (Sinha and Willborn, 1985). Quality performance can be measured on a variety of dimensions: 1. Performance: the product's primary operating characteristics 2. Features: attributes that supplement the product's primary operating characteristics 3. Reliability: the probability of a product failing within a specified time period

* Present address: University of Massachusetts at Dartmouth, Department of Management, North Dartmouth, MA 72747, USA. 0272-6963/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved. PII S0272-6963(97)00001-6

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4. Conformance: the extent to which a product's design and operating characteristics meet predetermined standards 5. Durability: the amount of use a product offers a consumer before the product deteriorates 6. Serviceability: how fast, how easily, and with what degree of courtesy and competence repairs are performed 7. Aesthetics: how a product appeals to the five senses 8. Perceived quality: reputation, image, or other inferences regarding the attributes of a product (Garvin, 1984b, 1987, 1988) If 'requirements' are defined as an end-user's desired characteristics for a product, then 'conformance to requirements' may encompass all eight dimensions. However, in intermediate manufacturing industries (the setting for the current study), quality performance is typically defined as 'conformance to specifications' (Juran, 1988). Therefore, the more narrowly defined 'conformance' dimension is the measure used in this research. For the past decade, American producers have devoted great effort to improving the quality of their products. Much of this effort has focused on the implementation of Total Quality Management (TQM) practices in intermediate and final product manufacturing operations. TQM is an integrated system of principles and procedures that utilizes quantitative (technical) and human resource (behavioral) methods to improve the quality of material and service inputs, intra- and interorganizational processes, and an organization's focus on meeting customer needs (Department of Defense, 1995). A recent study of TQM in the transportation, electronics, and machinery industries showed a positive correlation between a composite measure of quality management and a composite measure of internal and external quality (Flynn et al., 1993, 1994). However, some concurrent industry surveys have reported little or no performance improvements from TQM implementation (Bleakley, 1993; Fuchsberg, 1992a,b, 1993; Jacob, 1993; Mathews, 1993, Mathews and Katel, 1992; Naj, 1993). Why the TQM implementation-quality performance relationship has remained so inconsistent has been largely unexplained. The interest among practitioners and researchers in quality management practices reflects the understanding that a firm's output can be only as good as the quality of its inputs. However, by focusing exclusively on management practices, only one part of process management has been considered. Process optimization - the efficiency (productivity) of the improvement process - is an important complement to quality management (Pall, 1987). In the current study, both the management practices and the efficiency of those improvement processes are examined by focusing on the supplier population to an intermediate manufacturer of aerospace subassemblies. The intermediate manufacturer is both a customer (of components) and a supplier (of subassemblies) to the airframe industry. Previous studies have focused their attention on the quality practices and performance of manufacturers of finished goods (Garvin, 1983, 1984a, 1986, 1988). While examination of finished goods' producers' planning, assurance, and control is valuable for understanding successful quality practices, analysis of quality management and other processes implemented by an industry's suppliers is of equal importance in explaining the process-performance association. In this study, the inconsistent relationship between process and performance is accounted for by considering structural differences among the aerospace component producers surveyed. Structural differences are system-wide features of a firm's manufacturing and organizational processes where overall performance is influenced by nonlinear interactions among the system's components. Structural differences may be due to varying decisionmaking competencies among managements, better and worse communication with workers, different levels of morale/cooperation among firm employees, diverse degrees of intelligence and learning among a firm's workers, and/or any of a number of other intangible characteristics that directly impact company processes. The importance of the interplay between system components or processes means that 'aggregate behavior can be described without a detailed knowledge of the behavior of the individual agents' (Holland and Miller, 1991, p. 365). The aggregate behavior often differs from firm to firm, leading to various means of transforming inputs into outputs. These structural differences, which influence the efficiency with which company-wide programs (such as TQM) are implemented, may provide the key to why some companies have been effective in improving product quality while others have not. Both aspects of process management - quality management practices and process efficiency - should be considered when examining factors that affect supplier quality performance.

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2. Literature review

2.1. Supplier quality management

Supplier quality management has received less attention from both businesses and researchers than has the management of finished goods' quality, despite the belief that a company's output is only as good as its inputs. Numerous surveys and company case studies have been performed among the Fortune 500 and other large companies. (See Hiam, 1993, for an overview of some of these studies.) However, few surveys have addressed the small-to-medium suppliers that manufacture the building blocks of finished goods. The studies reviewed here deal specifically with supplier quality management. Supplier quality performance is influenced by at least two factors: internal operational practices and the customer-supplier relationship (the latter of which distinguishes it from internal quality performance) (Gitlow and Wiesner, 1988). Several studies have considered the influence of the customer-supplier relationship on supplier quality performance. Carter (1986) and Carter and Miller (1989) examined the effect of two different customer-supplier communication structures (serial and parallel) on the quality of mechanical seals manufactured in a supplier firm to the aircraft industry; their analysis indicated that the parallel communication structure was associated with fewer component defects. Lascelles and Dale (1989) surveyed 300 UK-based automotive suppliers to analyze the impact of three customer firms' supplier management practices on supplier quality. Supplier management activities of the customer firms were found to have little effect on the quality management practices of their suppliers. Instead, suppliers responded to customer firms with considerable purchasing power by engaging in 'stratified quality control' to grade the quality of their products according to the power of the customer. Very few customer firms actively worked with their suppliers to solve quality problems; requirements for suppliers to use statistical process control (SPC) or certain quality management techniques (as a condition of purchase) were considered sufficient to provoke product improvement. More recently, an analysis of the PIMS (Profit Impact on Marketing Strategies) database examined the relationship between availability of suppliers and relative product quality (Kekre et al., 1995). A two-stage least squares estimation found a positive relationship between restricted availability of suppliers and higher perceived product quality. Firms with superior products were also found to have a higher likelihood of being restricted in their choice of alternative supply sources. The authors concluded that a reduced supply base procurement strategy will significantly improve product quality (Kekre et al., 1995). While these three studies considered the influence of the customer-supplier relationship on quality performance, the impact of supplier process management on quality performance has received less attention. The current study addresses this research issue. 2.2. Quality management and quality performance

The 1990s have produced a number of empirical examinations of quality management's effect on quality performance; these analyses have reported conflicting findings. While many studies have been published, this literature review focuses on the results of large-scale surveys, particularly those published in refereed journals. (See Hiam, 1993, for a review of TQM studies carried out by consulting firms and private trade organizations.) One of the most widely quoted studies of TQM is a report by the American Quality Foundation and Ernst & Young (1991) that relayed the results of in-depth field interviews with over 580 automotive, computer, banking, and health care organizations in Canada, Germany, Japan, and the United States. Of 945 practices surveyed, only three significantly affected productivity, quality and/or profitability across all four industries, all four countries, and all levels of beginning performance: process improvement methods, strategic plan deployment, and supplier certification programs. TQM is one of the most commonly used process improvement tools. A key cornerstone of supplier certification programs is quality performance and the management policies, manufacturing processes, and assurance activities necessary to keep conformance high, with minimal variation. The

