استراتژی تولید عمومی و عملکرد کارخانه

This study examines the effects of the fit between generic manufacturing strategies (GMS) and manufacturing objectives upon strategically relevant plant level performance outcomes (e.g. cost-efficiency, quality, delivery, flexibility, and innovation). The proposition that plants with generic manufacturing strategies that are consistent (fit) with operational objectives will experience relatively higher levels of performance than others is tested using data from multiple countries and industries. A simultaneous estimation analysis revealed significant relationships between generic manufacturing strategies and plant performance, when accounting for operational objectives and while controlling for country, industry, and size effects.

Generic business strategies at the organizational level have been studied extensively in the strategic management literature (Homburg et al., 1999, Lassar and Kerr, 1996, Marlin et al., 1994, McGee and Thomas, 1986 and Miller and Dess, 1993). Generic business strategies are “common patterns of competition” that “generate competitive advantages across a variety of industries” (Kotha and Orne, 1989). More recently, generic strategies have also been examined at the product level (Nayyar, 1993). A number of scholars have suggested that the manufacturing function can be a source of competitive advantage to the firm (Hayes and Wheelwright, 1984, Hill, 1989, Miller and Rogers, 1956, Skinner, 1969 and Skinner, 1978). However, empirical validation studies of generic strategies that focus on the functional level of manufacturing are relatively rare, even though, “generic strategies … remain useful” (Ward and Duray, 2000). Some studies have examined the relationship between strategic operations intentions, deliberate strategy, and firm performance (e.g. Miller and Roth, 1994). Citing Mintzberg (1977), Kotha (1993), distinguishes between “intended strategy” (what the firm intended to do), “unrealized strategy” (intended actions that do not occur), “deliberate strategy” (actions the firm takes as a function of what it intended to do), “emergent strategy” (actions that were never intended at the outset, but nevertheless, get incorporated into the final outcome), and “realized strategies” (the pattern of actions observed by the researcher). However, few studies have reported analyses of generic manufacturing strategies (GMS) and plant-level performance outcomes (Bozarth and McDermott, 1998 and Ward and Duray, 2000). Miller and Roth (1994) observed that the definition of a manufacturing strategy includes two core elements. The first element is represented by the “task” of manufacturing. The manufacturing task identifies the purpose or mission of manufacturing and includes the objectives that must be accomplished by manufacturing (Skinner, 1978). The other key element in the definition of a manufacturing strategy is the “pattern of choices” that the manufacturing function makes over time (Miller and Roth, 1994). It is generally accepted in the manufacturing strategy literature that this “pattern of choices” should support or be consistent (fit) with the manufacturing “task”. Miller and Roth (1994, p. 286) succinctly state this important presumption: The demand that manufacturing choices and manufacturing tasks be linked follows from the presumption that good designs (such as those specified by the manufacturing choices) meet appropriate design criteria (as defined by the manufacturing task). However, as a general rule, theoretical and empirical studies in the manufacturing strategy literature tend to accept as given that the manufacturing task and the “pattern of choices” are consistent (mutually supportive), without explicitly empirically examining whether or not this is the case. This study seeks to fill a gap in the literature, by empirically examining whether a fit between intended manufacturing strategy (as evidenced in existing manufacturing structures at the plant level) and realized manufacturing strategy (also at the plant level) is predictive of strategically relevant manufacturing performance outcomes at the plant level. In the following section, we discuss relevant literature on the topic of generic manufacturing strategies and justify the theoretical framework that is used. Next, we elaborate on the theoretical framework and state our research proposition. Then, we present the empirical methods applied, and the data analysis that was performed, including the results of our analyses. We conclude by discussing the results of our study and their implications for theory and practice.

