Organizational and strategic predictors of manufacturing technology implementation success: an exploratory study
Introduction
The traditional paradigm of operations management and manufacturing strategy holds that efficiency is possible only through the production of large volumes of standard products, while customization is necessarily penalized with higher costs. Advanced manufacturing technology (AMT) directly contradicts traditional thinking by promising the capability of providing both efficiency and flexibility. In particular, we define AMT as a group of computer-based technologies, including computer-aided design (CAD), robotics, group technology, flexible manufacturing systems, automated materials handling systems, computer numerically controlled machine tools, and bar-coding or other automated identification techniques (Sambasivarao and Deshmukh, 1995, Zairi, 1992, Zammuto and O'Connor, 1992).
Clearly, the most distinguishing feature of AMT is its capability to provide a combination of flexibility and efficiency. While these operational benefits are extremely important, they may generally be seen as a means to the ultimate end of financial benefits, namely improved profitability, market share, and sales growth. This paper therefore explores relationships associated with these competitive outcomes as well.
Successful implementation of AMT often requires different types of organizations and/or management practices than are found in more traditional environments. This is because these technologies often directly challenge established norms and strategic options considered in a pre-AMT facility. Because these technologies are quite different from the equipment they may be replacing, the culture of the adopting organization itself may ultimately affect the level of success managers have with the technology. A firm whose organizational culture is characterized by flexibility-orientated values may be more likely to be effective in implementing AMT than one that is not (Zammuto and O'Connor, 1992). Prior research has recognized a link between organizational culture and operations strategy (Bates et al., 1996), so it stands to reason that a firm's operations strategy may also be a factor in implementation success. A firm whose strategy emphasizes operational flexibility might be expected to be more effective in implementing manufacturing technology than a firm emphasizing other competitive priorities.
Our paper focuses on how organizational culture and operations strategy relate to operational and competitive outcomes in AMT implementation. The remainder of the paper is organized as follows. The second section examines the literature relating to AMT implementation, organizational culture, operations strategy, and operational and competitive benefits. The next section discusses our methodological approach and sample. We then present our findings and end with a discussion of the contribution of this research to our understanding of AMT implementation and how it may be relevant to practicing managers.
Section snippets
Conceptual framework
While it often represents a radical change from their predecessors on the shop floor, advanced manufacturing technology is widely used in many companies. Its rise in popularity has been accompanied by questions regarding its effective implementation. In this section, we outline the conceptual basis for our study. Based on an examination of the literature on organizational culture and operations strategy, we consider how these constructs might be expected to affect AMT implementation outcomes.
Data collection
A mail questionnaire, based on several areas in the literature, was sent to 470 plant managers and vice-presidents of manufacturing. In cooperation with the American Production and Inventory Control Society (APICS), six industries were selected to increase the generalizability of our findings; automotive, electrical, plastics, textiles, metal fabrication, and furniture. Out of the original 470 questionnaires, 97 responses were received, for a response rate of 20.6%. This response rate is
Findings
We employed regression analysis to test our hypotheses. The analysis consisted of estimating two regression models. The results are shown in Table 7. As discussed above, the first uses OUT_OPER as the dependent variable, and in the second, OUT_COMP is the dependent variable. To ensure that there were no differences across industries, we also estimated models including a set of dummy variables corresponding to the industries listed above. None of the industry dummy variables was significant, and
Implications and conclusions
The findings of our analysis present some interesting implications. First, there are certain orientations of organizational culture that are likely to lead to positive results in implementing advanced manufacturing technology, and there are other orientations that are likely to lead to negative results. Contrary to our expectations, for operational outcomes, the cultural dimension that proved to be important was the internal/external orientation, rather than the flexibility/control orientation.
Gregory N. Stock is Assistant Professor in the College of Business at Northern Illinois University. His research has focused on technology and supply chain management. His recent articles have examined technology transfer, manufacturing technology implementation, product development, and new organizational approaches to supply chain management and have been published in journals such as IEEE Transactions on Engineering Management, the Journal of Operations Management, the Journal of High
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Gregory N. Stock is Assistant Professor in the College of Business at Northern Illinois University. His research has focused on technology and supply chain management. His recent articles have examined technology transfer, manufacturing technology implementation, product development, and new organizational approaches to supply chain management and have been published in journals such as IEEE Transactions on Engineering Management, the Journal of Operations Management, the Journal of High Technology Management Research, Production and Inventory Management Journal, and the International Journal of Operations and Production Management. Prior to beginning his academic career, Dr Stock spent several years in industry as a design engineer in high technology industries such as computer graphics and data communications. He has B.S. and M.S. degrees in electrical enginering from Duke University and the Ph.D. degree in operations management from the University of North Carolina. Dr Stock has taught undergraduate and gradate courses in operations management, supply chain management, and technology management at a variety of institutions, including Northern Illinois University, Arizona State University, and the China-Europe International Business School.
Christopher M. McDermott is Associate Professor in the Lally School of Management at Renssealer, where he teaches operations/technology management, new product development, and strategy at the Masters, Ph.D., and Executive levels. Dr McDermott's courses also engage corporate clients such as General Motors, Ford, IBM, Hewlett-Packard, and General Electric through Renssealer's satellite distance education program. His research on the above and other topics is based on his ongoing interactions with numerous organizations, as both a researcher and consultant. He has a B.S. in Engineering from Duke University and his Ph.D. in Business from the University of North Carolina. His work experience includes positions at Westinghouse Electric Company and at Fairchild, where he was an on-site contractor at NASA's Goddard Space Flight Center. His research has been published in journals such as Production and Inventory Management Journal, The Journal of Operations Management, IEEE Transactions on Engineering Management, Business Horizons, and the Journal of High Technology Management Research. He is co-author of a book on the management of radical innovation, Radical Innovation: How Mature Companies Can Outsmart Upstarts.