Demand forecasting is an important step of a company’s supply chain management process, allowing companies to project their needs for different components that are used in the final product. This is even more important in emerging industries with job order (or project-based) products where historical demands do not exist and components may not be readily available or may involve a long lead time. Developing a demand forecasting model which accurately projects the needs of components for a company can decrease costs while decreasing overall lead times of final products. This demand forecast model takes into account projected component needs along with the likelihood of successfully winning a project bid. The model is extended to four different demand forecasting formulas incorporating different use of the winning probabilities. Historical results are then used to compare the methods and their advantages and disadvantages are discussed. / text

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

This dissertation reconciles ostensibly conflicting evidence from prior research about the association between information uncertainty and post-earnings-announcement drift (PEAD). According to traditional PEAD studies there should be a positive association between PEAD and uncertainty about the implication of an earnings announcement for future earnings, referred to in this dissertation as "information uncertainty." Empirical studies have documented both positive and negative associations, however. In particular, studies that use analyst forecast dispersion as a proxy for information uncertainty report a negative association between information uncertainty and PEAD. Although the authors of those studies argue that their results are consistent with behavioral finance theories, a negative association between information uncertainty and PEAD is troubling because it is not consistent with the notion that more reliable information improves market efficiency. In fact, previous empirical studies that use proxies for information uncertainty other than analyst forecast dispersion find a positive association between information uncertainty and PEAD. This study argues that the negative association between analyst forecast dispersion and PEAD can be explained by "herding" behavior immediately after earnings announcements. I introduce an analyst-based proxy for information uncertainty that mitigates the effects of herding on forecast dispersion. I find that, after controlling for the effect of herding, there is a positive association between information uncertainty and PEAD even when analyst forecasts are used to measure information uncertainty.

No description available.

published_or_final_version / Geography and Geology / Master / Master of Philosophy

Agronomists are constantly experimenting with improved plot techniques that can enable them to make more precise inferences from field data. This dissertation reports two investigations: (a)evaluation of the yield potentials of some barley genotypes using two non-traditional methods, and (b)comparative assessment of the two methods. Two separate but related experiments were conducted. The nearest neighbor procedure was the first. The use of spaced-plant parameters to predict yield at normal commercial density was the second experiment. Four variations of the nearest neighbor procedure were examined. For each version the plant to be evaluated always occupied the center of the rectangle of nearest neighbors. Evaluation consisted of yield adjustments where the yield of the individual plot was compared with the mean of its nearest-neighbor genotypes. Individuals were ranked according to those deviations. Unadjusted yield data were also ranked. The error mean squares derived from ranks of various configurations were compared inter se and with that from unadjusted yield. Nearest neighbors always showed a smaller error variance than the unadjusted data. Of these the first nearest neighbors produced the smallest mean square for error and, hence, the highest efficiency of genotype ranking. This procedure substantially controlled for the effects of soil heterogeneity. Averages of individual ranks were computed and related to respective genotypes (entries). For each procedure the top 25% which fell in the upper bracket of the yield curve were considered to possess high yield potentials. This method of adjustment, ranking, averaging, and selection was applied to the unadjusted data as well as to each of the nearest neighbor procedures. Unadjusted mean yield and nearest neighbor techniques were contrasted. The rankings generated by the two procedures were similar but not identical. The significantly lower error variance of the nearest neighbor adjustments indicated that those should be used instead of unadjusted mean yield when precision is needed. However, unadjusted mean yield ranking provides broad identification of high yielding genotypes, and is a simpler statistical procedure. The second experiment examined the effectiveness of yield and yield components of spaced plants in predicting yield at normal cultural density. It was conducted for two years using primarily trend analysis. Results for individual years showed that none of the metric components of spaced plants was a satisfactory predictor of crop yield. However, when data were pooled over the whole experimental period, most of the yield components of spaced plants showed highly significant correlations with crop yield. Regression models were developed from the components which demonstrated good prediction of crop yield. Under the conditions of this study, productivity (biological yield or total weight) was revealed by all the analyses as the most important spaced-plant component for predicting yield at higher densities.

No description available.

No description available.

No description available.

No description available.