Barbara B. Flynn

A FRAMEWORK FOR QUALITY MANAGEMENT RESEARCH AND AN ASSOCIATED MEASUREMENT INSTRUMENT

Abstract 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 Cronbachs 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 industry users with confidence that the scales measure important constructs which are related to independent measures of the same constructs, and that each scale measures a single construct. It was concluded that the scales, and the instrument as a whole, are valid measures of quality management practices. Thus, the scales may be used with confidence by both researchers and industry users to measure quality management practices, with the ability to generalize beyond the immediate sample. This paper makes several important contributions to the area of quality management. It proposes an emergent theory of quality management and links it to the literature. Because the proposed scales are reliable and valid, they may be used by other researchers for hypothesis testing and by practitioners for assessing quality management practices in their plants and for internal and external benchmarking. Finally, the paper provides a step-by-step approach and criteria for conducting reliability and validity analysis of a measurement instrument.

Empirical research methods in operations management

Abstract 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 and validity. Conducting empirical research without considering its reliability and validity is pointless, because the researcher will not be able to generalize from the results. This should be considered in each of the four stages listed in the approach described above. A number of conclusions are discussed. These include the need for more empirical research and the need for P/OM researchers to become more critical readers of the empirical research done by others. Colleagues in the social sciences can be a valuable source of information about conducting empirical research. Industry contacts can be useful, as well, in pilot testing, finding industry sites and determining consensus on the definition of terms. Finally, researchers in operations management need to be more aware of the theory which underlies their work. Empirical research can be highly useful in both theory building and theory verification.

Relationship Between JIT and TQM: Practices and Performance

We propose that the use of total quality management (TQM) practices wilt improve just-in-time (JIT) performance through process variance reduction and reduced rework time and that JIT practices wil...

Further evidence on the validity of the theoretical models underlying the Baldrige criteria

Abstract The Baldrige framework has emerged as both a guide for quality management and the model upon which numerous state and international quality awards are based. It was introduced in 1988 as the foundation for the Malcolm Baldrige National Quality Award. The framework was significantly revised in 1992 and 1997. In order to test the validity of the theoretical model underlying the Baldrige framework as it has evolved over the years, we take the approach of analyzing the constructs upon which the Baldrige categories are based. Path analysis is used to test the fit of each of the three major frameworks, and the sums of direct effects are used to estimate the category weights implied by each of the path models. We found that all three frameworks were a good fit with the Baldrige frameworks for those years, and that both the 1992 and 1997 frameworks improved upon the foundation established by the 1988 framework. Thus, we conclude that appropriate adaptations to the Baldrige framework have been made over the years. We describe the implications for practitioners, in terms of critical success factors, and make recommendations for further minor modifications to the Baldrige framework.

Synergies between supply chain management and quality management: emerging implications

This paper examines the potential that quality management offers for improving supply chain management performance. Based on the theoretical and descriptive literature, four themes related to this topic are extracted. These are related to the pursuit of supply chain and quality goals simultaneously, leading to the development of cumulative capabilities, the relationship between quality management practices and supply chain performance measures and the relationship between a specific set of quality management practices known as co-makership and supply chain performance measures. Hypotheses were developed and tested using an existing database of information from 164 plants in the machinery, electronics and transportation components industries in the USA, Germany, Italy, Japan and England. There was strong support for all four hypotheses, indicating that there is a relationship between quality management and supply chain management. Practical implications and guidelines for managers focus upon leveraging this relationship as a competitive weapon in the increasingly complex global environment.

Decision Sciences Research in China: A Critical Review and Research Agenda—Foundations and Overview*

This article focuses on decision sciences research in China, providing an overview of current research and developing a foundation for future China-based research. China provides a unique research opportunity for decision sciences researchers, owing to its recent history, rapid economic development, and strong national culture. We examine recent economic reforms and their impact on the development of research questions in the decision sciences, as well as discuss characteristics of the diverse regions in China and their potential as sites for various types of research. We provide a brief overview of recent China-based research on decision sciences issues relating to national culture, supply chain management, quality management, production planning and control, operations strategy, and new product development and discuss some of the unique methodological challenges inherent in China-based research. We conclude by looking forward to emerging research opportunities in China.

A simulation comparison of group technology with traditional job shop manufacturing

A simulation model of an actual job shop was used to compare group technology with traditional job shop manufacturing. The experiment compared shops which had four different layouts, designed to emphasize different features of traditional job shops and group technology shops, and four distributions of demand for end items. The group technology shops exhibited superior performance in terms of average move time and average set-up time. The traditional job shops had superior performance in queue related variables (average queue length, average waiting time, work-in-process inventory, etc.). This was caused by group technologys dedication of machines. The effects of the queue related variables outweighed the effects of average move time and average set-up time: the average flow time was shorter in the traditional job shop than in the group technology shops.

