A SIMULATION MODEL OF THE HIGHER EDUCATION SYSTEM OF A STATE1

This paper describes a simulation model to estimate the enrollments and expenditures associated with various policy alternatives for the post-secondary education system of a state, with Indiana as an example. Enrollments, operating expenditures, and construction costs are predicted for every public and private school for each year until 1985. The basic model-building technique employed is regression analysis on historical data, but it is supplemented by other quantitative techniques and by experts' predictions. The model may be used to predict “most likely” future enrollments and costs, to perform sensitivity analyses to better understand the impact of various exogenous variables upon the system, and to evaluate possible policy alternatives. Including all of the schools in a state makes possible the evaluation of policy alternatives such as possible configurations of new two-year public schools, the effect of tuition changes, and the effect of enrollment ceilings for larger state universities.     

COMPLEX MULTIECHELON INVENTORY SYSTEM MANAGEMENT USING A DYNAMIC SIMULATION MODEL

A dynamic modeling approach to management of multiechelon, multi-indenture inventory systems with repair is addressed. The structure of the model follows that of the U.S. Air Force Reparable Asset Management System. The model is used as a vehicle to discuss the structure of typical multiechelon systems and to illustrate the advantages of a dynamic modeling approach to such systems.     

SIMULATION AND THE CAPITAL ASSET PRICING MODEL: A COMMENT*

This paper challenges Lewellen and Long's contention that a Hertz-type simulation generates irrelevant data for the evaluation of capital expenditure opportunities. The conclusion is reached that an estimate of an asset's “own risk” may be a vital first step in estimating its covariance properties with the market index. A method of incorporating the information generated by simulation into the capital asset pricing model is also provided.     

DEMAND MANAGEMENT: THE EVALUATION OF PRICE AND DUE DATE NEGOTIATION STRATEGIES USING SIMULATION

The research considers the problem of demand management in a firm where the firm's historical delivery service level reputation influences the number of quotation requests from its potential customers. Customers have a maximum and the firm has a minimum net price to due date tradeoff curve for each job. The demand management function bargains with the customer over price and promised due date. Bargaining finishes either with an agreed price and delivery date or with the customer refusing the firm's bid and placing the order elsewhere. The firm's objective is to maximize its long-term net revenue. The firm's demand management negotiation strategy guides this bidding process. The research demonstrates the use of simulation to test different demand management bidding and negotiation strategies for different market and firm scenarios. The demonstration uses 16 scenarios to test the different demand management negotiation strategies with a model of a classical job shop in a classical market. The investigation examines finite scheduling-based due date estimation methods, as well as the more traditional parameter-based methods. This demonstration shows that it is possible to test different bidding policies, using a simulation model of a firm and its customers, and to obtain usable results.     

A COMPUTER SIMULATION MODEL OF AN INDUSTRIAL WORK GROUP

The paper illustrates the desirability and feasibility of the computer simulation technique in socio-psychological research. A computer simulation model of a five-man industrial work group is constructed. After the model has successfully passed a two stage validation procedure, an experimentation phase is conducted. In a 2 times 3 replicated factorial experiment, five years of simulated weekly data is used to test several hypotheses which relate the independent variables–supervisory style and worker interpersonal orientation–to productivity, worker job satisfaction and group cohesiveness. The hypotheses were derived from the findings of prior short-lived laboratory and field research. The study indicates that the computer simulation approach is a valuable adjunct to the classical organizational research techniques.     

A SIMULATION MODEL OF PATRONAGE BEHAVIOR WITHIN SHOPPING CENTERS

This paper presents a micro-analytic store choice model which eliminates the assumptions of homogeneity of stores and homogeneity of shoppers found in earlier models. The model simulates individual shopping trips within a shopping center and was successful in predicting the stores chosen by a sample of shopping center patrons. Two earlier models were also tested, but the proposed model performs better than either of the simpler models. Besides its predictive value, the model suggests a number of strategic implications for retailers and shopping center developers and provides a methodology that may be useful in other areas of behavioral research.     

USING ARTIFICIAL INTELLIGENCE TECHNIQUES TO ENHANCE THE CAPABILITIES OF MODEL MANAGEMENT SYSTEMS

Decision Support Systems (DSSs) originally were proposed as interactive problem-solving vehicles through which models and analytical techniques could be made available to decision makers. Model management represents a line of research within the DSS field that focuses on the design and implementation of software environments to support the use of DSSs for this purpose. This paper describes the tasks that must be supported within these environments and illustrates how existing prototype Model Management Systems (MMSs) implementations provide this support. The use of artificial intelligence techniques in such implementations are reveiwed, and three scenarios are presented to show how future MMSs could be constructed using these techniques.     

CORPORATE RISK MANAGEMENT: A MODEL AND ITS APPLICATION

A problem faced by any corporate risk manager, once he has decided what set of properties will be insured, is to choose among the policies offered by potential insurers. This choice usually involves negotiating with insurers over the exact premium and deductible amounts which will characterize the coverage. In this paper, the problem is modeled in terms of expected utility of different forms of coverage. The model enables a risk manager to select among available terms of coverage and, more significantly, provides guidelines for him in negotiating with the insurers to obtain the best possible terms. As an illustration, an application using real data is described.     

THE MESSENGER BIAS: A RELATIONAL MODEL OF KNOWLEDGE VALUATION

How do managers value the knowledge that they encounter in organizations? A rational perspective assumes that managers carefully and accurately cull the best knowledge from their environments, while a random model situates managers in a chaotic organization, filled with preferences and solutions that are temporally matched. This paper develops a third view, a relational perspective, which describes how social relationships between knowledge messengers and knowledge receivers affect the way that managers evaluate new knowledge. We begin by focusing on two key dimensions of relational perception: social identification and threat appraisals. We then use these dimensions to derive a typology of six “relational schemas” that are commonly perceived between knowledge messengers and knowledge receivers at work. Next, we reveal how, holding knowledge content constant, these relational schemas bias the ways in which managers evaluate new knowledge. While network research demonstrates that relationships determine which knowledge managers see, this paper demonstrates that these relationships also have psychological implications by affecting how managers evaluate the knowledge that they see.     

