An information-processing perspective is adopted for analyzing the judgments of individual employment interviewers in a corporate setting. Linear policy capturing equations were estimated from three interviewers' ratings of 120 job applicants in live and audiotaped interviews. The equations were evaluated across interviewers to identify sources of predictive validity and consistency in information use. In competition with the interviewers from whom they were derived, regression models displayed higher predictive validities in a majority of instances. Following training on selected rating dimensions, interviewers' predictive validities improved. In addition, after interviewer training the regression models of the training dimensions yielded higher predictive validities than all three interviewers. The results suggest specific directions for enhancing the effectiveness of interviewing in the employee selection process.

Research reports of others indicate that many manufacturing organizations experience (a) the probiem of aggregate planning, and (b) experience systematic productivity changes throughout the "life" of a product. Methods for resolving (a) and for quantifying (b) have been developed and applied independently in the operations management literature. All current aggregate planning models are suitable only for constant productivity situations. The current research integrates (a) and (b) into a single computer-based model which permits the development of aggregate-output plans in the face of changing, productivity. The model requires reformulation of traditional aggregate planning methods to incorporate changes in productivity and thereafter solves the reformulated planning problem using direct-computer search. The potential significance of the model is demonstrated by generating a series of aggregate plans for various learning rates. These plans are then used to develop manpower schedules, for cash flow analysis, and for making product pricing decisions.

The feasibility of applying the "bootstrapping" methodology to the securities analyst's task of estimating future returns of common stocks is evaluated. Five analysts each estimated the returns of 35 securities on the basis of 22 information cues, selected independently by nonparticipating analysts. Stepwise regression was used to estimate bootstrapping models and actuarial models for all analysts. Using a subset of IS cross-validation securities, bootstrapping and actuarial models generally were superior to the analysts. The performance of a "composite" analyst was superior to the performances of the five participants. Results indicate the desirability of incorporating linear models into the analyst's judgmental task.In recent years two distinct but related approaches to the study of human judgment have been used. The first approach emphasizes the discovery of explicit descriptions of the form of the decision maker's judgment process. The objective of such research is to lay out in precise terminology (usually mathematical) the decision maker's process of integrating the available information into a conclusion. These efforts have resulted in complex simulation models (

A standardized approach to selecting a simple sequencing rule for decentralized application throughout a job shop is developed and illustrated. The sequencing rule is a linear combination of decision factors, each of which is initially assigned a relative weighting. The rule is then used to determine the priority of each job in the queues, and resulting shop costs are determined by computer simulation. The coefficients of the priority function are thereafter modified by a patterned search procedure to find priority coefficients that minimize expected cost per order for a specified cost structure. The cost structure is a combination of multiple response measures for the shop. Rather than leading to a "single best rule" for all job shops, the approach is a "method for finding" a sequencing rule that performs well in any specific job shop situation.

Spical forecast-error measures such as mean squared error, mean absolute deviation, and bias generally are accepted indicators of forecasting performance. However, the eventual cost impact of forecast errors on system performance and the degree to which cost consequences are explahed by typical error measures have not been studied thoroughly. The present paper demonstrates that these typical error measurn often arc not good predictors of cost consequences in material requirements plarming (MRP) settings. MRP systems rely directly on the master production schedule (MPS) to specify gross requirements These MRP environments receive forecast cmrs indirectly when the errors create inaccuracies in the MPS.Our study results suggest that within MRP environments the predictive capabilities of forecast-error measures are contingent on the lot-sizing rule and the productcomponents structure When forecast errors and MRP system costs are coanalyzed, bias emerges as having reasonable predictive ability. In further investigations of bias, loss functions are evaluated to explain the MRP cost consequences of forecast errors. Estimating the loss functions of forecast errors through regression analysis demonstrates the superiority of loss functions as measures over typical forecast error measures in the MPS.Subject Alaas: F m t i n g , Materhl Requlnmcnts Pbnning, prrfonnancc Evaluation, and Simulation.