Much has been written about how quality should be managed in an organization. The quality literature contains many case studies of successful companies and descriptions of quality concepts and quality improvement programs. To date, however, there has been no systematic attempt to organize and synthesize the various prescriptions offered, nor have measures of organizational quality management been proposed for areas such as top management leadership, training, employee involvement, and supplier management. While many organizations collect quality data such as defect rates, error rates, rework cost, and scrap cost, these are not measures of organization-wide quality management.This paper provides a synthesis of the quality literature by identifying eight critical factors (areas) of quality management in a business unit. Operational measures of these factors are developed using data collected from 162 general managers and quality managers of 89 divisions of 20 companies. The measures can be used individually or in concert to produce a profile of organization-wide quality practices. The measures are found to be both valid and reliable. Such measures could be used by decision makers in an organization to assess the status of quality management in order to direct improvements in the quality area. Researchers can use such measures to better understand quality management practice and to build theories and models that relate the critical factors of quality management to the organization's quality environment and quality performance.

Research on quality incorporates a range of concerns, including quality definition and management, and such specific mechanisms as statistical quality control (SQC). However, though research in statistical quality control has evolved in a scientific and rigorous fashion, based on the early works of Shewhart, Juran, Deming and others, the study of other aspects of quality, particularly quality management, has not evolved in a similarly rigorous fashion. Theory development and measurement issues related to reliability and validity are particularly weak in the quality management literature. Starting from a strategic perspective of the organization, this paper identifies and substantiates the key dimensions of quality management, then tests the measurement of those dimensions for reliability and validity. In doing so, it establishes a clear framework for subsequent research and for evaluation of quality management programs by practitioners. In order to specify the important dimensions of quality management, a thorough search of the relevant literature was undertaken. Quality management is defined as an approach to achieving and sustaining high quality output; thus, we employ a process definition, emphasizing inputs (management practices) rather than outputs (quality performance) in our analysis. Quality management is first viewed as an element of the integrated approach known as World Class Manufacturing; quality management supports and is supported by JIT, human resources management, top management support, technology management and strategic management. The key dimensions of quality management are then articulated. Top management support creates an environment in which quality management activities are rewarded. These activities are related to quality information systems, process management, product design, work force management, supplier involvement and customer involvement. They are used in concert to support the continuous improvement of manufacturing capability. As manufacturing capability and quality performance improve, a plant achieves and sustains a competitive advantage. This, in turn, provides feedback, reinforcement and resources to top management, which stimulates continuous improvement. Based on the seven dimensions of quality management identified in this paper, a set of 14 perceptual scales was developed. The scales were assessed for reliability and validity with a sample of 716 respondents at 42 plants in the U.S. in the transportation components, electronics and machinery industries. Reliability is broadly defined as the degree to which scales are free from error and, therefore, consistent. The use of reliable scales provides assurance that the obtained results will be stable. Application of Cronbach's alpha both across the board and by industry and nationality subsamples refined the original group of 14 scales to 11 internally consistent scales. Validity refers to the degree to which scales truly measure the constructs which they are intended to measure. This provides academic and industr...

As decision makers become more involved in implementing Total Quality Management, questions are raised about which management practices should be emphasized. In this exploratory investigation of the relationship of specific quality management practices to quality performance, a framework was constructed. It focuses on both core quality management practices and on the infrastructue that creates an environment supportive of their use. In addition, it incorporates two measures of quality performance and their role in establishing and sustaining a competitive advantage.Path analysis was used to test the proposed model, with multiple regression analysis determining the path coefficients, which were decomposed into their various effects. Weak linkages were eliminated. The trimmed model indicated that perceived quality market outcomes were primarily related to statistical controllfeedback and the product design process, while the internal measure of percent that passed final inspection without requiring rework was strongly related to process flow management and to statistical controllfeedback, to a lesser extent. Both measures of quality performance were related to competitive advantage. Important infrastructure components included top management support and workforce management. Supplier relationships and work attitudes were also related to some of the core quality practices and quality performance measures. The results were interpreted in light of Hill's concept of order winners and order qualifiers and Garvin's eight dimensions of quality. They indicate that different core quality management practices lead to success in different dimensions of quality, and that those dimensions function differently as order winners and order qualifiers.

This paper discusses the need for more research in operations management which is based on data from the real world. Tying operations management theory in with practice has been called for over a long period of time, however, many P/OM researchers do not have a strong foundation in gathering and using empirical data. This paper provides a starting point that encourages operations management researchers to use empirical data and provides a systematic approach for conducting empirical studies. Empirical research can be used to document the state of the art in operations management, as well as to provide a baseline for longitudinal studies. It can also be invaluable in the development of parameters and distributions for mathematical and simulation modeling studies. A very important use for empirical data is in theory building and verification, topics which are virtually ignored in most P/OM research. Operations management researchers may be reluctant to undertake empirical research, due to its cost, both in dollars and time and the relative risk involved. Because empirical research may be considered “soft,” compared with mathematical modeling, it may be perceived as risky. This paper attempts to provide a foundation of knowledge about empirical research, in order to minimize the risks to researchers. It also provides a discussion of analytical techniques and examples of extremely rigorous empirical P/OM research. Although operations management researchers may not recognize it, all research is based on theory. The initial step in conducting empirical research deals with articulating the theoretical foundation for the study. It also includes determining whether the problem under investigation involves theory building or theory verification. In the second step, a research design should be selected. Although surveys are fairly common in empirical P/OM research, a number of other designs, including single and multiple case studies, panel studies and focus groups, may also be used, depending on the problem being studied. Third, a data collection method should be selected. One method, or a combination of several data collection methods, should be used in conjunction with the research design. These include historical archive analysis, participant observation, outside observation, interviews, questionnaires and content analysis. The implementation stage involves actually gathering the data. This section of the paper focuses on using questionnaires as the method of data analysis, although some of the concepts discussed may be applicable to other data collection methods, as well. A brief overview of data analysis methods is given, along with documentation of the types of data analysis which have been used in various types of empirical research conducted by operations management researchers over the past ten years. Potential outlets for publication of empirical P/OM research are discussed and their history of publishing such research is documented. Underlying every step of the process are considerations of reliability ...

Research on Total Quality Management (TQM), Just-in-Time (JIT) and Total Productive Maintenance (TPM) generally investigates the implementation and impact of these manufacturing programs in isolation. However, many researchers believe and argue conceptually the value of understanding the joint implementation and effect of manufacturing programs. This study investigates the practices of the three programs simultaneously. We find that there is evidence supporting the compatibility of the practices in these programs and that manufacturing performance is associated with the level of implementation of both socially-and technically-oriented practices of the three programs.