Abstract:Management literature has suggested that contextual factors may present strong inertial forces within organizations that inhibit implementations that appear technically rational (Nelson and Winter, 1982). This paper examines the effects of three contextual factors, plant size, plant age and unionization status, on the likelihood of implementing twenty-two manufacturing practices that are key facets of lean production systems. Further, we postulate four "bundles" of interrelated and internally consistent practices; these are Just-in-Time, Total Quality Management, Total Preventive Maintenance, and Human Resource Management. 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 percent of the variation in operational performance after accounting for the effects of industry and contextual factors.

Our research addresses the confusion and inconsistency associated with "lean production." We attempt to clarify the semantic confusion surrounding lean production by conducting an extensive literature review using a historical evolutionary perspective in tracing its main components. We identify a key set of measurement items by charting the linkages between measurement instruments that have been used to measure its various components from the past literature, and using a rigorous, two-stage empirical method and data from a large set of manufacturing firms, we narrow the list of items selected to represent lean production to 48 items, empirically identifying ten underlying components. In doing so, we map the operational space corresponding to conceptual space surrounding lean production. Configuration theory provides the theoretical underpinnings and helps to explain the synergistic relationships among its underlying components.Keywords: Lean production, scale development, confirmatory factor analysis 3 DEFINING AND DEVELOPING MEASURES OF LEAN PRODUCTION INTRODUCTIONIn 360 BC, Plato (in Cratylus) suggested that linguistic confusion arises because multiple terms may refer to the same object or idea, a single term may refer ambiguously to more than one object or idea, and terms may be confusing because they are out of date. The same observations can be made today with respect to a number of management approaches. The current study addresses these issues with regard to lean production. We believe that the price paid for lacking a clear, agreed-upon definition is high because empirical testing of inexact and imprecise concepts leads to a body of research that examines slightly different aspects of the same underlying constructs masked by different terminology. Consequently, results from such testing do not improve our understanding, make marginal contributions to the existing knowledge base, and prevent academic fields from making real progress (Meredith, 1993). If theory and empirical work are to advance in this area, semantic differences between lean production and its predecessors must be resolved, the conceptual definition of lean production must be clarified, and operational measures must be specified. In this paper, we address these three issues.The approach now known as lean production has become an integral part of the manufacturing landscape in the United States (U.S.) over the last four decades. Its link with superior performance and its ability to provide competitive advantage is well accepted among academics and practitioners alike (e.g., Krafcik, 1988;MacDuffie, 1995;Pil and MacDuffie, 1996;Shah and Ward, 2003;Wood et al., 2004). Even its critics note that alternatives to lean production have not found widespread acceptance (Dankbaar, 1997) and admit that "lean 4 production will be the standard manufacturing mode of the 21 st century" (Rinehart et al., 1997:2). However, any discussion of lean production with managers, consultants, or academics specializing in the topic quickly points to an abse...

This paper reviews applications of structural equation modeling (SEM) in four major Operations Management journals (Management Science, Journal of Operations Management, Decision Sciences, and Journal of Production and Operations Management Society) and provides guidelines for improving the use of SEM in operations management (OM) research. We review 93 articles from the earliest application of SEM in these journals in 1984 through August 2003. We document and assess these published applications and identify methodological issues gleaned from the SEM literature. The implications of overlooking fundamental assumptions of SEM and ignoring serious methodological issues are presented along with guidelines for improving future applications of SEM in OM research. We find that while SEM is a valuable tool for testing and advancing OM theory, OM researchers need to pay greater attention to these highlighted issues to take full advantage of its potential. #

A typical approach to studying capabilities in the operations management literature is to assess the intended or realized competitive operational performance and their contribution to business and organizational objectives. While it is crucial to identify the operational performance that helps create competitive advantage, it is equally important to understand the means for delivering the needed performance at the operational level. Drawing on the resource-based view (RBV), we argue that routines are a critical source of operations capabilities and subsequently investigate operations capabilities by means of their underlying routines. Because a common problem to studying capabilities is the ambiguous and confusing definitions, we conduct an extensive literature review to address the semantic confusion among various definitions of capabilities and delineate it from other related terms. We identify improvement and innovation as two critical plant level capabilities, each consisting of a bundle of interrelated yet distinct routines. We then empirically measure the two capabilities as second-order latent variables and estimate their effects on a set of operational performance measures. The results suggest that routines form internally consistent bundles which are significantly related to operational performance. This supports our notion of ''capabilities as routine bundles'' that are difficult to imitate and thus a source of competitive advantage. #

It is widely recognized that new product development (NPD) is a highly interdependent process, yet efforts to empirically model the interdependence and examine its effect on firm performance are scarce. Our study addresses this research gap. We model firms’ abilities to collectively collaborate with suppliers, customers, and internal employee teams in NPD as collaborative competence and examine its impact on project and market performance. Using responses collected from 189 NPD managers, we find empirical evidence for collaborative competence and its differential impact on project and market performance. Specifically, we find that collaborative competence has a direct impact on project performance, but its impact on market performance is indirect, mediated through project performance. The results have significant managerial implications; achieving superior market performance from inter‐ and intra‐organizational involvement is contingent on achieving superior project performance, and companies that fail to achieve desired project performance outcomes will also fail in achieving market performance goals.