No abstract

Consideration of the task environment, those forces which are out of the short-run control of management, has been relatively neglected in operations strategy research. The neglect of environmental factors in operations strategy research is surprising when one considers that the fit between environment and organizational capabilities and resources is a central tenet of major strategic management paradigms. We use a path analytic framework to study the effects of environment on operations strategy selection and performance (self-reported change in profits) for a sample of Singapore manufacturers.We identify strong relationships between environmental factors such as labor availability, competitive hostility, and market dynamism and the operations strategy choices encompassed by competitive priorities. The data also indicate that, when faced with the same environmental stimuli, high performers choose to emphasize different competitive priorities than low performers. In addition to exploring substantive questions about the importance of the environment in explaining operations strategy, this research also demonstrates that environmental variables can provide effective controls for industry effects in multiple industry empirical studies in operations strategy.

This research examines whether investments in advanced manufacturing technologies (AMTs) such as flexible manufacturing systems (FMS), computer aided design (CAD), computer aided manufacturing (CAM), robotics, etc., are more likely to lead to improved performance if they are supported by improvements in the manufacturing infrastructure of the company. This question is evaluated using data gathered from 202 manufacturing plants chosen from industries generally considered to have relatively high investments in technology. Multiple item scales are developed and adapted from sources in the literature to measure investments in technology, infrastructure, and the performance of the plant. Evidence supporting the reliability and validity of these scales is provided. Hierarchical regression is used to analyze the relationship between technology, infrastructure, and performance. The results suggest that there is an important interaction between the adoption of advanced manufacturing technologies and investments in infrastructure. Firms that invest in both AMTs and infrastructure perform better than firms which only invest in one or the other. Separate analyses on sub‐samples of firms with the highest and lowest investments in AMTs show that infrastructural investments have a stronger relationship with performance in the high investment group. Thus, the data indicate that infrastructural investments provide a key to unlocking the potential of advanced manufacturing technologies.

An empirical analysis of the patterns in which companies invest in advanced manufacturing technologies (AMTs) such as computer-aided design, computer-aided manufacturing, and flexible manufacturing systems is presented. Data for this analysis are gathered from 202 manufacturing plants chosen from industries generally considered to have relatively high investments in technology.Three general types of AMTs are identified from the literature: design, manufacturing, and administrative. Multiple item scales are developed to measure each type of AMT. These scales are shown to be reliable instruments, and are used to develop an empirical taxonomy which validates existing conceptual typologies of AMTs. A cluster analysis reveals four distinct groups of companies with respect to their approaches toward investing in AMTs. TRADITIONALISTS do not invest heavily in any of the three types of AMTs. GENERALISTS have moderate investments in each technology type. HIGH INVESTORS have the highest investment in each of the three technology types. The most interesting group may well be the DESIGNERS, which have low investments for manufacturing and administrative AMTs, but have the second highest investment in design-related AMTs.An analysis of the four technology groups reveals that while plants do differ in terms of plant size and integration, they do not differ significantly in terms of industry membership or performance. This suggests that successful firms can be found in each of the groups and that good strategies may be found that are consistent with each of these approaches. Therefore, the taxonomy is fairly robust, and further research must analyze companies within these groupings in order to identify the contingencies or other factors that may act in conjunction with technology to separate high and low performing firms. The data from our study clearly suggest that investments in technology alone are not a causal factor for performance improvement.