A Learning Curve Analysis of Expert System Use*

In this article, we study how an expert system affects novice problem solving in a financial risk analysis domain. We demonstrate that novice performance is improved after exposure to an expert system. Further, we show that novice performance continues to improve when the system is withdrawn. By comparing learning curves for people with exposure to those without, we can assess how much the system has benefitted its users. We demonstrate a quantitative methodology to measure the increment of learning due to the use of an information technology. We also explore the issue of how expertise is transferred from the system to the user.     

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.     

Integrative Cycle Scheduling Approach for a Capacitated Flexible Assembly System*

This study revisits the traditional single stage, multi-item, capacitated lot-sizing problem (CLSP) with a new integrative focus on problem structuring. Unlike past research, we develop integrative cycle scheduling approaches which simultaneously address lot-sizing, capacity, and sequencing issues. Our purposes are to (1) explore the effect of sequencing on inventory levels, (2) examine the problem of infeasibility in the economic lot scheduling problem (ELSP), and (3) provide a simple methodology of generating low-cost cycle schedules in an environment with discrete shipping, dynamic demands, limited capacity, zero setup cost, and sequence-independent setup times. Our procedures are compared to benchmark cycle scheduling approaches in terms of both inventory cost and computation time under different demand scenarios, using the operating data from a flexible assembly system (FAS) at the Ford Motor Company's Sandusky, Ohio plant.     

The Impact of Learning and Labor Attrition on Worker Flexibility in Dual Resource Constrained Job Shops

This study investigates the impact of worker learning, worker flexibility, and labor attrition on the system performance of a dual resource constrained (DRC) job-shop. The effects of learning and labor attrition have not been previously addressed in DRC literature. Results from the study, consistent with previous literature, show that the greatest benefits are achieved when inter-departmental worker flexibility is incrementally introduced into the system. In addition, the learning environment, which depends on the initial processing time of jobs and the learning rates of workers, is shown to impact the acquisition of flexibility. The study also shows that the impact of labor attrition on system performance under certain shop conditions may be significant.     

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.     

Standard Container Size Discounts and the Single-Period Inventory Problem*

This paper analyzes the problem of choosing the optimal order quantity and its associated number of standard containers making up the order for single-period inventory models under standard container size discounts. A range is determined that contains the optimal order quantity. Two algorithms are presented. The first algorithm solves the general case in which there is no restriction on the types of containers included in an order. The second algorithm solves a more restricted policy that requires the buyer to accept the order with successively smaller container sizes.     

Graphical Presentations of Data Envelopment Analyses: Management Implications from Parallel Axes Representations*

The conversion of multivariate data analysis into information that managers can readily use in their decision making has always been a challenging task. In this paper we provide a parallel axis representation of multivariate data and demonstrate how this representation may be used as a tool for implementing the results of data envelopment analysis. We use an analysis of the efficiency of electricity generating plants to illustrate the technique.     

A Probabilistic Dealing Strategy

A probabilistic dealing strategy is proposed which allows all premium brands in an established market to earn nonnegative profits without cooperation. Following the strategy, brands take turns attracting deal-responsive customers. Relative to a reactive competitive strategy, the proposed strategy improves the positions of all premium brands. With use of the strategy, average deal sizes are positively related to a brand's market share, the proportion of quality conscious customers, the proportion of informed customers, the span of regular prices in a market, and the range of customers' acceptable prices.