bluuebunny/crossref_metadata_2025_split huggingface dataset:
The use of simulation in system design: A case study using the precision location strike system
Authors: Clark Thomas D. , Arnett Gary W. , Reybrock Nicholas W. 
Published: October 2002

AbstractThe purpose of the article is to illustrate the use of computer simulation study and modeling in the design of complex systems. When new systems are designed, decision‐makers rely heavily upon judgment, intuition and experience. The method discussed is intended to enhance these executive attributes by providing a vehicle with which the decision‐maker may be proactive before implementing the system rather than being solely reactive after implementation.The application of a method known as the “systems science paradigm” is discussed and illustrated using the U.S. Air Force's Precision Location Strike System. The problem was to design a logistical support structure for the Strike System. The three major options proposed were to locate repair and resupply structures at the point of equipment use in Europe, to have a mixed system, or to locate support at a base in the continental United States.The research employed the paradigm suggested to develop an experimental Q‐GERT simulation model of the processes and structures envisioned. Data development for abstract systems is illustrated and methods to experiment with alternate structures are developed and discussed. The concept of multiechelon inventory and repair are addressed within the problem's context.A proposed design alternative resulted from application of the process. Methods, other than cost effectiveness, to evaluate the system are also suggested. Implementing the design process for ill‐defined systems appears to be an effective method for dealing with such design problems.
Exponentially smoothed regression analysis for demand forecasting
Authors: Roodman Gary M. 
Published: October 2002

AbstractThis article proposes a new technique for estimating trend and multiplicative seasonality in time series data. The technique is computationally quite straightforward and gives better forecasts (in a sense described below) than other commonly used methods. Like many other methods, the one presented here is basically a decomposition technique, that is, it attempts to isolate and estimate the several subcomponents in the time series. It draws primarily on regression analysis for its power and has some of the computational advantages of exponential smoothing. In particular, old estimates of base, trend, and seasonality may be smoothed with new data as they occur. The basic technique was developed originally as a way to generate initial parameter values for a Winters exponential smoothing model [4], but it proved to be a useful forecasting method in itself.The objective in all decomposition methods is to separate somehow the effects of trend and seasonality in the data, so that the two may be estimated independently. When seasonality is modeled with an additive form (Datum = Base + Trend + Seasonal Factor), techniques such as regression analysis with dummy variables or ratio‐to‐moving‐average techniques accomplish this task well. It is more common, however, to model seasonality as a multiplicative form (as in the Winters model, for example, where Datum = [Base + Trend] ∗ Seasonal Factor). In this case, it can be shown that neither of the techniques above achieves a proper separation of the trend and seasonal effects, and in some instances may give highly misleading results. The technique described in this article attempts to deal properly with multiplicative seasonality, while remaining computationally tractable.The technique is built on a set of simple regression models, one for each period in the seasonal cycle. These models are used to estimate individual seasonal effects and then pooled to estimate the base and trend. As new data occur, they are smoothed into the least‐squares formulas with computations that are quite similar to those used in ordinary exponential smoothing. Thus, the full least‐squares computations are done only once, when the forecasting process is first initiated. Although the technique is demonstrated here under the assumption that trend is linear, the trend may, in fact, assume any form for which the curve‐fitting tools are available (exponential, polynomial, etc.).The method has proved to be easy to program and execute, and computational experience has been quite favorable. It is faster than the RTMA method or regression with dummy variables (which requires a multiple regression routine), and it is competitive with, although a bit slower than, ordinary triple exponential smoothing.
On the retention of finished goods inventory when reorder‐occurrence is uncertain
Authors: Jesse Richard R. 
Published: October 2002

