Gordon P. Wright

A Multibrand Stochastic Model Compounding Heterogeneous Erlang Timing and Multinomial Choice Processes

The model developed in this paper incorporates two submodels a purchase timing model which describes the occurrence over time of purchases of the product class and a multibrand stochastic choice model which specifies how any brand may be chosen on a given purchase occasion. The mathematical derivations obtained by combining both submodels lead to the identification of the formal connection between the aggregates of the market—in particular, market share, penetration, duplication and brand switching. The combining is done under the assumption of independence between the zero-order choice process and the Erlang purchase timing process. The model is fully determined when the following four types of parameters are known the market shares, mn, a measure of heterogeneity of the population in terms of choice, p, the order of the Erlang timing process, r, and two parameters which describe the distribution over the population of the purchase rate of the product class—a shape parameter k and a scale parameter c. An...

Process and Product Improvement in Manufacturing Systems with Correlated Stages

Manufacturing systems typically contain processing and assembly stages whose output quality is significantly affected by the output quality of preceding stages in the system. This study offers and empirically validates a procedure for 1 measuring the effect of each stages performance on the output quality of subsequent stages including the quality of the signal product, and 2 identifying stages in a manufacturing system where management should concentrate investments in process quality improvement. Our proposed procedure builds on the precedence ordering of the stages in the system and uses the information provided by correlations between the product quality measurements across stages. The starting point of our procedure is a computer executable network representation of the statistical relationships between the product quality measurements; execution automatically converts the network to a simultaneous-equations model and estimates the model parameters by the method of least squares. The parameter estimates are used to measure and rank the impact of each stages performance on variability in intermediate stage and final product quality. We extend our work by presenting an economic model, which uses these results, to guide management in deciding on the amount of investment in process quality improvement for each stage. We report some of the findings from an extensive empirical validation of our procedure using circuit board production line data from a major electronics manufacturer. The empirical evidence presented here highlights the importance of accounting for quality linkages across stages in a identifying the sources of variation in product quality and b allocating investments in process quality improvement.

A self-starting procedure for monitoring process quality in multistage manufacturing systems

Manufacturing systems typically contain processing and assembly stages whose output quality is significantly affected by the output quality of preceding stages. The deficiencies of using standard statistical process-monitoring procedures in such systems have been highlighted in the literature. This article proposes a procedure to monitor process and product quality in multistage systems. By accounting for the quality of the input to each stage, the procedure not only detects the presence of out-of-control conditions but also helps to identify the stages responsible for such departures. We extend previous research to the common case where the process parameters are unknown. An extensive performance study shows that the procedure is effective in detecting out-of-control conditions and that it convincingly outperforms existing methods. We illustrate the use of the procedure using production line data from a major electronics manufacturer. †Deceased

Integrated Modeling Environments in Organizations: An Empirical Study

Considerable attention in the information systems and management science literature has focused on computer-based modeling environments, sometimes called integrated modeling environments or model management systems. This research has been primarily concerned with suggesting features/components of modeling environments such as improved executable modeling languages for model creation, integration, and data representation; specialized database systems for managing model data; and customized model-solver software. However, there has been little (if any) empirical guidance offered in the literature about the specific needs of business and industry for computer-based integrated modeling environments. Using a data set compiled from a national survey of modelers (analysts) and model users (decision makers), we empirically investigate the validity of several of the key assumptions of modeling environment research reported in the literature, and examine the relationships between the modeling factors: data complexity, model complexity, modeling intensity, modeler/user requirements, and need for computer-based integrated modeling environments in organizations. Our empirical analysis of the data set shows that practitioners rank automated access to model data and automated error checking (e.g., model syntax and semantics checking) high as desirable components in modeling environments. We find that users prefer to have modeling environments linked to their current modeling and modeling-support software systems. Our findings further suggest that a high percentage of modelers and users are dissatisfied with the software systems they are currently using to support their modeling activities. Finally, a covariance structure analysis of the modeling environment factors clearly shows that: (a) model complexity has a direct positive effect on modeling intensity; (b) data complexity has an insignificant direct effect on modeling intensity, but has a negative effect on modeler/user requirements; and (c) modeler/user requirements have a direct positive effect on need for computer-based integrated modeling environments in organizations.

