Knowledge that Transforms

Integration is a buzzword in manufacturing strategies for global competitiveness. However, some fundamental questions have yet to be answered scientifically; namely, what is integration, why integrate, and what should be integrated? How do existing integration models approach the problems of integration and to what extent are they successful? A theory‐based model of information requirements for integrated manufacturing is needed to answer these questions properly. Such a model can unify hitherto narrowly defined and domainoriented approaches. We suggest a paradigm of parallel formulation and use it to formulate the information requirements for integration, including data and knowledge classes, decision spaces, and logical interactions.

I consider a dynamic input scheduling problem of a stochastic parallel processing system consisting of n identical flexible machining cells. The processing times at each cell are independent random variables. Previous study has indicated the NP complexity of the problem. In this paper, I prove the separability under an ideal just‐in‐time input condition. Using the separability, I then construct an approximation procedure for most realistic applications where the separability condition is violated. The approximation procedure requires only linear time and performed quite well on an extensive test with numerical examples.

This essay is based on my plenary address at the second annual meeting of the Production and Operations Management Society on November 11, 1991. I propose that as the competitive environment in which production and operations management (POM) practitioners operate becomes ever more demanding and the problems about which POM academics study and teach become more complex and interrelated, we need new approaches both in our teaching and our research. I describe five ways of expanding our “requisite variety” of capabilities.

The Logistics Management System (LMS) is a real‐time transaction‐based system combining decision technologies from AI, MS/OR, and decision support system that serves very successfully as a dispatcher or short‐interval scheduler by monitoring and controlling the manufacturing flow of IBM's semiconductor facility near Burlington, Vermont. LMS coordinates the actions and decisions of several logically isolated participants in a serially dependent system of activities. Therefore, it balances the requirements of several goals (cycle time, output, serviceability, and inventory management) that compete for the same resource, exploits emerging opportunities on the manufacturing floor, and reduces the distortion from unplanned events. This paper provides an overview of the LMS application, the concept of interrelated decision tiers in manufacturing decision making, and the need for the dispatch decision tier to successfully reduce apparent randomness. Historically, production and operations management has ignored this decision tier. This has significantly limited our ability to make an impact on the performance of the manufacturing operation.

A survey of 15 firms showed that agreement among operations managers on competitive priorities is related to agreement on long‐run strategic trade‐off decisions and not to agreement on shortrun trade‐offs. Furthermore, intended short‐run actions were often in conflict with stated competitive priorities. Use of management‐by‐objectives linked to performance appraisal was related to agreement on competitive priorities.

Raw lumber must be dried to a specified level of moisture content before it can be used to make furniture. This paper deals with a model‐based decision support system (DSS) for a local furniture manufacturing company to assist its management in scheduling lumber drying operations. In addition to buying ready‐to‐use dried lumber from vendors at a premium, the company processes raw lumber in house using two production process that require various lengths of processing time in predryers and dry‐kilns. Given the demand for various types of dried lumber over a specified planning horizon, the processing times and costs for each production process, technological restrictions, and management policies, the problem of interest is to satisfy the demand at a minimum cost. The DSS incorporates the mathematical formulation of this problem, is user friendly, maintains model and data independence, and generates the necessary reports, including loading and unloading schedules for the equipment.

As the world moves toward a global economy, it is increasingly important that operations management courses prepare students to address globalization issues. The purpose of this paper is to contribute to the dialog concerning how international topics are best incorporated into operations management curricula. On the basis of the results of a survey of operations management academicians worldwide, current course offerings are cataloged and topic areas critical to the globalization of operations are identified. Four major reasons for studying international operations management are proposed, which provide the basis for recommendations on how international topics can be introduced into established operations courses and for the design of an elective course in global operations. Finally, teaching materials relevant to international operations are surveyed.

We present a new paradigm of hierarchical decision making in production planning and capacity expansion problems under uncertainty. We show that under reasonable assumptions, the strategic level management can base the capacity decision on aggregated information from the shopfloor, and the operational level management, given this decision, can derive a production plan for the system, without too large a loss in optimality when compared to simultaneous determination of optimal capacity and production decisions. The results are obtained via an asymptotic analysis of a manufacturing system with convex costs, constant demand, and with machines subject to random breakdown and repair. The decision variables are purchase time of a new machine at a given fixed cost and production plans before and after the purchase. The objective is to minimize the discounted costs of investment, production, inventories, and backlogs. If the rate of change in machine states such as up and down is assumed to be much larger than the rate of discounting costs, one obtains a simpler limiting problem in which the random capacity is replaced by its mean. We develop methods for constructing asymptotically optimal decisions for the original problem from the optimal decisions for the limiting problem. We obtain error estimates for these constructed decisions.

The common approach to balancing mixed‐model assembly lines assumes that the line operators are well trained and that the learning effect is negligible. The assumption is that the line operates in steady state over a long period of time. Time‐based competition and frequent design changes in many products make this assumption incorrect, and the effect of learning on mixed‐model lines should not be neglected. We defined start‐up period and developed a model for the line design during start‐up. It can be used to evaluate a proposed line design or to develop a feasible line design and to estimate its cost. This proposed model integrates mixed‐model learning curves with aggregate planning under learning and a mixed‐model line design into a comprehensive framework designed to minimize the total cost of the line during the start‐up period.