AbstractFinished goods inventory may accumulate for a variety of reasons in the production of lowdemand or slow‐moving items. In this article a simple model is developed and some results are presented to aid in making inventory retention decisions when there exists uncertainty about whether reorders for a slow‐moving item will be received in the future. In formulating the model, it is assumed that some excess inventory remains after filling a customer order and that a single reorder for the item is possible. The problem is to determine how long to retain the inventory before disposing of it.The relevant expected costs of retaining some quantity of inventory for a period of time consist of two types: (1) the inventory carrying cost less the salvage value from disposal; and (2) the future production cost incurred due to premature disposal. Using conventional optimization, a relationship for finding the optimal retention period is derived and conditions are identified that ensure that the optimal retention period can be obtained directly from the relationship.To determine the optimal retention period, probability distributions are required for the interarrival time until the reorder occurs and for the reorder quantity. While it seems logical to assume that each item would have its own distributions, this argument leads to a specification problem since the data needed to establish distributions for individual items will generally not be available for slow‐moving items. One approach used to overcome this problem in practice is to combine data for a selected group of items and use the combined data to obtain distributions useful for any or all of the items in the group. An example illustrating this approach is provided.
The sources, measurements, and managerial implications of process commonality
Authors: Treleven Mark , Wacker John G. 
Published: October 2002

AbstractThis article explores the issue of commonality of processes. While the benefits of high process commonality (similarity of processes) are generally recognized, this research represents the first clear identification of the sources, development of measurements, and discussion of the managerial implications of process commonality.The first section of the article identifies and categorizes the sources of process commonality. These categories are: low set‐up times, flexibility to change from one operation to another, and flexibility in making expedite decisions‐ Indices are developed in the second section for each of these three categories at the work center level. These indices are examined using sensitivity analysis and are found to respond appropriately to changes in parameters. Formulation of a composite index is also discussed.The final section discusses the managerial implications of process commonality at the strategic, tactical, and operational levels. The impact of improved process commonality covers a wide range of decisions, including product/process selection, production/inventory control system selection, and definition of group technology cells, among others.
Operations management taxonomy
Authors: Prentis Eric L. 
Published: October 2002

AbstractOperations management is responsible for achieving an effective and efficient transformation process. Changes taking place in the marketplace and in technology require the correct positioning of operations in the production of goods and services. The service sector within our economy has grown tremendously, representing an ever greater share of our gross national product. A general operations management (OM) paradigm, combining both goods and services, will help researchers as well as practitioners.The operations classification schemes proposed previously have been narrower in scope and incorporated primarily manufacturing systems. The transformation process is universal; major constraints and standard approaches need to be identified regardless of whether it is a good or service produced. This article presents and defines a descriptive OM model. Sample industries and companies are positioned in the market and product/service matrix; in addition, operations activities and their long‐, medium‐, and short‐term solution approaches to operations problems are identified.This operations model keys on the definition of the product/service produced. The major groupings of the first two dimensions of the model depend on (1) market requirements, and (2) whether the product/service is discrete or divisible, fixed site or transportable. Major groupings of the operations activities, required to effectively and efficiently transform the product/service, define the management approach and product/process technology used. These represent the third dimension to our model. The fourth dimension is the time available to plan and implement solutions to operations problems.The four dimensional space‐time paradigm is cohesive and inclusive, and better identifies the major constraints on goods and/or services production. It is intended to analyze and synthesize both the similarities and differences among industries and companies positioned in matrix categories, and thus to help determine the appropriate actions of operations managers during the transformation process.The complexity of the OM paradigm shows realistically the trade‐offs that have to be made in this dynamic area. Changing markets, technology, management, and economics all play a part in positioning operations. Resulting changes in the market requirement and product/service positioning matrix can change unit costs. Movement in any direction on the matrix should not be thought of as a natural evolution but should be meticulously planned. Goods and services producers grouped in each of nine major transformation categories may be able to use similar planning, implementation, and control approaches.
A table for the calculation of safety stock
Authors: Aucamp Donald C. , Barringer Robert L. 
Published: October 2002