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American Quality Foundation/Ernst & Young survey demonstrates the widespread applicability of these improvement practices. One of the early academic studies of the relationship between management practices and quality performance was part of a research instrument developed and tested in the transportation, electronics, and machinery industries (Flynn et al., 1993, 1994). Quality performance was measured on two dimensions: 'internal quality performance' and 'external quality performance'. Internal quality performance was an objective measure that asked survey respondents to report the percent of items their firm produced that proceeded through final inspection without requiring rework (Flynn et al., 1993, 1994). External quality performance was a perceptual measure that asked survey respondents to assess the 'quality program's contribution to the plant's distinctive competence' (Flynn et al., 1994). Initial analyses using regression to evaluate the significance of the relationship between the eight quality management practices and the two quality performance measures found only one practice to be directly related to internal quality performance (process management) (Flynn et al., 1993). Several practices were related to external quality performance and external quality was found to be strongly correlated with a plant's competitive advantage (internal quality was not significant) (Flynn et al., 1993). Subsequent analyses used canonical correlation to establish a relationship between the set of TQM practices and the internal and external performance measures evaluated together. The initial results from the regression runs suggest that the significance found in the later canonical correlations performed to assess criterion-related validity of the quality management measurement instrument may have been highly influenced by the strong correlation between the perceptual measure of external quality and the practices (Flynn et al., 1994). Since the quality management practices and performance measures were treated as sets, the individual relationships between practices and performance measures were not reported. The correlation between internal quality performance and the management practices may have been quite low. In a follow-on study of the same data set, Flynn et al. (1995) divided the 42 survey respondents into roughly equal groupings of high-, medium-, and low-quality performers, based on the internal performance measure (i.e., the plant's 'percentage of products that pass final inspection without requiring rework'). Differences in the quality management practices used by these three groups were investigated using multiple discfiminant analysis. Practices which most significantly differentiated high- from medium- and low-quality plants included (in order, from best to worst): employee involvement, process control, new-product quality practices, concurrent engineering, feedback, maintenance, supplier relationship, labor skill level, and selection for teamwork potential (Flynn et al., 1995). Practices which did a poor job of differentiation were customer interaction and design characteristics (Flynn et al., 1995). The interesting finding that plants reaching the highest and lowest levels of quality performance used similarly high levels of management practices while plants in the middle of the performance spectrum implemented lower levels of quality management suggests that quality management and performance may have a nonlinear relationship. The current study investigates this intriguing possibility. The final analysis found of the quality management-quality performance relationship that uses actual (instead of perceptual) data is a survey of company presidents, manufacturing vice-presidents, general managers, plant managers, owners, and quality managers who are members of the Operations Management Association (Adam, 1994). Stepwise regression was used to correlate 20 managerial practices (13 related to quality improvement and seven related to productivity improvement) to eight quality performance measures, three operating performance measures, and three financial performance measures. (All practices and performance measures were self-reported by survey respondents.) The 20 managerial practices were factor analyzed and reduced to five groups of correlated items. Ten performance measures were found to have one or more factors significantly correlated; no performance measure had more than three significant explanatory variables. All R2s for the stepwise regressions using objective performance measures were quite low; they ranged from 0.056 for 'average percent of items defective' to 0.146 for 'total cost of quality as a percent of sales'). Additional stepwise regressions performed with only the quality improvement practices found no significant relationships between those items and the quality performance measure most closely related to the one used in this study and

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in the Flynn et al. (1993, 1994, 1995) studies - 'average percent of items defective'. The author concludes: 'A case can be made from this study that TQM is a failure' (Adam, 1994, p. 41). This conclusion may be premature. While reports in the business press have reported little or no performance improvements from TQM implementation (Bleakley, 1993; Fuchsberg, 1992a, b, 1993; Jacob, 1993; Mathews, 1993; Mathews and Katei, 1992; Naj, 1993), the achievements of the Baldridge winners and other globally successful companies suggest that TQM can indeed lead to high quality performance. Moreover, the findings of Flynn et al.'s (1995) follow-on study indicate that TQM and quality performance may have a nonlinear relationship. When cross-sectional (e.g., answers to a survey administered at a given point in time) and time series data (e.g., data covering several years, months, or varying business cycles) are used together in estimating a relationship, a model must be specified 'that will adequately allow for differences in behavior over cross-sectional units as well as any differences in behavior over time for a given cross-sectional unit' (Judge et al., 1980, p. 325). The error components model, which assumes that slope coefficients are constant and that error terms have mean zero and constant variance, uses a changing intercept term to capture differences in behavior or structure over individuals (the cross-sectional units) (Judge et al., 1980). The individual differences are random variables that are assumed to be uncorrelated with the error terms, have mean zero and constant variance, and whose cross-product terms with other random variables also have a mean of zero. Cross-product terms are often used to represent interaction between two or more variables. By considering the efficiency (productivity) with which behavioral (structural) units implement TQM, individual differences captured in the error components model can be explicitly recognized in the data analysis (Judge et al., 1980). Productivity and quality have long been recognized to be interrelated. For example, Gitlow and Hertz (1983) observed that reducing defectives improved productivity by allowing for more efficient utilization of available resources. Similarly, using fewer resources to achieve a given level of quality (through process adjustments) can improve performance, especially if the diminished resources are labor hours devoted to rework and reinspection. Productivity (efficiency) - the ratio of output achieved to input required - is a nonlinear measure. The interaction of these related concepts (productivity and quality) may provide the link between process improvement practices and quality performance. The possibility of a nonlinear association (such as the proposed interaction) has not been explored so far in the quality management literature. By presuming only linear relationships between quality management practices and performance, an analogous assumption has been made that these relationships are symmetrical (which they may not be). Furthermore, by examining responses from multiple industries in the same analysis, previous studies have introduced a potential confounding factor of variance by industry. These issues are addressed in the current study by: (1) limiting the study to suppliers of aerospace components and (2) comparing analyses using a traditional linear model to an alternative model that incorporates the anticipated nonlinear relationship. Another potential source of bias is eliminated - the self-reporting of quality performance measures used in all previous studies - by utilizing objective performance data collected on the responding population by a separate third party (a customer firm common to all survey respondents). Finally, the possible moderating influence of bilateral customer-supplier dependence on component quality performance is examined. Before describing the model, hypotheses, and data collection procedures, the theory behind supplier firms' structural differences, the efficiency implications of those differences, and the possible consequences of customer-supplier bilateral dependence will be explored.

3. Theoretical foundation 3.1. Efficiency as an indicator o f structural differences

In examining the process management-quality performance relationship and the potential effects of structural differences among supplier firms on the quality of manufactured output, the efficiency of the improvement

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process (i.e., TQM implementation) is a central, yet unexplored, concern. Efficiency - or how well a firm is utilizing its managerial, labor, and capital resources for a given level of output - is important because efforts spent on rework, retests, reinspection, and other nonproductive activities (activities aimed at reducing avoidable defects and improving conformance) generate avoidable operating costs for a firm that contradict the long-term goal of reduced costs through quality improvement. Transaction cost economics explicitly considers the efficiency implications of alternative production relationships (Williamson, 1981, 1985). Transaction costs are the 'costs of running the system' that depend on the implementation of an exchange itself, rather than on the prices of goods and services produced/traded that are determined by the competitive market (Arrow, 1969; Robins, 1987). While transactions differ from each other according to a number of parameters, their critical characteristics include how often they take place, the uncertainty associated with the transactions, and the extent and form of asset specificity associated with the good or service supplied (Williamson, 1981). Transaction costs encompass: the costs of search and information; the expenses of determining and setting prices; the costs of negotiating contracts; and the costs of measuring, supervising, and enforcing contractual performance (Robins, 1987). Human learning curve effects and information processing/communication difficulties can alter the magnitude of these costs.

3.2. Asset specificity Asset specificity (also called transaction-specific investments) is generally considered to be the most important dimension used in transaction cost economics to discriminate between various types of transactions because it increases transaction costs regardless of governance type (Klein et al., 1978; Williamson, 1981, 1991). Asset specificity refers to significant fixed investments that are unique to a particular transaction and cannot be redeployed easily for other applications or to other users without degrading productivity, yield, or value (Robins, 1987; Williamson, 1991). The following categories of significant fixed investments have been used to differentiate asset specificity (Masten et al., 1991; Williamson, 1991): 1. Site specificity 2. Physical asset specificity 3. Human-asset specificity 4. Brand name capital 5. Dedicated assets 6. Temporal specificity Site specificity is characterized by assets (e.g., plant and equipment) that are situated close together to reduce inventory and transportation costs. Physical asset specificity describes assets such as tools, dies, molds, or special software that are made to an individual customer's specifications for production of a good or service. Human-asset specificity refers to knowledge gained by experience with particular firms and to relationships built up between individuals or departments that have a history of inter-firm exchange. Brand name capital consists of intangible, firm-specific reputations that are gained through past performance or precipitated by advertising. Dedicated assets consist of plant and equipment purchased to fill the order(s) of specific customers. And temporal specificity is investment in assets to enhance timely responsiveness by on-site employees. These specificity types, especially the first five, increase contracting risks between the two parties to a transaction and introduce bilateral dependence. Monitoring exchange partner performance becomes more challenging, leading to a safeguarding problem for the customer and supplier firms involved and an increase in transaction costs. Performance on any of the dimensions by which exchange partners evaluate each other may degenerate and, due to the increased monitoring costs, efficiency deteriorates. 4. Model and hypotheses To examine the effect that efficiency and other factors have on supplier quality performance, the aerospace industry was chosen for analysis. Component quality is critical for airplane performance and travel safety. Not

LB. Forker~Journal of Operations Management15 (1997) 243-269 Exlmm of I"QM Implemmltatlon

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QualRy

.[

Performance

Fig. 1. Conventionallinearmodel.

only are airplane failures highly visible to the public, but airframe sales also account for a major portion of military expenditures and US exports due to their history of conformance and reliability. The entire population of suppliers to one aerospace customer was surveyed. Because the common customer is one of the largest US manufacturers of aerospace subassemblies, their supplier base was judged to be an adequate representation of all aerospace part producers. The suppliers' responses to the survey provided data for answering the research questions addressed by this study, which are: 1. Does the implementation of quality management practices in intermediate manufacturing facilities affect quality performance, as measured by a common customer firm? and 2. What insights can be gained by examining process optimization along with quality management that will help researchers and businesses better understand how to improve performance? 4.1. Does quality management directly affect quality performance ?

