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 ...

This paper examines manufacturing strategy from the perspective of the resource‐based view of the firm. It explores the role of resources and capabilities in manufacturing plants that cannot be easily duplicated, and for which ready substitutes are not available. Such resources and capabilities are formed by employees' internal learning based on cross‐training and suggestion systems, external learning from customers and suppliers, and proprietary processes and equipment developed by the firm. Based on data from 164 manufacturing plants, the paper empirically demonstrates that competitive advantage in manufacturing (as measured by superior plant performance) results from proprietary processes and equipment which, in turn, is driven by external and internal learning. The implication is that resources such as standard equipment and employees with generic skills obtainable in factor markets are not as effective in achieving high levels of plant performance, since they are freely available to competitors. The paper also demonstrates the important role of internal and external learning in developing resources that are imperfectly imitable and difficult to duplicate. Copyright © 2002 John Wiley & Sons, Ltd.

This paper proposes a relationship between manufacturing strategy and organizational culture, based on an examination of the research literature. Survey data were collected from 822 respondents in 41 plants in the transportation, electronics, and machinery industries in the U.S. These plants included random samples of U.S.-owned and Japanese-owned manufacturers in the U.S., and manufacturers reputed to use advanced manufacturing practices. Analysis indicates that manufacturing strategy and organizational culture are related, and that a manufacturer with a well-aligned and implemented manufacturing strategy exhibits a collectivist or group-oriented organizational culture with coordinated decision making, decentralized authority, and a loyal work force.organizational culture, manufacturing strategy

The term "world-class manufacturing" was first used by Hayes and Wheelwright (1985) to describe organizations which achieved a global competitive advantage through use of their manufacturing capabilities as a strategic weapon. They cite a number of critical practices, including development of the workforce, developing a technically competent management group, competing through quality, stimulating worker participation and investing in state-of-the art equipment and facilities. Schonberger (1986) developed these concepts and provided a number of examples of world-class manufacturers located in the USA. He focused on continuous improvement, adding the development of supplier relationships, product design and JIT to the practices cited by Hayes and Wheelwright.Since Hayes and Wheelwright and Schonberger's seminal work, a number of authors have expanded and tested their definitions. For example, Gunn (1987) provides a strong emphasis on the role of technology in world-class manufacturing, while Hall (1983) stresses that world-class manufacturing is a fundamentally different way of operating an organization, rather than a set of techniques. Giffi, Roth and Seal (1990) view quality and the customer as the primary focus of world-class manufacturing, supported by a combination of manufacturing strategy and capabilities, management approaches, organizational factors, human assets, technology and performance measurement.This paper describes the World Class Manufacturing (WCM) Project, which has developed a large database to test some of the relationships described as comprising world-class manufacturing. It is based on the early work in the area,

Few studies have moved beyond the dyadic level of an ongoing alliance and examined factors contributing to the success of entering a series of alliances. In this paper we expect biotechnology firms over time to learn from their alliance experience and to develop general alliance capabilities. Specifically, we expect the speed with which they enter into new research alliances, e.g. their alliance formation rate, to be affected by capabilities built up in prior alliances as well as by characteristics of their partners. We use longitudinal event history data for the complete population of US biotechnology firms for 1973-1999 to test four hypotheses about factors affecting the rate of new alliance formation. Our analysis suggests that the speed of entering research alliances is affected by prior experience of the focal firm, but not by partner characteristics. Our findings provide evidence that biotech firms learn how to learn more effectively from multiple research alliances; however, this effect is generalized and not tied to specific characteristics of the alliance partner.