AbstractSafety stock is often calculated to accommodate a given service level (SL), where service is defined in some manner by the user. In this regard, the two favorite definitions of service are the percent of good cycles and the fraction of demand satisfied by off‐the‐shelf inventory.Judging from its increased appearance in the literature, especially in textbooks, the latter definition apparently is gaining in popularity. Unfortunately, this definition requires a little more effort to use. A procedure originally worked out by Brown (Brown's Method) shows how the calculations are to proceed. The partial expectation, E(z), is first calculated as a function of the expected lead time demand (μ), the standard deviation (σ) of lead time demand, and the required service level. Once E(z) is found, a table is consulted (Brown's Table) to determine z. Finally, the safety stock is calculated from the formula, zσ.There are several problems with using Brown's Table. First, z is given as the dependent variable rather than the independent variable, so the user has to search a bit to find the nearest pair of entries that straddle the required value of E(z). Then the user must perform an awkward interpolation, something many business students find very confusing.An additional problem with the table is that it is both sparse and inaccurate for high values of z. In fact, there are no entries at all for z 4.50, and there is only one significant digit of accuracy for z 3.93. Finally, Brown does not supply a computer program that calculates z directly from E(z). While this lack of a program is not important to the casual user of the tables, it can be important when one wishes to set safety stock levels automatically and internally by the computer.This article presents a new table, in which E(z) is the dependent variable rather than the independent variable. The computer program used to evaluate the entries yields solutions that are accurate to about fourteen decimal places. With this kind of precision the authors were able to extend Brown's Table to high values of z, while at the same time providing for virtually any number of significant digits. Incidentally, the entry density in this table is nonuniform (somewhat logarithmic) to accommodate usage over various orders of magnitude of z and E(z).The article also includes a simpler version of our computer program (single precision), which yields about four decimal place accuracy. The double precision program actually used is available on request, but it is somewhat complicated and depends on the computer used (the authors used a TRS‐80).
Tooling constraints and shop floor scheduling: A simulation study
Authors: Melnyk Steven A. , Ghosh Soumen , Ragatz Gary L. 
Published: October 2002

AbstractResearch into the problems of job shop scheduling has dealt with two of three major categories of shop floor resources: machine capacity and labor. Generally ignored has been the impact of tooling constraints, the third form of shop floor resource, on the scheduling process. The impact of tooling is the focus of this study.Using a simple simulation model of a shop operating under varying levels of tooling availability, it is shown that the level of tooling availability has a significant impact on shop floor performance as measured in terms of mean flow time, mean tardiness, number jobs tardy and number of tool changes. Four different tooling assignment rules are evaluated and their effect on system performance is described.The results indicate that tooling assignment rules should consider both job priorities and tooling availability in order to achieve good shop performance. The paper concludes with general recommendations for the management of shop orders facing limited tooling availability.
Due date based scheduling in a general flexible manufacturing system
Authors: Raman N. , Talbot F.B. , Rachamadugu R.V. 
Published: October 2002

AbstractDynamic scheduling of manufacturing systems for due date based objectives has received considerable attention from practitioners and researchers due to the importance of meeting due dates in most industries. Research investigations have focused primarily on the relative effectiveness of various dispatching rules in job shops. These rules operate by prioritizing jobs using a “criticality index” based on job and system status. Jobs are then scheduled from most critical to least, with the indexes typically being updated as the system changes.This study considers two important issues which have not been addressed previously in the literature. First, we investigate the impact of unequal machine workloads on the relative effectiveness of dispatching rules. This is significant because workloads are likely to be unbalanced in most real systems. While it is clear, intuitively, that this imbalance in machine workloads is likely to deteriorate system performance, it is not obvious whether the superiority of certain dispatching rules established in earlier studies for balanced workloads is carried forward to this case. We show that the performance of different dispatching rules does indeed depend upon the degree of workload imbalance. We also propose and test a scheduling procedure which performs well in both balanced and unbalanced systems.Next, we develop a scheduling approach which shows promise as being an improved alternative to the use of dispatching rules. This approach decomposes the dynamic scheduling problem into a series of static problems.These static problems are then solved using an optimum‐seeking method, and the solutions are implemented on a rolling basis. We show through a simulation experiment that adopting this approach over dispatching rules leads to an improvement in the overall solution quality, even in a dynamic environment.The two very practical implications of our study are: (1) that commonly used dispatching rules in job shops or automated manufacturing systems may not be the best approach when capacity utilization is unbalanced (2) a job shop or automated manufacturing system would likely benefit from implementing optimal‐seeking scheduling rules instead of the traditional job dispatching rules.
Measurement of manufacturing flexibility: A value based approach
Authors: Ramasesh Ranga V. , Jayakumar Maliyakal D. 
Published: October 2002

