Peter T. Ward

Lean manufacturing: context, practice bundles, and performance

Management literature has suggested that contextual factors may present strong inertial forces within organizations that inhibit implementations that appear technically rational [R.R. Nelson, S.G. Winter, An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA, 1982]. This paper examines the effects of three contextual factors, plant size, plant age and unionization status, on the likelihood of implementing 22 manufacturing practices that are key facets of lean production systems. Further, we postulate four “bundles” of inter-related and internally consistent practices; these are just-in-time (JIT), total quality management (TQM), total preventive maintenance (TPM), and human resource management (HRM). We empirically validate our bundles and investigate their effects on operational performance. The study sample uses data from IndustryWeek’s Census of Manufacturers. The evidence provides strong support for the influence of plant size on lean implementation, whereas the influence of unionization and plant age is less pervasive than conventional wisdom suggests. The results also indicate that lean bundles contribute substantially to the operating performance of plants, and explain about 23% of the variation in operational performance after accounting for the effects of industry and contextual factors.

Approaches to mass customization: configurations and empirical validation

Abstract Mass customization is a paradox-breaking manufacturing reality that combines the unique products of craft manufacturing with the cost-efficient manufacturing methods of mass production. Although this phenomenon is known to exist in practice, academic research has not adequately investigated this new form of competition. In this research, we develop a configurational model for classifying mass customizers based on customer involvement in design and product modularity. We validate this typology through an empirical analysis and classification of 126 mass customizers. We also explore manufacturing systems and performance implications of the various mass customization configurations.

Research in the process and content of manufacturing strategy

Traditionally, research in manufacturing strategy has not explicitly recognized the distinction between process and content research. In this paper, however, separate conceptual models for process and content are extracted from the literature. These models are used to: (1) articulate the distinction between process and content research; (2) synthesize the predominant process and content models; (3) evaluate the existing research; and (4) propose future research directions from both a process and content perspective. The research reported in this paper reveals that the literature has not progressed sufficiently beyond articulating the major issues in manufacturing strategy. Three causes for this lack of progress are identified: (1) a paucity of theory construction; (2) little empirical research; and (3) insufficient efforts in adopting ideas and methods from related fields.

Manufacturing strategy in context: environment, competitive strategy and manufacturing strategy

Abstract Considering manufacturing strategy in its larger strategic context has been thematic in conceptual literature in operations but relatively neglected in empirical studies, thus leaving predominant conceptual models of manufacturing strategy largely untested. This research develops a conceptual model of manufacturing strategy from the literature and tests the model using data from a sample of manufacturers in three industries in the United States. This research contributes to manufacturing strategy literature in four ways. First, it supports empirically a model of manufacturing strategy that is predominant in the conceptual literature. Second, it demonstrates that the strategic linkages in manufacturing businesses are clearer among good performers than poor performers. Third, this research suggests that competitive strategy acts as a mediator between an organizations environment and its manufacturing strategy. Fourth, the findings suggest that the relationship between competitive strategy and performance is mediated by manufacturing strategy. These last two findings have important implications for approaching research in manufacturing strategy in the future.

Business environment, operations strategy, and performance: An empirical study of Singapore manufacturers

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 stretegic 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.

Configurations of Manufacturing Strategy, Business Strategy, Environment and Structure

By developing strategic configurations which describe commonly used paths to competitive advantage for manufacturers, this paper reconciles some basic concepts from competitive strategy and manufacturing strategy. Four basic strategic configurations are identified: niche differentiator, broad differentiator, cost leader, and lean competitor. The configurations are traced conceptually through competitive strategy, organizational structure, environment, and a strategic framework of manufacturing capabilities and decisions. Examples from the major home appliance industry are provided for each configuration.

Unlocking the potential of advanced manufacturing technologies

Abstract 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.

Impact of Information Technology Integration and Lean/Just‐In‐Time Practices on Lead‐Time Performance*

Managers seeking to improve lead-time performance are challenged by how to balance resources and investments between process improvement achieved through lean/just-in-time (JIT) practices and information technology (IT) deployment. However, extant literature provides little guidance on this question. Motivated by both practical importance and lack of academic research, this article examines empirically the relationships among interfirm IT integration, intrafirm IT integration, lean/JIT practices, and lead-time performance using data from IndustryWeeks Census of Manufacturers (IndustryWeek, 2006). The results provide several new insights on the relationship between IT integration and lean/JIT practices. First, the study confirms that implementing lean/JIT practices significantly reduces lead time. Second, lean/JIT practices mediate the influence of IT integration on lead-time performance. This suggests that process improvements that result from lean/JIT practices are important contributors to the success of IT integration. Even companies that have experienced success in reducing lead time through lean/JIT practices may benefit from IT integration practices such as those embodied in enterprise resource planning systems. The findings provide managers with empirical evidence and a theoretical framework on the balance between lean/JIT and IT for effecting improvement in lead-time performance, thus offering practical guidance on this important question. Future research is needed to extend the lean/JIT practices in this study to supply chain practices and explore the relationship between supply chain practices and IT integration.

Approaches to the factory of the future. An empirical taxonomy

Abstract 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.

Scheduling approaches for random job shop flexible manufacturing systems

This research examines the influences that scheduling schemes and the degree of routeing flexibility have on random, job shop flexible manufacturing systems within a static environment. The first factor in the experiment includes three scheduling schemes. Two of these schemes are off-line schemes which schedule many operations prior to actual production. The first of the off-line schemes establishes an overall optimal solution, and the second off-line scheme decomposes the problem into a loading subproblem and a resulting scheduling subproblem and finds optimal solutions to both subproblems. The third scheme uses control policies and dispatching rules to establish a schedule in a real-time mode. The number of alternative machine options (i.e., the degree of routeing flexibility) is a second factor in the experiment and includes eleven levels. In addition, the effects of interaction between scheduling and routeing flexibility on performance are explored.