Examination of the first research question is begun by considering the model portrayed in Fig. 1. At the beginning of the 1980s, varying degrees of quality management involvement by 16 American and Japanese room air conditioner producers led to dramatic differences in quality output (Garvin, 1983, 1984a, 1986, 1988). Ten years later, Flynn et al. (1993, Flynn et al., 1994) found a positive relationship between quality management and quality performance in the electronics, transportation, and machinery industries. However, the research findings of Adam (1994) and reports in the business press suggest that quality management implementation does not in and of itself guarantee high performance (Bleakley, 1993; Fuchsberg, 1992a,b, 1993; Jacob, 1993; Mathews, 1993, Mathews and Katel, 1992; Naj, 1993). The first hypothesis proposes the common-sense expectation that a direct relationship exists: HI: Extent of TQM implementation in intermediate manufacturing facilities is positively related to quality

performance. 4.2. Does process optimization interact with quality management to impact performance?

Given the mixed results of quality practice and performance studies, it was then hypothesized that even though TQM practices might affect component suppliers' quality performance, there exist, in addition, structural differences among the supplier firms that are embodied in variables not included in the model. These unobserved differences cause TQM to be implemented in a variety of ways. Just as students' grades may show no correlation to study time (when a positive relationship would be expected to exist), so too may TQM implementation show no relationship to performance. Different students have different 'intercepts' - an unobservable characteristic we call 'intelligence' - which affects the efficiency with which they learn. Similarly, different firms have different abilities, previous performance levels, and attitudes toward learning and change that affect the efficiency with which they implement process improvement programs such as TQM. Since we can't directly observe intelligence, we use a surrogate variable in statistical analyses to represent intelligence (e.g., SAT scores, IQ scores, etc.). Because we can't directly observe (and neatly capture) structural differences among supplier firms, a surrogate measure must be used in analyzing these data as well. Intelligence affects students' efficiency at learning; similarly, structural differences among suppliers should be correlated with (and reflected in) the suppliers' relative efficiency at carrying out the quality management practices

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h.p

U J

1

I Otml~

Optlmlzmtion

Fig. 2. Alternativemodel.

intended to improve output quality. The interaction of the suppliers' relative efficiency and extent of TQM implementation could capture a possible nonlinear association between quality practice and performance; its correlation with performance would indicate that quality management depends on (i.e., varies with) implementation efficiency. Southwood (1978) (p. 1156), in developing five statistical interaction models, identified nonlinearity as being analogous to interaction; the statistical significance of an interaction variable would therefore indicate a nonlinear association between dependent and independent variables. The proposed relationship is pictured in Fig. 2 and the corresponding hypothesis proposes: H2: The interaction of process optimization (efficiency) and extent of TQM implementation is positively related to quality performance. 4.3. Does bilateral dependence moderate the process management-quality performance relationship?

Transaction cost theory predicts that particular characteristics of transactions (namely, transaction-specific investments [asset specificity] and external/internal uncertainty) unite to cause 'failure' in traditional market exchange. Market failure occurs when irreversible investments made by the buyer and supplier lock the exchange partners into a relationship where the investments have far greater value within the relationship than outside it. For example specialized tooling, developed by a supplier to manufacture unique parts for a certain customer, ties the buyer and supplier; the supplier cannot readily sell the fabricated parts to an alternative buyer (should this customer cancel his order) and the buyer might have to pay for the same tooling to be developed again if he were to switch suppliers. So, the buyer and supplier maintain their trading relationship, even if they feel their partner has 'failed' to deliver what was expected. Failure is manifested in suboptimal performance by either the buyer or supplier on any of the dimensions by which each partner evaluates their exchange relationship. For a buyer evaluating a supplier, these dimensions have typically been cost, quality, delivery, and service/flexibility (Dobler et al., 1990). Because the existence of asset specificity makes exchange partners very difficult, costly, or even impossible to replace, the possession of transaction-specific assets is conceptually equivalent to dependence (Barney and Ouchi, 1986; Heide, 1994). Human limitations in ability to predict future outcomes and partner behavior compound the problems brought on by asset specificity/dependence. The last hypothesis, therefore, proposes that bilateral dependence will result in suboptimal quality performance, a contributor to market failure. The transaction-specific investments behind the bilateral dependence create transaction costs that lead to inefficiency in the exchange relationship and a safeguarding problem for the customer and supplier firms involved (Klein et al., 1978; Williamson, 1985). While the dependent variable captures only the supplier's quality performance, the performance measure used is one that is monitored by both buyer and supplier. Independent measures of the customer firm's dependence on each supplier and each

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L=t~ct ot Bllatmal

,1 Fig. 3. Alternativemodel with moderatingeffectof buyer/supplierdependence.

supplier's dependence on the common customer are introduced to capture the bilateral dependence in the relationship. Transaction cost theory intimates that this bilateral dependence will negatively affect quality performance. The proposed moderating effect of bilateral dependence is shown in Fig. 3. The third hypothesis suggests: H3: Bilateral dependence, a reflection of transaction-specific investments made by the buyer and supplier firms, will be negatively related to quality performance.

5.

Methodology

5.1. Source of the sample

The aerospace firms contacted for this study are all suppliers to a common customer fabricator of aerospace subassemblies. The common customer firm is a division of a Fortune 500 conglomerate corporation. The division supplies various subassemblies and services to aviation customers, both military and civilian, in the government, commercial, and overseas markets, In 1990, the division initiated a performance rating system for each supplier that considers buyer evaluations of a supplier's responsiveness, cost-cutting initiatives, and documentation adequacy along with the supplier's offered price. Further changes occurred in 1991 when, in response to recessionary pressures and reduced government defense spending, the corporation announced a series of streamlining moves that included centralization of procurement and other business functions. As part of the restructuring effort, all suppliers were surveyed, evaluated, and rated. Poorly performing suppliers were slated to be removed from the supply base. Remaining suppliers would fill a larger share of the customer firm's supply orders. 5.2. The research instrument

To empirically examine the practice-performance relationship among these aerospace suppliers, a reliable and valid measuring instrument of quality management (inputs) and a valid measure of quality performance (outputs) were required. The research instrument developed by Saraph et al. (1989) for measuring the critical factors of quality management was chosen to measure the quality practices due to its empirical verification in a different production (and geographic) setting. Saraph et al. (1989) constructed their survey instrument from the writings of quality management scholars and gurus, and tested the instrument in 20 service and manufacturing firms in the Minneapolis-St. Paul area. The researchers identified eight critical factors of quality management:

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

Role of management leadership and quality policy (TM) Role of the quality department (QD) Training (T) Product/service design (PD) Supplier quality management (SM) Process management (PM) Quality data and reporting (QR) Employee relations (ER) Because these eight factors had been shown to be valid and reliable in an alternative research setting, the factors were used in the present study to represent the practices critical to TQM implementation (Saraph et al., 1989). Many of these factors were first proposed by Garvin (1983, Garvin, 1984a, 1986, 1988) in his study of quality management practices and performance in the Japanese and American room air conditioner industry. The practices that Garvin found to contribute to lower defect and field failure rates included: 1. Quality programs, policies, and attitudes 2. Quality information systems 3. Product design review processes 4. Production training and work force policies 5. Vendor management practices These factors were incorporated into the Saraph et al. (1989) instrument and expanded. Saraph et al. (1989) validated their instrument by examining the eight scales for reliability, item-to-scale correlations, content validity, criterion-related validity, and construct validity. One construct (Process management) split into two factors; after examining the items that loaded onto the second Process management factor, those questions were eliminated from this survey instrument. The items either repeated other questions that loaded onto the first factor or else asked questions about practices that were rather outdated. Combining the items dropped in this study and those by Saraph et al. (1989) to improve their instrument's reliability, a total of 16 items from the original research instrument were deleted, leaving 62 quality management practices that were included on this study's survey instrument. (See Appendix A for a listing of all items and constructs, their corresponding abbreviations, and the response scale, as used in this research.) A second modification made to the instrument involved the scale for measuring the implementation of each practice. Saraph et al. (1989) utilized a scale that evaluated level of use (from 'Very Low' to 'Very High') of current and ideal practice of each item. The survey instrument used in the present study employed a scale measuring 'Current extent of use' (from 'No Extent' to 'Great Extent'). Because component product quality is measured by manufacturers in terms of conformance to specifications (Juran, 1988), a scale for the perceptual measures of quality management practice that would also reflect 'degree' of quality (i.e., conformance) was desired. Therefore, 'Current extent of use' was used. Five-point scales were chosen over seven-point scales due to the findings of Lissitz and Green (1975) who showed through a Monte Carlo simulation that the improvement in reliability (as evidenced by coefficient alpha) levels off after five scale points are exceeded. 5.3. The quality performance data

Data on the suppliers' quality performance were supplied by the common customer firm. These data are the actual measures used by the customer firm to evaluate its suppliers' quality performance for contractual

compliance and contractual renewal purposes. The customer firm records component part defectives found in source, receiving, and assembly/detail inspection, as well as any field failures that can be traced back to a part. Component part defectives are recorded at various inspection points and are classified into six inspection/rejection types: outside process rejection, field rejection, rework, shop overload, assembly line rejection, and detail inspection rejection. The first four inspection/rejection categories are defectives found either at the supplier's

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LB. Forker/Journal of Operations Management 15 (1997) 243-269 Table 1 Aerospace suppliers' location relative to customer firm Location Respondents Outside US Eastern US Rest of western US Rest of state Same metropolitan area

Nonrespondents

#

%

#

%

7 109 89 5 54

2.6% 41.3% 33.7% 1.9% 20.5%

1 19 31 1 20

1.4% 26.4% 43.1% 1.4% 27.8%

Xz test for differences g2 Sig. 3.77

0.44

facility or at the customer's receiving inspection. Outside process rejection consists of defectives found at the supplier's facility by the supplier's quality control personnel. Field rejection consists of defectives found at the supplier's facility by the customer's quality control personnel who made a field inspection. Rework includes defectives found during the customer's receiving inspection that were reworked, rather than returned to the supplier. Shop overload covers defectives found during the customer's receiving inspection that were returned to the supplier. A s s e m b l y line rejections include defectives discovered during assembly operations at the customer firm. And detail inspection rejections consist o f defectives found during final inspection of the customer f i n n ' s assembled products. H o w defectives are handled varies from case to case. For example, if an imperfect lot is discovered during inspection containing parts that are not immediately needed in production, the entire lot will be sent back to the supplier for replacement. However, if the part is needed quickly in production, the defective units will be reworked by the customer f i r m ' s production personnel. Around 50% of the time, entire lots are screened when a defective is found in sampling inspection; whether a lot is screened or not depends on the criticality of the part found defective and the lead time available before the part is needed in production.

5.4. Survey response A survey instrument composed of the quality management constructs and practices described earlier was used for data collection purposes. After pretesting the instrument with four finns, survey instruments were mailed to 348 aerospace component suppliers. A modified version of Dillman' s (Dillman, 1978) Total Design Method was used. Two hundred and ninety firms responded, for a raw response rate of 84%. However, some surveys were incomplete. In addition, other suppliers, while returning completed surveys, had not sold any components to the focal customer firm in the previous year, leaving the customer firm with no current quality performance data to share with the researcher. These responses were therefore unusable. Removing the two sources of unusable

Table 2 Major product line supplied Product line Forgings Castings Electrical components Sheet metal/airfoils Mechanical components General machining Other components

Respondents

Nonrespondents

X2 test for differences

#

%

#

%

X2

Sig.

20 29 33 40 59 60 23

7.6% 11.0% 12.5% 15.1% 22.4% 22.7% 8.7%

2 6 10 2 10 32 10

2.8% 8.3% 13.9% 2.8% 13.9% 44.4% 13.9%

14.91

0.02

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Table 3 Size of respondent firms Size as measured by number of employees

Frequency

Percent of sample

Large ( > 300) Medium (101-300) Small (1-100)

57 88 119

21.6% 33.3% 45.1%

surveys left a sample of 264 responses (usable response rate = 76%). Respondent finns in the usable sample ranged in size from 3 employees to 3700 employees, with average finn size around 253 workers. Respondents were located in geographic areas across North America and represented a variety of product lines. There were no significant differences in the locations of responding and non-responding supplier finns and only a moderate difference in the major product lines supplied. (Crosstabs of respondent status by supplier location in relation to the focal customer firm, and of respondent status by major product line supplied to the focal customer finn were calculated, g 2 statistics for differences among responding and non-responding supplier finns, sorted by location and major product line supplied, are provided in Tables 1 and 2.) Frequency distributions for the sample respondents' demographics are laid out in Tables 1-3; comparative statistics for non-respondents are presented in Tables 1 and 2.

6. Analysis 6.1. The constructs Because survey items and constructs tested in previous studies were used in this research, the same procedure as Saraph et al. (1989) was employed to operationalize the constructs. This involved averaging together the items assigned to each construct by the original three researchers (with the modifications to the Process management construct incorporated, as noted above). Each construct was factor analyzed and checked for reliability. Table 4 summarizes the results of the reliability analyses. Because component loadings for the constructs stayed within a narrow bandwidth of 0.884 to 0.581, there was no compelling reason to use the weights of the actual loadings (instead of the equal weights of an arithmetic average) in computing each construct's mean; both methods would lead to essentially the same value.

Table 4 Summary of reliability analyses Construct

Average item-to-total correlations for construct items

Coefficient alpha

Role of top management Training Employee relations Product/service design Quality data and reporting Role of the quality department Supplier quality management Process management/operating procedures

0.76 0.77 0.75 0.80 0.75 0.80 0.66 0.71

0.94 0.90 0.89 0.89 0.89 0.86 0.82 0.81

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6.2. Descriptive statistics Supplier quality performance data were collected and provided by the common customer firm. The performance data consisted of defectives produced by each supplier found through inspection at the supplier's facility, receiving inspection at the customer firm's facility, assembly line rejections at the customer's facility, final inspection rejections by the customer's quality control personnel, and field failures, to the extent that a failure could be traced back to a specific component. Defective items reworked by the supplier or customer firm's production personnel were included in the performance data. These performance data are the actual objective quality measures used by the customer finn to evaluate its suppliers' quality performance. The quarterly piece part rejection data provided were summed, converted to defective parts per million (DPPM), and finally inverted and normalized to the interval 0 to 1 according to the following transformation: DPPM APPM = 1

(1)

1 000 000

This transformation was performed to avoid scaling problems and to facilitate the interpretation of results. (The original DPPM data were measured on a scale of zero to 1 000000 while the TQM practices were measured on a scale of one to five.) According to the transformation, a value of 1 for APPM (acceptable parts per million) corresponds to zero defectives (i.e., DPPM = 0), and a value of 0 corresponds to all defectives (i.e., DPPM = 1 000 000). Ranges, means, and standard deviations for the eight TQM constructs and the DPPM and converted APPM data are displayed in Table 5.