OR/SM: A Prototype Integrated Modeling Environment Based on Structured Modeling

This article describes the design and implementation of (OR/SM), a computerized modeling environment based on Structured Modeling. The uniqueness of OR/SM is in the following: (1) the use of oracle Tools and Database as the delivery platform; (2) automatic and interactive links to sas, a powerful and widely used commercial statistical analysis software system and optimization solver; and (3) an interactive link to qs (Quantitative Systems)—a commercial software package for solving a wide range of operations management models. Some other key features are: (1) automatic generation of relational database tables for model data; (2) interactive checking of model syntax and semantics; and (3) automatic generation of several reference documents. Examples from blending, inventory control, and marketing mix management are used to illustrate the capabilities of OR/SM.

The design and implementation of OR/SM: A prototype integrated modeling environment

This paper describes the design, implementation, and interaction of the processes of OR/SM, a computerized modeling environment built on ORACLE Tools and Database (OR) using Structured Modeling (SM) as the conceptual framework. Some of the key features of OR/SM include: (a) interactive checking of model syntax and semantics; (b) automatic gen-eration of relational database tables for model data; (c) automatic generation of several reference documents; (d) automatic and interactive links to SAS, a powerful and widely used commercial statistical analysis software system and optimization solver; and (e) an interactive link to QS (Quantitative Systems) - a commercial software package for solving a wide range of operations management models.

Structural models of brand loyalty with an application to the automobile market

Although several studies have found that past purchases are important determinants of a consumers current purchase, there is disagreement whether past purchases are capturing brand loyalty or consumer heterogeneity. This article addresses the question of consumer heterogeneity, explores the relationship between brand loyalty and singleton choice sets and estimates alternative structural models that are based on the common assumption that consumers are of two types: brand loyals and shoppers, who face the entire set of available choices. Employing data from a 1989 J. D. Power and Associates survey of new vehicle buyers, aggregate switching and logit captivity/DOGIT models are estimated and comparatively evaluated in holdout testing. Among the results, there is evidence that new vehicle purchasers are brand loyal but that brand loyalty may be overestimated if the model fails to control for availability constraints. Further, a parametrized logit captivity model finds that male and college-educated consumers are important determinants of singleton choice sets.

Testing for competitive submarkets

Abstract We extend some of the methods Urban, Johnson and Hauser (1984) have developed to the problem of testing for the existence of competitive submarkets in a market. A submarket or a group of products is said to be competitive if consumers are statistically more likely to purchase again in that submarket than would be predicted using an aggregate constant ratio criterion. Unlike the ujh procedure, our method (1) identifies the specific submarkets that are competitive, (2) uses an appropriate one-sided multivariate likelihood ratio test to account for the correlation between the submarket statistics and to identify competitive submarkets over time, and (3) uses purchase probabilities estimated from behavioral data. Our application to the coffee market shows that the proposed method has substantial validity and confirms the presence of competitive submarkets.

An Optimal Prevention and Detection Model for Pollution Patrol

ABSTRACT This paper presents a flight scheduling model for sensor (infra-red and ultra-violet) equipped aircraft whose mission is the detection and prevention of harbor and coastal oil and hazardous material pollution. The objective of the model is to maximize the expected number of pollution incidents detected per pollution flight. The model requires, as an input, parameters representing probabilities of pollution incidents occurring for different geographical sectors. These parameters are estimated using, coastwise and harbor, petroleum and hazardous material shipping statistics. The shipping statistics considered include movement of petroleum products along the Atlantic, Pacific, and Gulf coasts as well as the movement of such commodities in the intra-coastal waterway system and the Great Lakes. Particular port characteristics and past pollution incident statistics are also used to estimate required parameters of the scheduling model. The constraints in the model concern the availability of aircraft, f...

Using Linear Programming to Design Oil Pollution Detection Schedules

Abstract This paper presents a flight scheduling system for sensor (infra-red and ultra-violet) equipped aircraft whose mission is the detection and prevention of harbor and coastal oil and hazardous material pollution. The aircraft scheduling model is a form of the stochastic traveling salesman problem which can be solved using linear programming. The objective of the model is to maximize the expected number of pollution incidents detected per pollution flight. The model requires as input parameters representing probabilities of pollution incidents occurring for different geographical sectors. These parameters are estimated using coastal and harbor shipping statistics for petroleum and hazardous material. The shipping statistics considered include movement of petroleum products along the Atlantic, Pacific and Gulf coasts as well as the movement of such commodities in the intra-coastal waterway system and the Great Lakes. Particular port characteristics and past pollution incident statistics are also used...