This dissertation develops methods to minimize recommendation error costs when inputs to a rule-based expert system are prone to errors. The problem often arises in web-based applications where data are inherently noisy or provided by users who perceive some benefit from falsifying inputs. Prior studies proposed methods that attempted to minimize the probability of recommendation error, but did not take into account the relative costs of different types of errors. In situations where these differences are significant, an approach that minimizes the expected misclassification error costs has advantages over extant methods that ignore these costs. Building on the existing literature, two new techniques - Cost-Based Input Modification (CBIM) and Cost-Based Knowledge-Base Modification (CBKM) were developed and evaluated. Each method takes as inputs (1) the joint probability distribution of a set of rules, (2) the distortion matrix for input noise as characterized by the probability distribution of the observed input vectors conditioned on their true values, and (3) the misclassification cost for each type of recommendation error. Under CBIM, for any observed input vector v, the recommendation is based on a modified input vector v' such that the expected error costs are minimized. Under CBKM the rule base itself is modified to minimize the expected cost of error. The proposed methods were investigated as follows: as a control, in the special case where the costs associated with different types of errors are identical, the recommendations under these methods were compared for consistency with those obtained under extant methods. Next, the relative advantages of CBIM and CBKM were compared as (1) the noise level changed, and (2) the structure of the cost matrix varied. As expected, CBKM and CBIM outperformed the extant Knowledge Base Modification (KM) and Input Modification (IM) methods over a wide range of input distortion and cost matrices, with some restrictions. Under the control, with constant misclassification costs, the new methods performed equally with the extant methods. As misclassification costs increased, CBKM outperformed KM and CBIM outperformed IM. Using different cost matrices to increase misclassification cost asymmetry and order, CBKM and CBIM performance increased. At very low distortion levels, CBKM and CBIM underperformed as error probability became more significant in each method's estimation. Additionally, CBKM outperformed CBIM over a wide range of input distortion as its technique of modifying an original knowledge base outperformed the technique of modifying inputs to an unmodified decision tree.

The aim of this thesis is to analyze the costs of the chosen company and through the analysis suggest provisions which will lead to costs optimization. The practical part starts with introducing the company, which was chosen for this thesis. Then the analysis of operating and financial cost is made. The optimization suggestions of selected costs are based on this analysis.

Increasing competition and regulation are raising the pressure on manufacturing organisations to innovate their products. Innovation is fraught by significant uncertainty of whole product life cycle costs and this can lead to hesitance in investing which may result in a loss of competitive advantage. Innovative products exist when the minimum information for creating accurate cost models through contemporary forecasting methods does not exist. The scientific research challenge is that there are no forecasting methods available where cost data from only one time period suffices for their application. The aim of this research study was to develop a framework for forecasting cost uncertainty using cost data from only one time period. The developed framework consists of components that prepare minimum information for conversion into a future uncertainty range, forecast a future uncertainty range, and propagate the uncertainty range over time. The uncertainty range is represented as a vector space representing the state space of actual cost variance for 3 to n reasons, the dimensionality of that space is reduced through vector addition and a series of basic operators is applied to the aggregated vector in order to create a future state space of probable cost variance. The framework was validated through three case studies drawn from the United States Department of Defense. The novelty of the framework is found in the use of geometry to increase the amount of insights drawn from the cost data from only one time period and the propagation of cost uncertainty based on the geometric shape of uncertainty ranges. In order to demonstrate its benefits to industry, the framework was implemented at an aerospace manufacturing company for identifying potentially inaccurate cost estimates in early stages of the whole product life cycle.

Making good supply management decisions is essential to competing in the global market, as these decisions often account for more than 60% of the average company's total costs. The purpose for this single case study was to explore the strategy that a large manufacturing firm in northeast Ohio used to identify costs when making effective purchasing decisions. The total cost of ownership (TCO) theory was the conceptual framework for the study. The data collection included a semistructured interview with a senior level supply manager and a focus group consisting of mid-level supply managers. Member checking provided verification of the interpreted participants' responses. Methodological triangulation included 2 company documents pertinent to the supply management department that resulted in 4 emerging themes: identifying total costs, tools for implementing TCO, supplier rating and management, and detailed recordkeeping. The findings of this study revealed a simpler approach to capturing and organizing data than was acknowledged in the literature reviewed. The findings showed TCO supported purchasing decisions that often resulted in domestically or regionally purchased products rather than offshore buys. Therefore, reassessment of true total costs by senior manufacturing supply managers might impact social change as more procurement decisions forego sourcing offshore and bring manufacturing of products back to local communities.

This thesis investigated the cost-effectiveness of a 12-month supervised aerobic walking program with or without a behavioural intervention and an educational pamphlet, compared to an unsupervised/self-directed educational pamphlet intervention, among individuals with moderate osteoarthritis (OA) of the knee. Analyses included an economic evaluation to assess the cost effectiveness of the two walking interventions from both the societal and Canadian provincial/territorial health care payer perspectives. A value of information analysis exploring the potential value of future research was also performed. Results revealed that the unsupervised/self-directed intervention was the most cost-effective approach given that it cost the least to implement and participants had higher quality-adjusted life years (QALYs). Walking, either supervised in a community setting, or unsupervised in a setting such as the home, may be a favourable non-pharmacological option for the management of OA of the knee. The thesis concludes with a policy discussion relating to the funding of non-pharmacological therapies.