6.3. Testing the hypotheses 6.3.1. Hypothesis 1 Using the eight constructs and the transformed quality performance data, the following linear model was fitted to test hypothesis 1, establishing the relationship between the 'critical factors of quality management' and APPM: APPMj =/3,, +/3~TMj + / 3 2 S M j +/33Tj +/34PD j +/35QD j + / 3 6 Q R / + / 3 7 P M i + / 3 s E R j + ej

(2)

Although all suppliers in the sample are from the aerospace industry, different firms produce different commodities and may use different TQM practices that vary with their major product line. Chi-square tests performed with respondent and non-respondent data indicated that suppliers of some commodities are better represented in the sample than suppliers of other commodities. (See Table 2.) To be sure that neither TQM Table 5 Descriptive statistics Variable Minimum

Maximum

Mean

Standard deviation

ANOVA by major product line supplied F

Sig. of F

QD TM PD ER QR T PM SM DPPM APPM

5 5 5 5 5 5 5 5 1000000 1

4.22 3.96 3.57 3.43 3.41 3.34 3.31 3.24 73114.09 0.93

0.58 0.67 0.77 0.71 0.72 0.78 0.70 0.62 164822.39 0.16

0.96 0.68 0.44 1.74 1.10 0.60 1.50 0.22 0.96 0.96

0.45 0.67 0.85 0.11 0.36 0.73 0.18 0.97 0.45 0.45

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practices nor the quality performance data were influenced by the unequal representation of certain commodity groups, one-way ANOVAs for each of the independent and dependent variables in Eq. (2) were performed. Table 5 indicates that no systematic differences were found among different commodity groups for either TQM practices or quality performance; consequently, no adjustments were made for commodity groups in subsequent analyses. 6.3.2. Hypothesis 2

To test hypothesis 2, it was necessary to first define and calculate a measure of efficiency that would capture the structural differences among the intermediate manufacturing firms surveyed. A supplier (or any other economic agent) is said to be 'efficient' if it is not possible to increase (decrease) any of its outputs (inputs) without increasing (decreasing) any of its inputs (outputs) (Charnes et al., 1981). Relative efficiency (relative to the best-performing suppliers in the sample) for the individual firms can be measured by using Data Envelopment Analysis (DEA) (Chames et al., 1978, 1981). DEA is a non-parametric linear-programming-based methodology that can evaluate multiple inputs and multiple outputs to calculate a ratio of total weighted output to total weighted input; this ratio (generated from actual field data) is the relative efficiency of a supplier. In calculating this ratio, DEA assumes constant returns to scale and perfect information. Each supplier's relative efficiency rating is calculated in relation to all other suppliers in the data set, using the collected data for each firm's inputs and outputs. The resulting ratio is not a measure of absolute efficiency but rather a calculation of the maximum output empirically attainable for any supplier in the data set, given the extent of its TQM implementation. For each supplier, a measure of relative efficiency was calculated as the maximum ratio of weighted APPM (output) to weighted TQM practices (inputs), subject to the conditions that similar ratios for every supplier be less than or equal to one and that weights chosen for the various inputs and output be greater than or equal to zero (Charnes et al., 1978). In mathematical terms, these conditions can be formulated for the supplier k as follows: U1 ×

Max ~,~, . . . . 8 v l × T M k + v z × P D k + V 3 × S M k + v

u I × APPMi


s.t. V 1 × T M i --[- u 2 ×

PDi +

APPM k

4×QD k+v 5×PM k+v 6×QR k+v v×ER k+v 8×T k .....

N

...

u 1,v 1. . . . . v 8 > 0

(3)

There is an infinite number of solutions to the fractional program presented above. If (u~, v 1. . . . . v 8) is a solution, so is (ku~, kv 1. . . . . kv8). Two solutions that differ by a proportionality constant are said to be equivalent. Charnes and Cooper (1962) developed a transformation to change the fractional program into a linear program, selecting a representative solution from each equivalent class. The representative solution is the one for which u 1 × T M k + V2 × PD k + ... + v 8 × T k = 1. Applying this transformation to the current problem, the following linear program was obtained: Max u I × APPM k

(4)

S.t. v I ×TMk+v2×PDk+

. . . + v 8 × Tk = 1

u 1 × A P P M i - v I × TM i - v 2 × P D i - ... _< O f o r i = l , 2 . . . . . U 1, V 1, . . . ,

N

V8>__0

An optimal objective function value of one for the linear fractional program presented above indicates that the supplier under consideration is efficient relative to its peer suppliers. The set of all efficient suppliers are

257

LB. Forker/Journal of OperationsManagement 15 (1997) 243-269 Table 6 Partial results of the DEA analysis Supplier DEA Supplier

DEA

Supplier

DEA

Supplier

DEA

1 2 3 4 5 6 7 8 9 10

0.667 0.477 0.407 1 0.540 0.620 0.894 0.686 0.589 0.599

21 22 23 24 25 26 27 28 29 30

0.596 0.489 0 0.694 0.616 0.482 0.657 0.507 0.987 0.491

31 32 33 34 35 36 37 38 39 40

0.943 0.828 0.665 0.815 0.537 0.490 0.546 0.257 0.723 0.571

0.738 0.551 0.596 0.528 0.960 0.577 0.484 0.494 1 0.734

11 12 13 14 15 16 17 18 19 20

said to lie on the 'efficient frontier' of the process that links TQM practices to APPM; this efficient frontier is assumed to be nonlinear. A number less than one for the DEA rating indicates that the supplier is 'inefficient' relative to other suppliers in the data set. The DEA rating also indicates how far the inefficient supplier is from the efficient frontier. The closer the DEA rating is to one, the closer the supplier is to the efficient frontier. A DEA rating of zero would indicate a totally inefficient supplier. Because reports in the business press have indicated a weak or nonexistent relationship between TQM implementation and quality performance when one would expect a strong positive relationship to prevail, differences in how TQM practices are implemented must exist that are not captured in the survey questions. These differences in process optimization would be reflected in divergent DEA ratings. In agreement with prior expectations, the DEA analysis showed large variations among the suppliers regarding relative efficiency. Efficiency ratings ranged from zero to one. (A look at the endpoints of the efficiency ratings' distribution showed four DEA = 0 suppliers and six DEA = 1 suppliers in the sample.) Partial results of the DEA analysis for individual suppliers are displayed in Table 6. With the efficiency ratings calculated, the remaining hypotheses could be tested. The interaction of process optimization and TQM implementation was represented with cross-product terms for each quality management practice and DEA. The following linear model was formulated for the expected relationship between APPM, TQM, and the interaction of process optimization and TQM implementation (hypothesis 2): APPMj = flo + fllTMj + fl2SMj + fl3Tj + fl4PDj + flsQDj + fl6QRj +/37PMj +/3sERj + fl9DEAj + flloDEAj *TMj + flllDEAj *SMj + flI2DEAj * Ts + fl,3DEAj *PDj + fl14DEAj *QDj +/315DEA j *QRj + flI6DEAj *PMj + fllTDEAj *ERj + ej

(5)

6.3.3. Hypothesis 3 Finally, the reduced model was tested to examine the potential moderating influence of a buyer's and supplier's economic dependence on each other. Supplier dependence (SUPD) was operationalized as the percent of business the common customer firm accounted for in each supplier's operation for the primary product or product line supplied to this customer. (This information was obtained from responding firms on the suppliers' survey form.) Customer dependence (CUSD) was operationalized as the percent of the common customer's purchases each supplier firm accounted for, for the primary product or product line procured. (This information was obtained through a survey administered to the customer firm's buyers.) These two measures of dependence

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were considered along with the TQM practices and interaction effects that were significantly related to quality performance (as determined in the testing of hypothesis 2). The following model was tested: APPMj =/30 +/3~SUPDj +/32CUSDj +/3i sig. TQM variablesj +/3i sig. interaction termsj + ~j

(6)

7. Results

7.1. The quality management-quality performance relationship Results from the regression analyses showed support for hypotheses 2 and 3 but not hypothesis 1. As shown in Table 8, none of the TQM practices were significantly related to quality performance at the 5% level when they were regressed without the interaction terms. The reduced model, testing HI, yielded a very low R 2 - 0.02 (RZadj= 0.00). This low R 2 contrasted sharply with the R 2 for the complete model (interaction terms included) 0.89 (R2adj= 0.88). (See Table 7.) Hypothesis 1, predicting a simple positive linear relationship between extent of TQM implementation and quality performance, was therefore not supported. To be sure that these results were not caused by multicollinearity among the independent variables, variance inflation factors (V1Fs) were calculated for each X variable in the analysis. All VIFs for the eight TQM practices were under 3, well below the borderline value of 10 that indicates possible undue influence on the least squares estimates (Neter et al., 1985). It does not appear that intercorrelation among the predictor variables biased the estimates (Table 8). Other potential causes of the lack of statistical significance include flaws in the construction of the items on the survey instrument. The Saraph et al. (1989) instrument sometimes proposed practices or questions that didn't match the scale managers were supposed to respond with. For example, TM 13 - 'Existence of a comprehensive quality plan in the firm' - really requires a ' y e s / n o ' response and yet managers are asked to evaluate this

Table 7 Results o f the linear regression analysis for quality performance Variable

