A COMPARISON OF LEARNING CURVE ANALYSIS AND MOVING AVERAGE RATIO ANALYSIS FOR DETAILED OPERATIONAL PLANNING*

This study tests the use of learning curve analysis for production planning at the detailed component level under various conditions, represented by factors of product turnover rate, learning rate, variance levels, and planning horizon length. It also presents an alternative to learning curve analysis that considers aggregation of cost data across time. This alternative is periodic revision of standard cost data using moving average forecasts to reflect productivity trends. Results of this study indicate that in most circumstances a moving average analysis can provide better estimates of short-term, detailed component operations costs than either a learning curve analysis or a standard analysis.     

德黑兰中期交通和运输研究

德黑兰综合交通运输研究开始开１９９４年，研究中收集了社会经济数据，网络特性和公交数据，完成了出行调查和交通容量的计算，出行产业，出行分布和方式选择都准确地采用了这些数据，在这些研究中，考虑到德黑兰市的规模和它相应的数据库以及具体的目的，选用ＥＭＭＥ／２作为软件工具进行需求预测和交通分配较为适合。德黑兰市交通模型则上经历了两个阶段；ａ目前状况的形成；ｂ通过确定和评价可选方案实现交通系统的优化和未来预     

Evidence on the Superiority of Analysts Quarterly Earnings Forecasts for Small Capitalization Firms*

Financial analysts provide information to support investment analysis and decisions for an ever increasing number of firms. As part of their services they also produce earnings forecasts for covered firms. While there has been much research investigating the determinants of financial analyst earnings forecast superiority for large, widely-followed firms, little research has focused on smaller firms. Until recently, these smaller firms have been largely ignored. This study focuses exclusively on small firms and provides evidence of differing behavior for such firms compared to results previously reported for large firms. Errors in quarterly earnings per share forecasts of small firms obtained from a univariate time-series model are also examined. Regression results indicate that time-series model parameters possess information content with respect to forecast accuracy for analyst-covered firms only. These results are obtained after controlling for firm size, model adequacy, and industry, quarter, and year effects. This suggests that analysts are more likely to cover small firms for which they are able to decipher information correlated with that impounded in the “shocks” in the quarterly earnings time series as captured by the time-series model parameters.     

AN ANALYSIS OF THE USEFULNESS OF DISAGGREGATED ACCOUNTING DATA FOR FORECASTS OF CORPORATE PERFORMANCE*

The purpose of this paper is to derive the conditions under which disaggregated accounting data contribute to more accurate forecasts of corporate performance. A comparison formula is derived and applied to actual data. The results obtained indicate that disaggregated data do not necessarily produce better forecasts of corporate performance than do aggregated data. The paper concludes with implications of the results to some reporting issues.     

Development of a Prediction Model for Hospital Closure Using Financial Accounting Data*

Numerous research studies have examined the use of financial accounting data in the prediction of corporate bankruptcy. Partly due to a lack of available data, however, little work has been done in developing a closure prediction model specifically for hospitals. Using cost reports from the Health Care Financing Administration and a sample of 71 closed hospitals and a matched sample of 71 open hospitals, the current study examines the relationship between 21 financial accounting ratios and hospital closure. Univariate logit results indicate that hospital closure is significantly related to 17 of the 21 ratios one year prior to closure. Results are also presented using a multivariate model, and for the relationships two years prior to closure. The current study provides information helpful to users in identifying financial variables which may be important indicators of hospital closure.     

An Information-Maximizing Interactive Procedure for Scenario Probability Elicitation

Various approaches have been proposed for determining scenario probabilities to facilitate long-range planning and decision making. These include microlevel approaches based on the analysis of relevant underlying events and their interrelations and direct macrolevel examination of the scenarios. The determination of a unique solution demands excessive consistency and time requirements on the part of the expert and often is not guaranteed by these procedures. We propose an interactive information maximizing scenario probability query procedure (IMQP) that exploits the desirable features of existing methods while circumventing their drawbacks. The approach requires elicitation of cardinal probability assessments and bounds for only marginal and first-order conditional events, as well as ordinal probability comparisons (probability orderings or rankings) of carefully selected scenario subsets determined using concepts of information theory. Guidelines for implementation based on simulation results are also developed. A goal program for handling inconsistent ordinal probability responses is also integrated into the procedure. The results of behavioral experimentation (which compared our approach to Expert Choice and showed that the IMQP was viable) compared favorably in terms of ease of use and time requirements, and works best for problems with a large number of scenarios. Design modifications to IMQP learned from the experiments, such as incorporating interactive graphics, are also in progress.