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No description available.

No description available.

Sales forecasting is an essential tool for the planning function of corporate restaurant management. Accurate sales forecasts allow functional areas, such as marketing, advertising, human resources, and finance, to effectively develop programs to advance the company. Examples of these programs include budgets, promotion and advertising campaigns, training programs, and capital equipment proposals. Research in restaurant sales forecasting will aid restaurant corporations in properly allocating resources for more efficient utilization. Utilizing a descriptive sales forecasting benchmarking model developed by Mentzer et al. (1996; 1999), and adapting the model into the restaurant industry, the research sought to determine the relationship that the dimensions of the sales forecasting benchmarking model (functional integration, approach, systems, and performance measurement) had with level of accuracy of the sales forecast and level of managers'; satisfaction with the sales forecasting process. The adapted model addressed two research questions. The first question was what is the relationship of the four dimensions of the sales forecasting benchmarking process (Mentzer et al., 1996; 1999) with the level of accuracy of the sales forecast in the commercial restaurant setting? The second question was what is the relationship of the four dimensions of the sales forecasting benchmarking process (Mentzer et al., 1996; 1999) with the level of managers' satisfaction with their sales forecasting process in the commercial restaurant setting? A qualitative research methodology combining McCracken's (1988) 4-step method of inquiry and Strauss & Corbin's (1990) grounded theory research methodology allowed investigation of this phenomena. Two propositions guided the research and a scheme was developed that allowed for analyzing the company participants based on the constructs of functional integration, approach, systems, and performance measurement, level of accuracy of the sales forecast and level of managers' satisfaction with the sales forecasting process. The analysis revealed that there was a relationship between the dimensions of the sales forecasting benchmarking model and the level of managers' satisfaction with the sales forecasting process. The analysis also revealed that the constructs of performance measurement and level of accuracy of the sales forecast might actually be one construct. Another dimension emerged, training, and scenarios were developed to relate training to the original dimensions. Recommendations were developed based on the research findings and hypotheses were developed based on the propositions. The findings suggest that there is a positive correlation between the dimension of the sales forecasting benchmarking model and the level of managers' satisfaction with the sales forecasting process. That is to say the more evolved a company may be in a dimension, the higher the level of managers' satisfaction with the sales forecasting process. / Ph. D.

This study investigated forecasting accuracy over time. Several quantitative and qualitative forecasting models were tested and a number of combinational methods was investigated. Six time series methods, one causal model, and one subjective technique were compared in this study. Six combinational forecasts were generated and compared to individual forecasts. A combining technique was developed. Thirty data sets, obtained from a market leader in the cosmetics industry, were used to forecast sales. All series represent monthly sales from January 1985 to December 1989. Gross sales forecasts from January 1988 to June 1989 were generated by the company using econometric models. All data sets exhibited seasonality and trend.

The purpose of this research is to analyze the impact that a sample of securities blended together would have upon the variance of the expected returns of an energy and a gold-minerals portfolio. A framework based on the Markowitz model, but solved linearly, has been constructed in which the optimal weight of each security in its respective portfolio is determined in order to minimize variance given the expected portfolio returns. The data elaborated for each stock (price, return and dividend) were on an annual basis for a period of 16 years and are the basis from which the projections of both the energy and the gold-minerals portfolio expected returns were derived. The results show that the variance in both portfolios is considerable, because stocks as a group show co-movement, meaning that stocks tend to do well or poorly as a group.

This study presents improved methods for long-term forecasting of mineral demands. Intensity of use, both in its simple, original form and as described by richer economic relations is one such method, particularly when intensity of use is estimated using rigorous statistical methods. Additionally, this dissertation explores the implications of the learning curve for long term forecasting of mineral demands. This study begins by considering the empirical evidence which applies when a learning curve is present. Then, if a learning pattern is present, the learning model is used to examine an economic measure for specified levels of economic activity. This dissertation also provides some empirical results on the learning curve in mineral industries and demonstrates how the learning model can be used as an economic forecasting tool. As an alternative to the intensity of use and learning models, there is a vector model, either using time varying coefficients or expressed as a transcendental function, to capture dynamics. This model estimates the time varying parameters from the vector space instead of the variable space. The major advantage of this model is that it honors correlations between variables. This is especially important in ex ante forecasting in which explanatory variables themselves must be forecast to obtain a forecast of the dependent variable.

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Statistics / Master / Master of Philosophy

The relationship between aqueous activity coefficients (log γ(w)) and different physico-chemical properties has been studied for a number of solutes by both empirical correlations as well as by applying existing theoretical models. The solute properties selected have been classified into three categories: geometrical, polar, and electrostatic. The solutes chosen were divided into two major groups: (a) Training Set. Structurally simple compounds, i.e., each containing only one functional group, and (b) Test Set. A series of drugs and pollutants covering a wide variety of functional groups. The Training Set is in turn formed by four sub-sets of structurally related solutes, each representative of typical data sets used in the literature for solubility studies. Linear relationships were found for polar and geometric parameters in agreement with those reported in the literature. However, although the overall correlations are good, the quality of the regressions among the sub-sets is not uniform. The generality of the relationships obtained with the Training Set was tested by applying the obtained expressions to estimate log γ(w) of the solutes of the Test Set. It was found that the parameters of the theoretical models are the only ones whose relationship with log γ(w) is maintained for both the Training and the Test sets. The theoretical models used are: octanol-water partition coefficient estimated by both Rekker's (parameter LOGP) and by Leo's (parameter PCLOGP) methods; the solubility group contributions method of Wakita et al. (1986) (parameter WAKITA); the Linear Solvation Energy Relationships model (parameter KAMLET), and the UNIFAC model. The theoretical approaches were evaluated based on two criteria: accuracy of predictions and range of applicability. The accuracy of predictions was quantitated by a prediction coefficient, P², which although analogous to regression coefficient (R²) is far less flexible. Prediction coefficient is sensitive not only to scatter of the predictions but also to the systematic errors of the model being tested. The range of applicability was quantitated by the fraction (f) of solutes within the data set for which estimates by the given methodology are possible. The Accuracy-Generality Product (AGP) defined as the product of P² and f was used as the overall criterion for evaluation. The results indicated that the quality of predictions of the theoretical models as determined by the AGP is PCLOGP > LOGP > WAKITA > UNIFAC > KAMLET, for both the Training and Test sets.