B

Relative efficiency Interaction o f relative efficiency and SM

Beta 0.222 0.034

T

Sig. T

0.220 0.100

1.489 0.716

0.138 0.474

SM Interaction o f relative efficiency and QR QR Interaction of relative efficiency and T

- 5.276E - 04 - 0.014 0.012 0.212

- 0.002 - 0.043 0.052 0.619

- 0.018 - 0.289 0.405 4.871

0.986 0.773 0.686 0.000

T Interaction PD Interaction QD Interaction PM Interaction ER Interaction TM (Constant)

- 0.073 0.323 - 0.167 9 . 3 8 7 E - 04 0.118 - 0.149 0.083 0.119 - 0.039 - 0.168 0.099 - 0.102

- 0.344 1.023 - 0.779 0.003 0.413 - 0.486 0.352 0.351 - 0.167 - 0.572 0.402

- 2.466 9.483 - 6.985 0.018 3.283 - 3.279 2.864 2.081 - 1.052 - 2.758 2.481 - 0.955

0.014 0.000 0.000 0.986 0.001 0.001 0.004 0.038 0.294 0.006 0.014 0.340

of relative efficiency and PD of relative efficiency and QD o f relative efficiency and PM o f relative efficiency and ER of relative efficiency and T M

R 2 0.887 Adjusted R 2 0.879

F = 113.627 Signif. F = 0.000

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Table 8 Results of the linear regression of quality performance on TQM practice Variable

B

Beta

T

Sig. T

TM SM T PD QD QR PM ER (Constant)

0.017 0.021 -0.010 - 0.016 - 0.025 -0.027 0.027 0.013 0.946

0.071 0.079 -0.048 - 0.075 - 0.089 -0.116 0.113 0.057 12.040

0.690 0.911 -0.505 - 0.879 - 0.972 - 1.287 1.188 0.510 0.000

0.490 0.363 0.614 0.380 0.332 0.199 0.236 0.610

R2 0.022 Adjusted R 2 0.000

F = 0.710 Signif. F = 0.683

p r a c t i c e o n a ' V e r y H i g h - V e r y L o w ' scale. S u c h m i s m a t c h e s c a n a d d v a r i a n c e to s u r v e y r e s p o n s e s s i m p l y d u e to d i f f e r e n t i n t e r p r e t a t i o n s o f h o w the scale a n d i n s t r u m e n t i t e m s are s u p p o s e d to m a t c h . H o w e v e r , t h e r e d o n o t a p p e a r to b e e n o u g h o f t h e s e m i s m a t c h e s a m o n g s u r v e y i t e m s to c o n f o u n d a statistical r e l a t i o n s h i p b e t w e e n the dependent and independent variables.

7.2. The process optimization-quality management interaction and its effect on quality performance H y p o t h e s i s 2, w h i c h p r o p o s e d the r e l a t i o n s h i p b e t w e e n quality p e r f o r m a n c e , T Q M practices, a n d the i n t e r a c t i o n o f p r o c e s s o p t i m i z a t i o n a n d T Q M i m p l e m e n t a t i o n , w a s s t r o n g l y s u p p o r t e d b y the results o f the r e g r e s s i o n a n a l y s i s . ( S e e t h e c o m p l e t e m o d e l in T a b l e 7 a n d t h e r e d u c e d m o d e l in T a b l e 9.) O n e i n t e r a c t i o n t e r m w a s r e l a t e d to q u a l i t y p e r f o r m a n c e at t h e 5 % l e v e l ( D E A T M - the i n t e r a c t i o n o f s u p p l i e r e f f i c i e n c y a n d R o l e o f t o p m a n a g e m e n 0 a n d o n e T Q M p r a c t i c e w a s s i g n i f i c a n t at the 0.10 l e v e l ( T M - R o l e o f t o p m a n a g e m e n t ) . F o u r i n t e r a c t i o n t e r m s a n d f o u r T Q M v a r i a b l e s w e r e s i g n i f i c a n t at the 0.01 l e v e l ( D E A P D - the i n t e r a c t i o n o f supplier efficiency and Product/service design, DEAT - the interaction of supplier efficiency and Training, D E A P M - t h e i n t e r a c t i o n o f s u p p l i e r e f f i c i e n c y a n d P r o c e s s m a n a g e m e n t / o p e r a t i n g p r o c e d u r e s , D E A E R - the i n t e r a c t i o n o f s u p p l i e r e f f i c i e n c y a n d E m p l o y e e relations, Q D - R o l e o f the q u a l i t y d e p a r t m e n t , T - T r a i n i n g ,

Table 9 Results of the linear regression of quality performance on TQM practices, controlled for suppliers' structural differences Variable

B

Beta

T

Sig. T

Interaction of relative efficiency and PD PD Interaction of relative efficiency and PM PM Interaction of relative efficiency and T T Interaction of relative efficiency and TM TM Interaction of relative efficiency and ER QD Constant

0.339 - 0.173 - 0.125 0.075 0.232 - 0.084 - 0.095 0.051 0.059 0.121 0.065

1.074 - 0.803 - 0.408 0.319 0.678 - 0.398 - 0.323 0.208 0.175 0.424

10.996 - 7.720 - 3.004 2.797 6.274 - 3.351 - 2.236 1.805 4.340 12.225 1.857

0.000 0.000 0.003 0.006 0.000 0.00 l 0.026 0.072 0.000 0.000 0.064

R 2 0.881 Adjusted R z 0.876

F = 187.074 Signif. F = 0.000

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Table 10 Results of the linear regression of quality performance on supplier and customer dependence and TQM practices, controlled for suppliers' structural differences Variable

B

Beta

T

Sig T

Interaction of relative efficiency and PD PD Interaction of relative efficiency and PM PM Interaction of relative efficiency and T T Interaction of relative efficiency and TM TiM Interaction of relative efficiency and ER QD SUPD CUSD Constant

0.341 - 0.177 - 0.125 0.076 0.225 - 0.082 -0.091 0.054 0.061 0.122 - 0.036 0.016 0.056

1.081 - 0.823 - 0.408 0.322 0.659 - 0.387 -0.311 0.219 0.180 0.427 - 0.047 0.027

11.131 - 7.935 - 3.024 2.849 6.113 - 3.279 -2.168 1.904 4.483 12.382 - 2.028 1.262 1.857

0.000 0.000 0.003 0.005 0.000 0.001 0.031 0.058 0.000 0.000 0.044 0.208 0.064

R2 0.883 Adjusted R2 0.878

F = 172.387 Signif. F = 0.000

PM - Process m a n a g e m e n t / o p e r a t i n g procedures, and PD - P r o d u c t / s e r v i c e design). Supplier efficiency alone (DEA) was not significantly correlated with quality performance in the presence of the interaction terms.

7.3. The process management-quality performance relationship, moderated by customer-supplier bilateral dependence Hypothesis 3, testing the potential moderating influence o f a customer's and supplier's bilateral dependence (reflecting transaction-specific investments), was partially supported by the results o f the linear regression. (See Table 10.) Supplier dependence (SUPD) was negatively related to quality performance and was significant at the 0.05 level. Customer dependence (CUSD) was not significantly correlated with quality performance. When regressed with the significant variables in the reduced model (from testing hypothesis 2), R 2 rose from 0.881 to 0.883 (Ra2dj rose from 0.876 to 0.878). T Q M practices and T Q M / e f f i c i e n c y interaction terms that were significant in the reduced model remained significant in the presence of the b u y e r / s u p p l i e r dependence variables. To assure the accuracy o f the statistical results obtained, regression diagnostics were performed on the reduced models testing hypotheses 2 and 3. Normal probability plots o f the residuals from the reduced models (with the D E A / T Q M interaction terms included) indicated that the errors are normally distributed. (The residuals fell approximately on a diagonal straight line in the probability plots.) It can therefore be concluded that the residuals are ' w h i t e noise' (i.e., uncorrelated and normally distributed) and that the supplier responses come from a normal population.

