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This chapter develops a productivity analysis of the US pharmaceutical industry via Data Envelopment Analysis (DEA). This study concerns itself with 16 US pharmaceutical companies. The output variables are profit margin, operating margin, return on assets, and return on equity. The input variables are corporate workers and market capital. Since negative data appear in DEA, a directional distance approach was applied.

This chapter develops a productivity analysis of the New Jersey PPO Health Insurance Industry for 2018. The chapter concerns five New Jersey PPO insurance companies. The two output variables are claims paid and loss ratio. The two input variables include premiums collected and assets.

This chapter develops a productivity analysis of the US telecommunications industry using a data envelopment analysis (DEA) approach. The study concerns itself with eight telecommunications companies. Output variables used are market price, return on equity, and debt equity ratio. The input variables are sales to profit, return on equity, and debt ratio to capital.

This chapter develops a productivity analysis of the US consumer drug store business using data envelopment analysis. This study concerns itself with five major US consumer drug chains. The output variables used are profit, total revenue, and prescription revenues. The input variables are number of pharmacists, number of drug store assets, and capital equity.

Forecasting sales for an innovation before the product's introduction is a necessary but difficult task. Forecasting is a crucial analytic tool when assessing the business case for internal or external investments in new technologies. For early stage investments or internal business cases for new products, it is essential to have some understanding of the likely diffusion of the technology. Diffusion of innovation models are important tools for effectively assessing the merits of investing in technologies that are new or novel and do not have prima facie, predictable patterns of user uptake. Most new product forecasting models require the estimation of parameters for use in the models. In this chapter, we evaluate three techniques to determine the parameters of the Bass diffusion model by using an example of a new movie.

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

The research is directed toward the prediction of operating income within the MetLife Insurance Company. The operating income of the firm is the amount of profit realized from a firm’s own operation, as opposed to net income. The econometric model is based on 10 years of quarterly data (2004–2014). The explanatory variables used in this modeling effort are (1) stock price, (2) long-term borrowing, (3) capital surplus, (4) free cash flow, (5), S&P average, (6) GDP, and (7) CPI.

This chapter concerns itself with the development of a regression model for an executive compensation forecasting of the top-level executives of MetLife. The data observations consist of a list of 12 comparable corporations selected from comparable financial institutions. A set of 28 financial variables from each of the corporations is compiled as the data source of this regression model.