Constrained Forecasting: Some Implementation Guidelines

Constrained 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.     

Developing Measures for Assessing the Organizational Impact of Information Technology: A Comment on Mahmood and Soon's Paper

In a recent study, Mahmood and Soon [26] identified and operationalized a set of variables that can be potentially impacted by information technology. The lack of factor analysis to verify the dimensionality of different variables is a notable omission in the study. This paper describes and employs the limited-information factor analysis approach and the minimum average partial criterion for testing the unidimensionality of different variables in the Mahmood and Soon study. While reconfirming the construct validity of eight variables, our results question the unidimensionality of two original variables. Further testimony is provided in favor of our results by citing previous research studies.     

Two-Group Classification Using Neural Networks*

Artificial neural networks are new methods for classification. We investigate two important issues in building neural network models; network architecture and size of training samples. Experiments were designed and carried out on two-group classification problems to find answers to these model building questions. The first experiment deals with selection of architecture and sample size for different classification problems. Results show that choice of architecture and choice of sample size depend on the objective: to maximize the classification rate of training samples, or to maximize the generalizability of neural networks. The second experiment compares neural network models with classical models such as linear discriminant analysis and quadratic discriminant analysis, and nonparametric methods such as k-nearest-neighbor and linear programming. Results show that neural networks are comparable to, if not better than, these other methods in terms of classification rates in the training samples but not in the test samples.     

A Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

A Comment on Second-Order Polynomial Regression Models

In a recent article, Chatterjee and Greenwood [1] addressed the problem of multicollinearity in polynomial regression models. They noted that there is a high correlation between X and X²; therefore, a second-order polynomial model suffers the consequences of collinearity. Chatterjee and Greenwood [1] suggested a method they believe will overcome the problem. The contention of the present comment is that the suggested method accomplishes nothing and, indeed, has the potential to lead the unwary researcher to the wrong inference and misinterpretation of his results.     

On Sampling Error in Market Share Estimates*

The relative error in the usual estimator of a brand's market share is reformulated in terms of marketing parameters. Such error is shown to be influenced in an important way by market penetration, as well as by variation in brand and product category volume. Of particular interest is the result that the relative error does not depend on the actual share level. Using data from a marketing research firm that supplies share estimates to the health products industry, we find that the relative error may be substantial even when a large sample is available. An upper bound on this relative error is obtained using marketing parameters that can frequently be measured using industry data and a company's internal records, thus reducing the level of judgmental input required in the planning of sample surveys.     

A TIME-SERIES APPROACH FOR CONSTRUCTING EXPECTATIONS MODELS*

This article presents an efficient way of dealing with adaptive expectations models—a way that makes use of all the information available in the data. The procedure is based on multiple-input transfer functions (MITFs): by calculating lead and lag cross correlations between innovations associated with the variables in the model, it is possible to determine which periods have the greatest effects on the dependent variable. If information about k periods ahead is required, fitted values for the expectation variables are used to generate k-period-ahead forecasts. These in turn can be used in the estimation of the transfer function equation, which not only contains the usual lagged variables but also allows for incorporation of lead-fitted values for the expectation variables. The MITF identification and estimation procedures used are based on the corner method. The method is contrasted with the Almon distributed-lag approach using a model relating stock market prices to interest rates and expected corporate profits.     

ASSESSING ENVIRONMENTAL SCANNING AT THE SUBUNIT LEVEL: A MULTITRAIT-MULTIMETHOD ANALYSIS*

Environmental scanning activities of over 400 top management subunits in 108 European manufacturing firms provided the data base for this research. Four traits (constructs) of scanning using three methods (interest, frequency, and time) were examined. A confirmatory factor analysis approach to multitrait-multimethod (MTMM) data was employed. Verification of the reliability as well as convergent and discriminant validity of the scanning scales are reported and discussed. The viability of confirmatory factor analysis in providing a precise analysis of partitioning variance according to trait, method, and error is demonstrated.     

A NOTE ON THE USE OF SELECTED NONFINANCIAL RATIO VARIABLES TO PREDICT SMALL-BUSINESS LOAN PERFORMANCE*

Small business loan applications have not been evaluated successfully by traditional methods. This paper explores the possibility of using three types of nonfinancial ratio variables (owner, firm, and loan characteristics) to predict whether a small business will pay off or default its loan. The owner and loan variables were better predictors of loan success than the firm variables.     

A VARIANCE-REDUCTION STRATEGY FOR RSM SIMULATION STUDIES

The application of optimization techniques in digital simulation experiments is frequently complicated by the presence of large experimental error variances. Two of the more widely accepted design strategies for the resolution of this problem include the assignment of common pseudorandom number streams and the assignment of antithetic pseudorandom number streams to the experimental points. When considered separately, however, each of these variance-reduction procedures has rather restrictive limitations. This paper examines the simultaneous use of these two techniques as a variance-reduction strategy in response surface methodology (RSM) analysis of simulation models. A simulation of an inventory system is used to illustrate the application and benefits of this assignment procedure, as well as the basic components of an RSM analysis.