7.4. Discussion The results from the hypothesis tests demonstrate that consideration of suppliers' variances in process optimization (as captured by each supplier's relative efficiency at implementing quality management practices) is crucial in understanding which practices are important for quality performance. The lack o f significance shown in the output from testing hypothesis 1 indicates that noise factors are so strong when T Q M practices are evaluated alone that the model c a n ' t detect the signal sent by the significant T Q M variables. Only when the

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Table 11 Results of the linear regressionof quality performanceon relativeefficiency Variable B Beta Relative efficiency 0.669 0.664 Constant 0.531 Re 0.441 Adjusted R2 0.439

T 14.378 18.631

Sig T 0.000 0.000

F = 206.733 Signif. F = 0.000

D E A / T Q M interaction terms are included in the model is noise in the regression analysis reduced enough to show which relevant parameters are significant. Given that DEA ratings are a function of the dependent variable, APPM, it might appear that APPM is being regressed on itself by including the DEA variable in the regression analysis. This is not so. DEA ratios represent nonlinear functions of the data that reveal information not disclosed by linear regression. If APPM were regressed on a linear dimension o f itself, the regression would have generated a perfect R 2 of l and no independent variable except DEA would have emerged significant. To confirm this conviction that additional information beyond that contained in the DEA variable is being captured, the quality performance variable (APPM) was regressed on DEA by itself. With no TQM variables in the model, R E fell from its value of 0.89 (in the complete model) to a value of 0.44. (See Table 11.) This is a significant difference that is directly attributable to the correlation between APPM and the TQM practices when suppliers' process optimization is considered in the analysis. Five TQM practices and five interaction terms were statistically significant, showing that quality management is related to quality performance when efficiency is examined together with extent of implementation. Two quality management practices - Training and Product/service design - were strongly positively correlated with quality performance for supplier firms that were especially efficient in their use of these practices. However, for firms that were inefficient or made poor use of these techniques, these same two practices had strong negative relationships to quality performance. For the inefficient firms, greater use of Training or Design of products and services resulted in even worse component quality output. Similarly, but with the opposite relationship, Role of top management and quality policy and Process management/operating procedures had strong negative relationships to quality performance in the highly efficient firms but strong positive relationships to performance in firms that were inefficient at involving their executives in quality policy and managing their production processes. The influence of the quality department was strongly (and positively) correlated with quality performance, regardless of a supplier's efficiency at using its quality control personnel. And suppliers who were effective at involving their employees in quality-related activities showed a strong positive relationship of this practice to quality performance. Employee relations was not significantly correlated with quality performance for suppliers who were inefficient at involving their employees in quality activities. Finally, when the transaction-specific investments made by a supplier with this particular customer firm were considered in the quality management-quality performance analysis, a supplier's economic dependence on this one customer was shown to have a significant negative correlation with the quality of the components it produced and supplied. This finding supports the theory of transaction cost economics (TCE) which predicts that asset specificity (transaction-specific investments) increases transaction costs, regardless of governance type. Poorer component quality from suppliers certainly magnifies transaction costs, due to the need for suppliers (or sometimes the buyer) to rework, retest, reinspect, scrap or replace defective components. The analysis did not support TCE's (and the third hypothesis's) assertion that bilateral dependence will result in 'market failure'; this customer's economic dependence on its suppliers was not significantly related to component quality performance. However, since market failure is embodied by suboptimal performance by either the buyer or the supplier on any of the dimensions by which they evaluate their exchange relationship, the negative correlation of

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the supplier's economic dependence to its component quality performance can be said to support the predictions of TCE theory.

8. Implications 8.1. Quality management It appears that the entire aerospace components industry has adopted TQM, although not to the same degree. Table 5 shows all mean implementation scores for the eight TQM practices to be above the midpoint value of 3 on the five-point scale. Among the eight practices, respondents indicated a heavy reliance on the quality department for assuring output quality. (Role of the quality department had the highest mean implementation rating [4.22], the smallest percentage of respondents indicating little or no extent of usage, and the highest average item-to-total correlation among the eight constructs.) Suppliers did little to develop their own supply bases, however, as a tool for managing quality. (Supplier quality management had the lowest mean implementation rating - 3.24.) Between one fourth and one third of the survey participants made little or no use of Supplier quality management, Process management/operating procedures, Quality data and reporting, Employee relations, and Training. Instead, respondents relied on their quality control personnel, top managers, and product designers to assure quality output. How well did these functional areas operate in assuring output quality? Results from the simple linear regression of APPM on the eight TQM practices suggest that none of these quality improvement activities directly affects quality performance. No individual practice was significantly related and almost none of the variability in the effectiveness data could be explained by the eight practices. (See Table 8.) This finding could be peculiar to the aerospace industry but other researchers have similarly found no direct relationship between process improvement activities and performance when surveying other industries (Adam, 1994; Forker et al., forthcoming). The lack of a direct relationship is all the more surprising when one considers the explicit conformance to industry-wide specifications and the high degree of product testing required of aerospace products by the Federal Aviation Administration. The lack of a direct relationship may account for some of the disappointments with TQM published in recent years in the business press. Simple execution of SPC, supplier certification, simplification of work processes, etc. may not be enough to reduce defectives.

8.2. Process management On the other hand, when process improvement was considered jointly with process optimization, several quality management practices showed a significant correlation with performance. The fact that efficient and inefficient implementation displayed different signs in their relationship to performance suggests that not all aerospace suppliers adopted TQM at the same time. Early adopters of quality management might be more efficient practitioners than late adopters due to the learning-by-doing they have already experienced. Fine (1986) showed that firms could reduce their quality control costs, even as they aimed for zero defects, by making use of their quality-based learning. Similarly, firms that have incorporated the benefits of learning into their quality management practices may reach a plateau of efficiency where some practices are strongly related to performance and other practices are no longer relevant. This plateau would explain why Training and Product design are strongly positively correlated with performance for efficient suppliers while Top management/quality policy and Process management/operating procedures are strongly negatively related. The efficient suppliers could be at a point where top management's involvement and organizational focus on automation, line balancing, and process design 'fool-proofing' actually contribute to more errors, rather than fewer. It's possible that top management's attention has moved on to other improvement programs (such as cost reduction or cycle time reduction) and that process management knowledge is being applied for other purposes. Training, Product

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design, and Employee relations are the crucial TQM practices at this advanced stage of efficiency for quality effectiveness. The inefficient suppliers may be late adopters of Training and Design for quality who are still learning (perhaps through trial and error) the most effective means of implementing these practices. Late adopters may be at a stage of development where concentration and guidance from top management is crucial for the reduction of defects. Similarly, application of line balancing, process automation, and process simplification could be imperative for TQM 'newcomers' to improve quality conformance. With attention focused on these zero defect 'qualifiers', the active involvement of employees in quality improvement programs and enhancement of quality awareness (i.e., Employee relations) may have no effect on performance at this early stage. One of the most interesting results was the universal importance of a supplier's quality department in assuring conformance, regardless of the firm's efficiency at using its quality control personnel. How much a supplier firm used its quality department was the most significant explanatory variable in accounting for performance variability. In contrast, the collection, display, use, and relevance of quality data in a component manufacturer's firm (i.e., Quality data and reporting) was not related at all to the number of defectives found. Similarly, implementation of quality management further up the supply chain (i.e., Supplier quality management) showed no relationship to quality performance, either on an interaction or stand-alone basis. These somewhat contradictory results may be due to the nature of the industry surveyed and the stage in the supply chain of the component manufacturers. Four of the seven product lines represented in the data consist of metal parts that are either heated and hammered into shape (Forgings), heated and shaped by pouring liquid metal into a mold (Castings), formed into a particular shape and joined with other metal sheets to make a wing, rudder, or propeller blade (Sheet metal/Airfoils), or cut and bent to make parts for larger sub-assemblies (General machining). These components rely on quality control personnel to check for conformance to specifications, yet they all use plain sheet metal as their primary material input. It's likely that many defectives found are simply scrapped, sent to a recycler to be melted down and reformed into new metal sheets. Data collected and reported may not have the same impact on quality performance if workers are not required to rework defects and to learn from previous mistakes. And since suppliers to these aerospace component manufacturers would be mostly sheet metal producers - makers of commodity products - there would be few supplier management practices the component producers could engage in that would significantly impact quality performance. The gatekeepers of outgoing quality - the quality department - would be the key players in assuring conformance to specifications in this industry setting. 8.3. Bilateral dependence

Analysis of the third hypothesis showed a supplier's economic dependence on this one aerospace customer to have a significant negative relationship to the quality of components it manufactured and supplied. Although the finding supports the predictions of TCE theory, it does appear to be counterintuitive. Subsequent conversations with the customer firm's procurement manager provided additional information that help explain this survey result. Due to significant sunk costs of time, training, and attention made by this customer firm with its suppliers in the development of specialized tooling for the customer's production needs, buyers in the customer firm were reluctant to switch suppliers, even if an existing supplier was performing poorly. In addition, years of bilateral relationships between suppliers and buyers led to friendships that buyers were reluctant to break by removing a poorly performing supplier from the supply base. Many of the suppliers at the time the survey was administered were small family-owned businesses located very close (same metropolitan area) to the customer firm. Often the majority of their business was accounted for by this one large aerospace company. The suppliers' physical proximity, specialized tooling, and friendships with purchasing personnel are all forms of asset specificity. These transaction-specific investments were leading to poorer component quality and higher transaction costs for the customer firm, above and beyond poor performance determined strictly by the suppliers' quality management practices.