AbstractFlexibility is emerging as one of the key competitive strengths in today's manufacturing systems. Measuring the flexibility of manufacturing systems is important to operations managers engaged in decision making on strategic issues related to flexibility. Flexibility is widely recognized as a multidimensional attribute. Most of the studies reported in the literature have focused on measuring separate dimensions independently and also have tended to be non‐financial in nature. As a result, many of these measures have only limited application in strategic decision making. In this paper we consider the measurement of the aggregate flexibility of a manufacturing system by investigating the joint effect of the flexibilities on a variety of dimensions when measurement for a medium‐term time horizon is appropriate. We propose a value‐based approach in which flexibility is measured by the ability of a manufacturing system to generate high net revenues consistently across all conceivable states of the nature in which it may be called upon to function. We suggest the ratio of the mean to the standard deviation of the distribution of optimal net revenues as the flexibility measure.We develop a stochastic mathematical programming formulation that facilitates the modeling of the manufacturing flexibility on different dimensions and the determination of the distribution of the net optimal revenues across all possible states. We exploit the special structure of our formulation to efficiently solve large problems that arise in some practical situations. We illustrate the application of the model using a numerical example which shows that the value of each type of flexibility varies depending on how it enhances the revenues generated by the system. We also find that synergistic benefits can be gained by combining different types of flexibilities. Our model provides an approach to determine the aggregate value of manufacturing flexibility and to explore the trade‐offs that may exist among the different dimensions of flexibility.
Production flow analysis for planning group technology
Authors: Burbidge John L. 
Published: October 2002

AbstractGroup technology (GT) is a method of organization for factories in which organizational units known as “groups” each complete a particular set or “family” of parts with no backflow, or crossflow between groups, and are equipped with all the facilities they need to do so.Production flow analysis (PFA) is a technique for planning the change to GT in existing batch and jobbing production factories. It finds a total division into groups, using the existing machines and methods to make the existing parts, without any need to buy additional machine tools.Because the change to GT is generally both possible (if PFA is used for planning) and profitable, process organization, in which organizational units specialize in particular processes, is now obsolete.
Accounting for the multi‐period impact of service when determining employee requirements for labor scheduling
Authors: Thompson Gary M. 
Published: October 2002

AbstractProviding good customer service, inexpensively, is a problem commonly faced by managers of service operations. To tackle this problem, managers must do four tasks: forecast customer demand for the service; translate these forecasts into employee requirements; develop a labor schedule that provides appropriate numbers of employees at appropriate times; and control the delivery of the service in real‐time.This paper focuses upon the translation of forecasts of customer demand into employee requirements. Specifically, it presents and evaluates two methods for determining desired staffing levels. One of these methods is a traditional approach to the task, while the other, by using modified customer arrival rates, offers a better means of accounting for the multi‐period impact of customer service. To calculate the modified arrival rates, the latter method reduces (increases) the actual customer arrival rate for a period to account for customers who arrived in the period (in earlier periods) but have some of their service performed in subsequent periods (in the period). In an experiment simulating 13824 service delivery environments, the new method demonstrated its superiority by serving 2.74% more customers within the specified waiting time limit while using 7.57% fewer labor hours.
Improving the assessment of gestational age in a Zimbabwean population
Authors: Feresu S.A , Gillespie B.W , Sowers M.F , Johnson T.R.B , Welch K , Harlow S.D 
Published: October 2002

AbstractObjectives: To evaluate the performance and the utility of using birthweight‐adjusted scores of Dubowitz and Ballard methods of estimating gestational age in a Zimbabwean population. Method: The Dubowitz and the Ballard methods of estimating gestational age were administered to 364 African newborn infants with a known last menstrual period (LMP) at Harare Maternity Hospital. Results: Both methods were good predictors of gestational age useful in differentiating term from pre‐term infants. Our regression line was Y(LMP gestational age)=23.814+0.301*score for the Dubowitz and Y(LMP gestational age)=24.493+0.420*score for the Ballard method. Addition of birthweight to the regression models improved prediction of gestational age; Y(LMP gestational age)=23.512+0.219*score+0.0015*grams for Dubowitz and Y(LMP gestational age)=24.002+0.292*score+0.0016*grams for Ballard method. Conclusions: We recommend the use of our birthweight‐adjusted maturity scales; the Dubowitz for studies of prematurity, and the Ballard for routine clinical practice.