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ABSTRACTThe inability to identify all causal variables in a linear regression demand model may result in serial correlation which is generally considered undesirable; but it may be possible to take advantage of such an event. This case study, based upon Chemical Bank of New York, investigates the use of simple and exponential smoothing for modifying initial estimates from a regression model by using prior forecast error patterns to obtain better forecasts. The smoothing approaches are combined with a regression model to test for improved performance in predicting daily check volumes.

ABSTRACTThis paper presents a comparison of statistical techniques versus a sales force‐executive opinion approach to forecasting single item demand over a five year period for a single company. Three statistical techniques are used: Winter's three parameter exponential smoothing model, Brown's harmonic model, and Box‐Jenkins methodology. These techniques are compared against the company's forecasts and actual sales for a five year period. The results indicate an interesting area for further research.

ABSTRACTSupply chain management is a central and important area for academic research due to its impact on firms competing in today's global economy. Managing the flow of materials from supply sources to the ultimate customer represents a major challenge for today's managers. To assist managers, the concept of supply chain management has been adopted by many business leaders as an important way to assist in designing, planning, and controlling the network of facilities and tasks that comprise the many stages of the supply chain. In turn, the flow of academic research in the area has increased to provide a better set of guidelines for effective implementation and execution.This article sets the stage for recently completed research concentrating on supply chain management issues. First, a definition of supply chain management is provided and compared to recent usage in this area and logistics management. Also, a framework is provided that structures this dynamic and complex management task. Second, a review of past research is presented to illustrate the many paths supply chain management has traveled, and important contributions to supply management understanding and decision making. Third, recently completed research articles are introduced that have been selected to be part of this special issue of Decision Sciences. And fourth, future research directions for supply chain management that need to be pursued by interested investigators are discussed.

ABSTRACTMany American firms are implementing just‐in‐time production in order to minimize inventories, reduce flow time, and maximize resource utilization. These firms recognize that, in the short run, setup costs really are fixed expenses and it is available capacity which is the critical factor in determining production‐run quantities. We propose using available capacity to increase the number of setups and reduce lot‐size inventories. This results in improved relevant cost performance. Sugimori, Kusunoki, Cho, and Uchikawa [16] in their paper on the Toyota kanban system developed a relationship for lead time but failed to use it for lot sizing. We use this relationship to develop the joint lot‐sizing rule. The efficacy of our proposed rule is demonstrated by applying it to lot‐size scheduling problems at the John Deere Engine Works [14]. Extensions of the proposed rule to undercapacity situations with material‐wastage costs in the setup processes and to multistate production inventory systems also are discussed.

ABSTRACTAlthough order and labor dispatching in the job shop manufacturing setting have been investigated extensively over the last three decades, its representation of actual processes found in practice today is limited due to the move to cellular manufacturing (CM). Manufacturing cells have become an important approach to batch manufacturing in the last two decades, and their layout structure provides a dominant flow structure for the part routings. The flow shop nature of manufacturing cells adds a simplifying structure to the problem of planning worker assignments and order releases, which makes it more amenable to the use of optimization techniques. In this paper we exploit this characteristic and present two mathematical modeling approaches for making order dispatching and labor assignment/reassignment decisions in two different CM settings. The two formulations are evaluated in a dynamic simulation setting and compared to a heuristic procedure using tardiness as the primary performance measure. The formulations are superior to the heuristic approach and can be incorporated into detail scheduling systems that are being implemented by corporations employing enterprise resource planning (ERP) systems today.

ABSTRACTConstrained forecasting has become a popular approach to the complex task of predicting future sales. Mixed performance results have been obtained by companies employing constrained forecasting systems software packages which probably reflects the lack of any clear system implementation guidelines. A number of important operational parameters such as the number of product families, criterion used to define product families, number of system levels, etc. must be specified. This study reports on an investigation of two of these parameters: product group size and appropriate grouping criterion. Different tests are performed with the constrained forecasting system and compared to a traditional forecasting approach using product data from the Cummins Engine Company of Columbus, Indiana.

Describes an integrative microcomputer‐based exercise, ITEC, used
in teaching manufacturing strategy and manufacturing planning and
control courses. The exercise allows students the opportunity to gain
actual experience in making strategic decisions in manufacturing and in
managing the flow of materials in complex operations. It gives students
experience in making process choice decisions, and reinforces their
understanding of the difference between the planning and execution
decisions necessary to manage manufacturing operations using MRPII
planning logic under dynamic conditions. It provides experience in
managing both batch and line operations and is useful in demonstrating
the introduction of Just‐in‐Time manufacturing. The exercise also
provides experience in scheduling and co‐ordinating vendor operations to
support manufacturing requirements. Because of the uncertainty in
customer demand, process yields and vendor deliveries, students gain
insight and facility in managing complex operations with imperfect
information. Outlines the structure of ITEC, its educational benefits,
and its application within undergraduate, masters and executive
education classes.

ABSTRACTBatch manufacturing firms are experiencing significant changes because of technological developments in work center design, such as flexible manufacturing systems (FMS) and planning/control tools like computer‐aided process planning (CAPP). These new developments provide production managers with some solutions to a number of complex problems. For example, numerical‐controlled (NC) machine center installations are effective in providing quality parts because of tight tolerance specifications built into the equipment. However, these highly efficient centers create bottlenecks that constrain shop throughput, since production planners tend to rely too much on them.To help improve manufacturing planning, we introduce an important element to the batch production scheduling component of CAPP's mission—evaluating possible alternate routes. Production scheduling encompasses job route selection as well as machine center assignment (loading), job releasing, and setting due dates. In this paper, three routing strategies requiring different levels of shop floor information are tested and evaluated using computer simulation. Shop performance is measured by total cost and traditional measures of job flow time, lateness, and tardiness.

With the increasing emphasis on improving service sector staff scheduling, many organisations have turned to employing part‐time staff in greater numbers. Operating managers need to schedule this staff in an efficient manner to cover the highly variable demand that is commonly present. This study demonstrates a number of important factors that influence how well staff schedules can be matched against variable demand. Four heuristic staff scheduling procedures are examined that provide optimal, or near optimal, staff schedules under different operating conditions. It is shown that variability in demand controls the number of excess staff hours scheduled, and that the smaller the number of daily shift hours and/or the number of days worked per week, the lower will be the level of excess staff hours scheduled.

ABSTRACTControlling the flow of material on the shop floor involves releasing and dispatching jobs to meet customer due‐date requirements while attempting to keep operating costs low. This report presents an evaluation of five releasing mechanisms and four dispatching rules under various levels of aggregate due‐date tightness, shop cost structure, and machine utilization using simulation. The performance criteria of total shop cost, jobs on shop floor, deviation from due dates, and job queue time are collected to demonstrate the interactive nature of releasing and dispatching on shop performance.