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Why would a highly dependent supplier sell poor-quality components to its major customer? Conversations with a small sample of survey respondents revealed that quality was not the major supplier selection criterion of this customer finn (at least, this was the suppliers' perception). While the customer firm measured components' conformance to specifications, suppliers unanimously agreed that sourcing decisions were made primarily on price. The perceived lack of importance for quality as an order winner may explain the nonintuitive results of Table 10. If suppliers feel that orders will come regardless of their components' conformance, as long as they're the lowest bidder, they may have little motivation to improve quality. Scrapping or reworking defective products may be less expensive in the short run than reengineering internal processes.

9. Conclusion Investments in employees and innovation have been flagged as the necessary inputs for raising long-term productivity, expanding productive capacity, and maintaining market share (Roach, 1996). The results of this analysis of the aerospace components industry support that assertion while pointing to process improvement practices that can help firms boost short-term efficiency and, together with process optimization, improve conformance quality. Firms that are just embarking on process improvement initiatives may benefit most from analyzing, balancing, simplifying, and perhaps automating current production practices; at this early stage, the active involvement, attention, and direction of top management is crucial in assuring firm-wide TQM adoption. Process capability studies, by measuring each machine's ability to hold tolerances, can help pinpoint which machines are in greatest need of repairs, upgrades, or replacement. Organization of work tools, molds, jibs, and dies near work stations can also help in simplifying production processes by reducing the search-and-fetch time needed for these implements. As processes become more streamlined and capable, firms should invest their resources in product design (design innovation), training all employees in quality improvement concepts and techniques, and actively involving hourly workers in quality-related decisions. The latter requires the assignment of responsibility to employees for error-free output, feedback to workers on their quality performance, and recognition of workers who have met and exceeded quality goals. Operators' observations and ideas for process improvement should be actively sought and rewarded. As line workers take ownership for quality, the hands-on involvement of top management will no longer be needed and could even be detrimental. However, quality control personnel will still play an important role in assuring conformance, regardless of the short-term efficiency with which they're used. Future research in other industries can determine the generalizability of this study's results. There may be a pattern of relevant quality management practices that vary by industry, time of adoption, and degree of efficiency in implementation. Quality will remain a cornerstone of competitive advantage; reduction of the trial-and-error efforts toward zero defects will improve both efficiency and effectiveness for manufacturers of intermediate goods.

Acknowledgements This research was partially funded by the National Association of Purchasing Management through a Doctoral Dissertation Grant. The author would like to thank Jim Hershauer and David Mendez for their helpful comments on an earlier draft.

LB. Forker/Journal of Operations Management 15 (1997) 243-269

Appendix A. The survey instrument Abbreviation

Item from the survey instrument

Response scale

TM

Role of top management and quality policy

(Construct-N/A)

TMI

Assumption of responsibility for quality performance by the most senior executive responsible for profit and loss Acceptance of responsibility for quality by major department heads Importance of quality results in top management performance appraisals Support by top management of long-term quality improvement process Participation by department heads in the quality improvement process Degree to which objectives are set by top management for quality performance Specific quality goals set within the firm Extent of functional involvement (# of functions included) in the firm's quality goal-setting process Clear understanding by top management of quality goals and policy set within the firm Importance attached to quality by top management in relation to costs and schedules Review of quality issues in top management meetings Belief by top management that quality improvement is a way to increase profits Existence of a complete quality plan in the firm

NE LE SE ME GE

TM2 TM3 TM4 TM5 TM6 TM7 TM8

TM9 TM10 TMll TM12 TMI3

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

PD

Product/service design

(Construct-N/A)

PD1

Thorough new product/service design reviews before the product/service is produced and marketed Coordination among affected departments in the product/service development process Emphasis on quality of new products/services instead of cost or schedule objectives Unambiguous product specifications and procedures Consideration of implementation/producibility in the product/service design process

NE LE SE ME GE

PD2 PD3 PD4 PD5

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

265

266

L.B. Forker~Journal of Operations Management 15 (1997) 243-269

PD6

Emphasis on quality by sales, customer service, and PR personnel

NE LE SE ME GE

SM

Supplier quality management

(Construct-N/A)

SM1

Extent to which you select suppliers based on quality rather than price or schedule Thoroughness of your supplier rating system Reliance by you on a few dependable suppliers Amount of education you provide to your suppliers Extent of technical assistance you provide your suppliers Involvement of suppliers in your product development process Extension of long-term contracts to your suppliers Clarity of specifications provided to your suppliers

NE LE SE ME GE

SM2 SM3 SM4 SM5 SM6 SM7 SM8

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

QD

Role of the quality department

(Construct-N/A)

QDI QD2

Visibility of the quality department in the finn Degree of access the quality department has to top management Degree of decision-making influence of the quality department Coordination between the quality department and other departments Effectiveness of the quality department at influencing quality improvement

NE LE SE ME GE NE LE SE ME GE

PM

Process management/operating procedures

(Construct-N/A)

PM1 PM2 PM3 PM4 PM5

Use of preventive equipment maintenance Automation of inspection, review, checking of work Evenness of production schedules/work distribution Automation of processes 'Fool-proofing' of process design to minimize chances of employee errors Unambiguous work or process instructions given to employees

QD3 QD4 QD5

PM6

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

NE NE NE NE

LE LE LE LE

SE SE SE SE

ME ME ME ME

GE GE GE GE

NE LE SE ME GE

QR

Quality data and reporting

(Construct-N/A)

QR1 QR2

Data available on quality costs in your firm Data available on quality shortfalls (error rates, defect rates, scrap, number of defects) Timeliness of the quality data

NE LE SE ME GE NE LE SE ME GE

QR3

NE LE SE ME GE

LB. Forker/Journal of Operations Management 15 (1997) 243-269

Use of quality data as tools to manage quality Relevance of the quality data made available to hourly employees Relevance of the quality data made available to managers and supervisors Use of quality data to evaluate supervisor and managerial performance Display of quality data, control charts, etc. at employee work stations

NE LE SE ME GE NE LE SE ME GE

ER

Employee relations

(Construct-N/A)

ER1

Implementation of quality-related employee involvement programs Effectiveness of implemented quality-related employee involvement programs Responsibility assigned to employees for error-free output Feedback provided to employees on their quality performance Participation in quality decisions by nonsupervisory employees Continual enhancement of quality awareness among employees Recognition of employees for superior quality performance Extent of supervisors' effectiveness in solving quality problems

NE LE SE ME GE

QR4 QR5 QR6 QR7 QR8

ER2 ER3 ER4 ER5 ER6 ER7 ER8

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

T

Training

(Construct-N/A)

T1

Specific work-skills training (technical and vocational) given to hourly employees in the firm Quality-related training given to hourly employees throughout the firm Quality-related training given to managers and supervisors throughout the firm Firm-wide training in the total quality concept (philosophy of company-wide responsibility for quality) Firm-wide training in basic statistical techniques (e.g., histograms and control charts) Firm-wide training in advanced statistical techniques (e.g., design of experiments and regression analysis)

NE LE SE ME GE

T2 T3 T4

T5 T6

NE LE SE ME GE NE LE SE ME GE NE LE SE ME GE

NE LE SE ME GE NE LE SE ME GE

267

268

L.B. Forker~Journal of Operations Management 15 (1997) 243-269

T7

Commitment of top management to employee training

NE LE SE ME GE

T8

Available resources for employee training

NE LE SE ME GE

SUPD

% of a supplier's business this customer firm accounts for

0-100%

CUSD

% of customer's purchases (for relevant product) that are bought from this supplier

0-100%

Scale: NE - No Extent; LE - Little Extent; SE - Some Extent; ME - Much Extent; GE - Great Extent.

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