This study confirms an important, but untested presumption that underlies virtually all manufacturing strategy typologies in the literature—the link between intended strategy (objectives) and realized strategy (manufacturing structure) and its effect upon manufacturing performance outcomes. However, this presumption should be explicitly tested for other typologies, since the results reported herein are based upon the GMS typology and therefore, they should not be generalized beyond the scope of the GMS framework. The results reported herein indicate that the GMS framework has predictive ability at the plant level. Since the GMS framework has not been substantially validated in the literature (Bozarth and McDermott, 1998 and Devaraj et al., 2001), studies such as this, which demonstrate the empirical validity of aspects of the framework are useful (and necessary) for the advancement of knowledge in operations management (Swamidass, 1991). In addition, we have extended the application of the GMS framework to the plant level of analysis. The GMS was not specifically designed for use at the plant level, but this study clearly demonstrates that it does have some predictive ability at that level. Initially, we were surprised that the results for cost-oriented objectives and strategies were not as strong as those for differentiation oriented. One plausible explanation lies in the results obtained from studies of Porter’s cost–differentiation model in the business strategy literature. Scholars have argued, and generally observed that only a single firm in an industry can obtain or maintain cost leadership. If this is the case at the plant level as well, then with a limited number of industries and countries in the study, a very small number of plants would actually be able to excel in cost leadership in a sample such as this. Statistically, this might appear as a weak relationship of the fit between objectives and strategy with performance, particularly if more than one plant was pursuing a cost-orientation. In our sample, we observed that several, but not large numbers of plants were attempting cost leadership. The results reported are generally statistically significant; however, GMS did not explain large amounts of variance in manufacturing performance. This indicates that other factors exist that could help explain manufacturing performance, beyond that explained by GMS. One potentially important factor is that the theory was developed for the SBU level of analysis, but applied at the plant level of analysis. It is possible, that greater variance may be explained at the SBU level using this same approach. However, many other variables have been shown to predict a portion of manufacturing performance, such as manufacturing practices, organizational structure, etc. Future studies might consider how GMS is related to other variables believed to affect manufacturing performance. Prior studies of manufacturing typologies are relatively few. We comment on our findings in light of those of other related studies. In a partial test of Hayes and Wheelwright, 1979a, Hayes and Wheelwright, 1979b and Hayes and Wheelwright, 1984 product-process matrix, Safizadeh et al. (1996) observed that manufacturers that had an appropriate fit between product and process structures obtained superior manufacturing performance—providing some evidence to validate the product-process matrix. The results of our analyses agree, in general, with their findings. For example, we found significant correlation between the product and process structure dimensions of the GMS. However, our findings also extend their results, because the GMS model uses a broader conceptualization of the product and process structures, and adds another dimension of organizational scope. In addition, we also explicitly considered the fit between intended strategic objectives and existing manufacturing structures—and how that fit was related to performance outcomes. Thus our analysis is more complex, both in terms of the number of dimensions of manufacturing structures considered and in terms of the explicit consideration of the fit between strategic intentions and manufacturing structures. For managers, the general finding of the study that a fit between manufacturing objectives and manufacturing design choices is related to better manufacturing performance, highlighting the importance of manufacturing design decisions. Managers are often rightfully skeptical of theories and models that have not been empirically validated. This study provides empirical validation of some aspects of the GMS framework. Taken with other published studies of the GMS framework (e.g. Devaraj et al., 2001), the results of this study should provide an increased level of confidence in the GMS framework for the practicing manager. However, the practical application of the GMS framework in an industrial setting will require additional development. The GMS framework identifies several generic strategies and indicates how they are linked to cost, differentiation, or both orientations. Furthermore, the framework suggests manufacturing structures that are likely to be feasible and others that are likely to be infeasible. The results of this study support the predictions of infeasibility for GMS types 4 and 5, since we did not find any plants following these “infeasible” strategies. Furthermore, the results of this study indicate that when manufacturing structures are appropriately matched (fit) with objectives, that superior performance on relevant objectives was observed. However, managers should be cautioned that application of the GMS framework or mere consistency in objectives and design choices does not guarantee the observed results, since substantial amounts of variance in performance remain to be explained, beyond that which is identified by our analyses. Some industry and country effects were observed in the analyses. While this result is not surprising, relatively few manufacturing or operations strategy studies actually control for different levels of performance by industry or country. This raises an important issue, since the failure to control for industry and country effects may confound the results of studies not incorporating these controls. Following the approach taken in the business strategy literature, we suggest that future studies of manufacturing strategy (1) seek to identify if and why the proposed strategies are industry or country specific; and (2) explicitly control for industry and country effects. In addition to the limitations already mentioned, we acknowledge that one of the limitations of this study is that the sample size is relatively small. The results of this study should not be generalized beyond what is reasonable, given the nature of the sample. Future studies should consider substantially larger samples including greater representation of industries and countries. Given the cross-sectional nature of the data that were employed in this research, we should exercise caution in drawing causal inferences from the findings of this study. Despite this caveat, we observe an association between the consistency of a plant’s manufacturing strategy, objectives, and performance. More detailed longitudinal studies may be appropriate for assessing causality. In conclusion, this study evaluated how the fit between manufacturing objectives and manufacturing strategies (as evidenced by manufacturing structure) was related to relevant manufacturing performance outcomes in plants from three industries and four countries. Empirical results indicated that the better the fit between objectives and strategies, the better the performance of manufacturing plants.