Decision Sciences Research in China: Current Status, Opportunities, and Propositions for Research in Supply Chain Management, Logistics, and Quality Management*

As China becomes increasingly important to the global economy, it is critical to conduct high-quality research on important decision sciences issues there. This article provides an extensive review and critique of the extant China-based literature on supply chain management, logistics, and quality management, based on the foundation established by Zhao, Flynn, and Roth (2006). In general, decision sciences research in China is in its infancy. Although there have been some very interesting and well-executed articles, the majority are descriptive and focus on status updates. We provide a set of propositions to guide future research in logistics, supply chain management, and quality management in China, as well as guidelines for dealing with some of the unique challenges of conducting empirical research in China.

Operational Capabilities: The Secret Ingredient

We develop a theoretical definition of operational capabilities, based on the strategic management and operations management literature, and differentiate this construct from the related constructs of resources and operational practices, drawing upon the resource-based view of the firm as our foundation. We illustrate the key features of operational capabilities using the illustration of a restaurant kitchen. Because the traits of operational capabilities are distinct, they create a barrier to imitation, making them a potential source of competitive advantage. However, operational capabilities are particularly challenging to measure, because they emerge gradually and are tacit, embedded, and manifested differently across firms. In solving this measurement conundrum, we draw upon similar situations experienced by Schein (2004) and Eisenhardt and Martin (2000) in operationalizing organizational culture and dynamic capabilities. A taxonomy of six emergent operational capabilities is developed: operational improvement, operational innovation, operational customization, operational cooperation, operational responsiveness, and operational reconfiguration. A set of measurement scales is developed, in order to measure each of the operational capabilities, and validated using two different datasets. This allows replication of the psychometric properties of the multi-item scales and helps to ensure the validity of the resulting measures.

Repetitive lots: The use of a sequence-dependent set-up time scheduling procedure in group technology and traditional shops

Abstract There has been a great deal of recent interest in group technology as a scheme for parts grouping, machine dedication, and shop arrangement, which offers many potential benefits for traditional job shops. Among the benefits cited are a reduction in material handling and set-up times, less work-in-process (WIP) inventory, and shorter flow times. On the other hand, a number of authors claim that many of the advantages believed to be associated with group technology will not occur in practice, due to the inflexibility of machine dedication in group technology. This study investigated a procedure designed to improve performance in group technology shops. The repetitive lots (RL) scheduling procedure capitalizes on the sequence dependency of set-up times in shops. This procedure scans a queue of waiting jobs, seeking to find a job identical to the job that was just processed on a machine, which should eliminate the need for a machine setup. The truncated repetitive lots (TRL) procedure prevents lots from becoming excessively large by allowing no more than K jobs to be combined. In this study, K was set at five. Computer simulation was used to compare a shop configured as a group technology shop with the same shop configured as a traditional job shop and as a hybrid, which combined features of both group technology and traditional job shops. The group technology shop model had previously been demonstrated to exhibit performance that was inferior to the traditional job shop. Data were gathered on nine variables of interest in alternate simulated years for a period of twenty years, following a seven year start-up period. The study was designed to investigate two research questions: 1. Will the use of RL scheduling procedures improve performance in group technology shops? 2. Will the use of RL scheduling procedure cause the performance of the group technology shops to be indistinguishable from (or superior to) the performance of traditional job shops? The use of RL scheduling procedures clearly led to an improvement over first-come, first-served performance by all shops. This improvement was greatest in the group technology shops (those with dedicated machines). Their machine dedication caused a lower variance of parts types in queues, leading to more opportunities for the combination of lots by the RL scheduling procedures. No substantial differences in performance were found between the RL and TRL procedures; this was probably due to a combination of relatively high K values and relatively low utilization rates. When the three shop environments were compared, using only the RL procedures, major differences among shop environments were found. The shops with dedicated machines exhibited superior performance in terms of set-up time, machine utilization, and production lot size. However, the traditional job shops showed superior performance in the queue-related variables: queue length, waiting time, and WIP inventory. These effects combined to lead to an average flow time lower in the traditional job shop than in the shops with dedicated machines. These findings were consistent with the findings from previous studies where repetitive lots procedures were not considered. Although the use of repetitive lots scheduling procedures caused substantial improvement in the performance of the group technology shops, the improvement was not substantial enough to make group technology a viable alternative to the traditional job shop, at least for this shop. Future research in this area should concentrate on the K-utilization relationship in the TRL procedure, working with other sequence-dependent set-up time scheduling procedures and the interaction of repetitive lots procedures with alternate routing.