Knowledge that Transforms

Despite the attention given to restructuring and trimming down manufacturing firms during the 198Os, little attention has been paid to the mix of skills they needed under different circumstances. We examined the patterns of employment by occupation in manufacturing industries utilizing different production technologies and the effect of establishment size on nonproduction employment. We found that a relationship exists between production technology and nonproduction employment per 100 production workers. Establishment size is found to be a moderator between nonproduction employment and production technology. Our findings imply two clear messages for managers. First, when considering major changes in production technology, managers should be aware that the supporting skills they will need from their nonproduction work force are likely to change greatly. Further, these changes involve technical and managerial workers as well as clerical and production support people. Second, they should restructure the functional or occupational mix of an organization in the context of the process technologies in place. Different process technologies require different structures.

During the 19805 much attention, both pro and con, was directed toward optimized production technology (OPT). Criticism was directed at the proprietary nature of the scheduling algorithms within the software. Despite these criticisms, many large companies installed OPT. Little has been written on how the software is constructed and operates. This article introduces the software, provides a discussion of how it interfaces with management, and how various algorithms are imbedded in the software function. Last, a survey of companies that had implemented the software revealed that the major users are in the automotive industry.

A simple mixed integer programming model for the N job/single machine scheduling problem with possibly sequence‐dependent setup times, differing earliness/tardiness cost penalties, and variable due dates is proposed and evaluated for computational efficiency. Results indicated that the computational effort required to reach optimality rose with the number of jobs to be scheduled and with decreased variance in due dates. Though computational effort was significant for the largest problems solved, the model remained viable for optimizing research scale problems.

We formulate a general sequencing problem that includes two classes of jobs with setup times, setup costs, holding costs, and deadlines. The formulation is unique in its explicit recognition of the opportunities to exploit productive capacity increases due to batching. An algorithm based on tabu search is then used as a solution method. Computational results are presented that suggest that the algorithm is effective.

With the use of questionnaire data from owners, presidents, CEOs, and chairmen of boards and a logistic regression approach, we analyzed the manufacturing strategies of 64 new‐venture firms in the computer and communications equipment manufacturing industries. We found statistically significant differences in manufacturing posture as a function of whether the new venture was corporate‐sponsored or independent (i.e., new venture origin). The findings suggest that independent firms attack the market with superior product quality with the use of technology in the public domain. They offer a narrow range of products directed to a few large customers and do not make developing new products a primary issue. In contrast, corporate‐sponsored tirms are less concerned with superior product quality but emphasize patented technology and new product development. They provide a broader range of products and service smaller customer orders.

The challenges and opportunities in production and operations management (POM) are almost unlimited because in the world economy, manufacturing andservice operations account for more than 10 trillion dollars per year and in any single industry, the performance varies widely from country to country and from organization to organization. The goal of Production and Operations Management is to contribute to improving the management of manufacturing and service operations all over the world. The editors and reviewers judge the papers published in the journal for their contribution to improving of business practices and to further closeness between research and practice. The journal will publish high quality papers on a broad range of topics in POM, and it encourages all paradigms, old and new. We also invite managers from around the world to describe specific POM problems that provide challenging opportunities for academic research.

This paper introduces a stochastic model of a distribution system where the stocking location is owned by a dealer (or retailer) and the product is supplied by a manufacturer. Inventory is managed by the dealer, and the manufacturer is responsible for delivery of the product through both regular replenishment and expedite shipment modes. The dealer and the manufacturer share the goal of providing a high level of customer service. Demand, moreover, is a function of the service level offered to the market by the dealer. We develop optimal stock control policies for the cases where each decision maker in turn is dominant and acts unilaterally while being constrained by the supply/demand linkages of the system. We also develop an optimum policy for the case where both levels are managed under centralized control (i.e., both levels cooperate). Results indicate that the expected profit for a dominant dealer (or dominant manufacturer) is higher under decentralized control than the optimal solution for either under centralized control. However, the centralized solution is a global‐optimal solution and therefore will guarantee longterm stability. Differences between the various solutions are analyzed explicitly to estimate the cost of coordination.

The persistent shortage of nurses adversely affects the productivity, quality of care, and operating costs in most acute care hospitals. Aggravating the shortage are high nurse turnover rates, approaching 200% in some institutions. Policies to ensure adequate staffing levels and provide more attractive work schedules are alleged to improve nurse retention. However, their cost is seldom discussed. We compared expected nursing expense and workforce requirements to staff eight medical and surgical nursing units of a large hospital for 1 month, under 12 different scheduling policies alleged to improve turnover. Using simulation and an integrated staffing and scheduling methodology, we found that the expected nursing wages and workforce requirements for some policies differed by as much as 33%. In this hospital, the expected labor costs for certain policies could erode the benefits expected from improved retention. In contrast, other policies appear to allow high utilization of nursing resources, enhancing the expected benefits of reduced turnover with significant reductions in expenses for labor, recruiting, training, and fringe benefits.

Small manufacturing firms make a significant contribution to the economy. Yet, partly because of the greater availability of data on larger firms, strategic management and manufacturing strategy research have tended to neglect small business. Using a survey of small manufacturers, we examine the applicability of Porter's typology of generic strategies to this context and investigate the links between generic strategies and one important dimension of manufacturing strategy‐technology management. The analysis of generic strategies shows consistency with previous work on larger firms and tends to support the use of the typology in this setting. Moreover, the internal and external influences on the technology adoption decision process vary significantly with generic strategies, implying that the role played by individuals and networks constitutes a dominant factor. Surprisingly, no distinction was found between firm groupings with respect to decision criteria, which suggests that the decision process of small firms remains rather inexplicit, informal, and, to a large extent, intuitive. We also found that technologically more sophisticated firms tend to hold stronger competitive positions and that technological strength appears to be related to both cost advantage and differentiation.

Most inventory and production planning models in the academic literature treat lead times either as constants or random variables with known distributions outside of management control. However, a number of recent articles in the popular press have argued that reducing lead times is a dominant issue in manufacturing strategy. The benefits of reducing customer lead times that are frequently cited include increased customer demand, improved quality, reduced unit cost, lower carrying cost, shorter forecast horizon, less safety stock inventory, and better market position. Although the costs of reducing lead times in the long term may be relatively insignificant compared with the benefits, in the short term these costs can have a significant impact on the profitability of a firm. This article develops a conceptual framework within which the costs and benefits of lead time reduction can be compared. Mathematical models for optimal lead time reduction are developed within this framework. The solutions to these models provide methods for calculating optimal lead times, which can be applied in practice. Sensitivity analysis of the optimal solutions provides insight into the structure of these solutions.