REPORTING OF MANAGEMENT FORECASTS: AN EIGENVECTOR MODEL FOR ELICITATION AND REVIEW OF FORECASTS

Increasingly, business firms will be disclosing management forecasts in financial reports to investors. Single estimates based on a consensus view (e.g., Delphi estimates) may be used. It seems logical, however, to consider methods that seek formally to derive internally consistent subjective views regarding the sensitivity of assumptions and interacting events on forecasts. The major purpose of this paper is to extend micro-level cross-impact analysis to a macro-level eigenvalue analysis approach that elicits views of the effects of assumptions and interaction events on entire scenarios. Both methods are illustrated and contrasted. They are complementary rather than alternative methods. In particular, each method offers some potential in the development of operational guidelines for elicitation and reporting of management forecasts. In addition, the eigenvalue analysis offers several key advantages that make it potentially useful in preparing entire scenario forecasts.     

THE HARMS SHOE COMPANY: DEVELOPMENT OF A COMPUTER MODEL FOR THE CLASSROOM

This paper describes a case that examines five key topics in production and operations management [1]—forecasting, inventory control, transportation planning, aggregate planning, and the disaggregation of the aggregate plan—in an integrated, realistic manner for upper-level undergraduate business majors [3]. The case structure stresses the linkage of these interrelated subjects and supplements the regular classroom presentations dealing with them. The case relies on computer calculations at each stage to provide the information input for calculations at the next stage. It is a static model with a twelve-month planning horizon. Students complete several exercises and assume several roles as the case unfolds. They act on their own in filling some roles and as members of teams in performing others. They do not compete with each other, as the purpose of the case is to help them develop an appreciation of the factors that persons performing the role must consider.     

SIMULATION OF THE ECONOMIC FACTORS AFFECTING ORGANIZATIONAL SLACK: A FACTORIAL DESIGN

This paper investigates, through simulation models, the factors that affect organizational slack in a monopoly company under varying conditions of budget information, managerial behavior, and economic growth. The impact of organizational slack on corporate economic variables was identified in the models. The analysis of individual factors and the interactions among factors was based on the output of the factorial design. The implications of the findings for decision-making are discussed.     

A GOAL PROGRAMMING MODEL FOR INSURANCE AGENCY MANAGEMENT

Rapid changes in the business environment have forced property and casualty insurance agencies to re-examine their objectives. The need to maintain contact with a large number of insurers in order to place business, efforts to reduce cost by utilizing company services, and the expanding demand for commercial lines have all had major impacts on these agencies. In this paper, a goal-programming model is developed for use in insurance agency decision-making involving multiple and often conflicting goals. An example is presented.     

A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this “nonbelief in the Law of Large Numbers” by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a nonbeliever expects the distribution of signals will have fat tails. In inference, a nonbeliever remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.     

A CHANCE-CONSTRAINED GOAL PROGRAMMING MODEL FOR BANK LIQUIDITY MANAGEMENT

The bank liquidity-management model presented herein is formulated as a chance-constrained goal programming model thus allowing uncertainty to be incorporated into a lexicographic solution of the problem. This model plans both for the reserve-short and reserve-excess bank, describing appropriate investment or disinvestment action during the computation period. The testing of the model was performed on two banks, one a small rural bank with assets of about $25 million, and the other a large metropolitan bank with assets well in excess of $1 billion, with the actual bank results and the prescribed strategy of the model being compared over a one-year period. Additionally, sensitivity analysis was performed on the model, providing valuable information concerning the risk-return relationships associated with each management goal. It is hoped that this model will provide bankers with both an effective diagnostic and operational tool in the development of liquidity-management planning decisions.     

THE DOLLARS AND SENSE OF SIMULATION

This paper deals with the economics of computer simulation applied to problems in the operations management/industrial engineering area. First, the material in the literature dealing with the economics of simulation is summarized. However, the amount of information available on the economics of simulation is very small. A number of hypotheses are presented for this paucity of material. Since many managers operate in an environment in which costs and benefits are relevant to their decision-making process, the lack of data on the economics of simulation may be a barrier to the utilization of the technique. In an attempt to improve this situation, a framework for the collection and specification of the economics of simulation modeling projects is proposed as a method of urging management scientists to begin to collect and publish data on the economics of simulation projects.     

A MODEL FOR THE PREDICTION OF JUROR DECISIONS*

A logit model approach designed to assign a probability whether prospective jurors will favor the defendant or plaintiff in a case, as a function of perceived individual juror characteristics, is described in the context of a case situation. The model, which has been programmed on a hand-held computer, is designed for implementation in courtroom settings to help defense attorneys evaluate and select jurors in order to minimize the likelihood of large jury awards. Empirical tests of the model are also described.     

GREENING THE SERVICE PROFIT CHAIN: THE IMPACT OF ENVIRONMENTAL MANAGEMENT PRACTICES

This paper explores the relationship between environmental practices and performance in services and the impact of such practices on the external portion of the service profit chain. Using structural equation modeling, it tests the hypotheses developed with data from the European hospitality industry. The findings suggest that environmental practices are positively related to performance through the mediating effect of enhanced customer satisfaction and loyalty. The paper's contributions include: the conceptual development of the relationship between environmental practices and performance in services, the incorporation of environmental practices within the service profit chain, and the testing of their impact on customer satisfaction.