Knowledge that Transforms

While Just‐in‐Time (JIT) manufacturing has emerged as one of the major tools to enhance manufacturing competitiveness, no attempt has been made to develop a reliable and valid measurement instrument for empirical research in JIT. Without such an instrument, generalization beyond the immediate sample is difficult or misleading. We have proposed a JIT framework and developed a valid and reliable instrument with 16 summated scales for dimensions that capture essential aspects of JIT useful in assessing its impact in manufacturing environments. In addition, we discuss in detail the interactive nature of JIT practice. And, we propose a step‐by‐step approach to reliability and validity testing. Four JIT practices (equipment layout, pull system support, supplier quality level, and Kanban) are identified as major contributing factors to JIT performance.

An innovation strategy for the manufacturing function covers four areas: a firm's desired innovation leadership orientation (i.e., being a leader versus being a follower), its level of emphasis on process and product innovation, its use of internal and external sources of innovations, and its intensity of investment in innovation. We examine two models of the association between manufacturing companies' innovation strategy and their financial performance. The first examines the variations in company financial performance as a function of the simultaneous effect of the dimensions of innovation strategy. The second is a sequential model that suggests a causal sequence among the dimensions of innovation strategy that may lead to higher performance. We used data from a sample of 149 manufacturing companies to test the models. The results (1) support the importance of innovation strategy as a determinant of company financial performance, (2) suggest that both models are appropriate for examining the associations between the dimensions of innovation strategy and company performance, and (3) show that the sequential model provides additional insights into the indirect contribution of the individual dimensions of innovation strategy to company performance. Finally, we discuss the implications of these results for managers.

Queueing models can usefully represent production systems experiencing congestion due to irregular flows, but exact analyses of these queueing models can be difficult. Thus it is natural to seek relatively simple approximations that are suitably accurate for engineering purposes. Here approximations for a basic queueing model are developed and evaluated. The model is the GI/G/m queue, which has m identical servers in parallel, unlimited waiting room, and the first‐come first‐served queue discipline, with service and interarrival times coming from independent sequences of independent and identically distributed random variables with general distributions. The approximations depend on the general interarrival‐time and service‐time distributions only through their first two moments. The main focus is on the expected waiting time and the probability of having to wait before beginning service, but approximations are also developed for other congestion measures, including the entire distributions of waiting time, queue‐length and number in system. These relatively simple approximations are useful supplements to algorithms for computing the exact values that have been developed in recent years. The simple approximations can serve as starting points for developing approximations for more complicated systems for which exact solutions are not yet available. These approximations are especially useful for incorporating GI/G/m models in larger models, such as queueing networks, wherein the approximations can be components of rapid modeling tools.

Production and Operations ManagementVolume 1, Issue 3 p. 333-333 EDITOR'S INTRODUCTION Kalyan Singhal, Kalyan Singhal Editor-in-ChiefSearch for more papers by this author Kalyan Singhal, Kalyan Singhal Editor-in-ChiefSearch for more papers by this author First published: September 1992 https://doi.org/10.1111/j.1937-5956.1992.tb00364.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume1, Issue3September 1992Pages 333-333 RelatedInformation

We are concerned with a discrete‐time undiscounted dynamic lot size model in which demand and the production setup cost are constant for an initial few periods and the holding cost of inventory is an arbitrary nondecreasing function assumed to be stationary (i.e., explicitly independent of time) in the same initial few periods. We show that there exists a finite forecast horizon in our model and obtain an explicit formula for it. In addition, we obtain fairly general conditions under which the existence of a solution horizon in the model implies the existence of a forecast horizon. We also derive an explicit formula for the minimal solution horizon. These results extend the earlier ones obtained for the dynamic lot size model with linearly increasing holding costs.

To plan and schedule the repair shops for recoverable parts at Deutsche Lufthansa AC, we designed a hierarchical model consisting of two levels. The top level calculates the optimal number of parts in the system to guarantee a certain service level while minimizing the capital tied up in parts. Given this provision, the lower level schedules the repair of parts so that the service level is actually maintained. Using queuing theory, the solution gives special attention to the different hierarchical dependencies. Lufthansa has implemented the model for its repair shops of electronic parts. Their experience with the model is discussed briefly.

This letter is from the chairmen of American Express, Ford, IBM, Motorola, Proctor & Gamble, and Xerox. These companies sponsor Total Quality Forum, an annual gathering to discuss total quality management.

R. J. Reynolds Tobacco USA (RJR) is currently implementing a microcomputer‐based decision support system to computerize and optimize the selection of patterns for loading cases of finished product into truck trailers at RJR's Central Distribution Center. This system allows for the efficient loading of trucks with less supervision. Total annual savings from reduced personnel and shipping costs is approximately $850,000. In addition to these benefits, the system is a stepping stone for trailer loading automation and the integration of a comprehensive load planning system.