This dissertation investigates the relation between firm disclosure, analyst forecast bias, and the cost of equity capital (COEC). Since analyst forecast bias is associated with both implied COEC estimates and disclosure, it is important to control for or remove it from COEC estimates when estimating the relation between disclosure and ex ante expected returns. I begin my analysis by predicting and removing systematic ex ante bias from analyst forecasts to produce de-biased analyst forecasts that better proxy for the market’s ex ante earnings expectations. I use these de-biased analyst forecasts to produce estimates of ex ante expected returns, both at the portfolio- and the firm-level. In addition, I develop a novel estimate of ex ante expected returns by applying Vuolteenaho’s (2002) return decomposition framework to ex post realized returns and accounting data. Finally, using several techniques to control for analyst forecast bias and self-selection bias, I find theoretically consistent evidence of a negative association between regular disclosure and ex ante expected returns. I predict and show that inferences can change when analyst forecast bias is controlled for.

This dissertation investigates the relation between firm disclosure, analyst forecast bias, and the cost of equity capital (COEC). Since analyst forecast bias is associated with both implied COEC estimates and disclosure, it is important to control for or remove it from COEC estimates when estimating the relation between disclosure and ex ante expected returns. I begin my analysis by predicting and removing systematic ex ante bias from analyst forecasts to produce de-biased analyst forecasts that better proxy for the market’s ex ante earnings expectations. I use these de-biased analyst forecasts to produce estimates of ex ante expected returns, both at the portfolio- and the firm-level. In addition, I develop a novel estimate of ex ante expected returns by applying Vuolteenaho’s (2002) return decomposition framework to ex post realized returns and accounting data. Finally, using several techniques to control for analyst forecast bias and self-selection bias, I find theoretically consistent evidence of a negative association between regular disclosure and ex ante expected returns. I predict and show that inferences can change when analyst forecast bias is controlled for.

Escalation of right-of-way (ROW) costs have been shown to be a prime contributor to project cost escalation in the highway industry. Two problems contribute to ROW cost escalation: 1) the ROW cost estimation and cost estimate management process generally lacks structure and definition as compared to other areas of cost estimation; and 2) there is a lack of integration and communication between those responsible for ROW cost estimating and those responsible for general project cost estimating. The research for this thesis was preceded by a literature review to establish the basis for the study. Data collection was completed through interviews of seven state highway agencies (SHAs) and two local public agencies (LPAs). The findings of the research are presented in a set of ROW flowcharts which document the steps, inputs, and outputs of the ROW cost estimation and cost estimate management process. Three ROW cost estimates and a cost management process take place throughout project development. An effort was made from the onset of the research to relate the ROW cost estimating and cost estimate management process to the first four project development phases (planning, programming. preliminary design, and final design). There are five flowcharts produced as a result of this research: 1) an agency-level flowchart showing all cost estimates and the interaction of ROW with the project development process; 2) a conceptual ROW cost estimating flowchart which depicts the required steps during planning; 3) a baseline ROW cost estimating flowchart which depicts the required steps during programming; 4) an update ROW cost estimating flowchart which depicts the required steps during preliminary design to include a cost estimate management loop; and 5) a ROW cost management flowchart which depicts the required steps during final design. Although selected SHA contacts provided input following the development of the flowcharts, the flowcharts were only validated to a limited extent due to time and budget constraints. These flowcharts attempt to address the two contributing problems to ROW cost escalation by providing structure to the ROW cost estimation process and by developing the ROW process flowcharts linked to the project development process. Based on the input provided by SHA contacts, the flowcharts appear to have the potential to provide guidance to SHAs in improving the accuracy of ROW cost estimates through addressing these two problems.

Climate change and its mitigation is rapidly becoming an item of social concern. Climate change mitigation involves reduction of atmospheric greenhouse gas concentrations through emissions reduction and or sequestration enhancement (collectively called offsets). Many have asked how agriculture and forestry can participate in mitigation efforts. Given that over 80 percent of greenhouse gas emissions arise from the energy sector, the role of agriculture and forestry depends critically on the costs of the offsets they can achieve in comparison with offset costs elsewhere in the economy. A number of researchers have examined the relative offset costs but have generally looked only at producer level costs. However there are also costs incurred when implementing, selling and conveying offset credits to a buyer. Also when commodities are involved like bioenergy feedstocks, the costs of readying these for use in implementing an offset strategy need to be reflected. This generally involves the broadly defined category of transaction costs. This dissertation examines the possible effects of transactions costs and storage costs for bioenergy commodities and how they affect the agriculture and forestry portfolio of mitigation strategies across a range of carbon dioxide equivalent prices. The model is used to simulate the effects with and without transactions and storage costs. Using an agriculture and forestry sector model called FASOMGHG, the dissertation finds that consideration of transactions and storage costs reduces the agricultural contribution total mitigation and changes the desirable portfolio of alternatives. In terms of the portfolio, transactions costs inclusion diminishes the desirability of soil sequestration and forest management while increasing the bioenergy and afforestation role. Storage costs diminish the bioenergy role and favor forest and sequestration items. The results of this study illustrate that transactions and storage costs are important considerations in policy and market design when addressing the reduction of greenhouse gas concentrations in climate change related decision making.

Nuclear energy is a growing field worldwide due to its wide range of applications in various walks of life. It, however, deals with radioactive materials, specifically special nuclear material, which, if misused, could result in catastrophic consequences. In order to protect this precious resource and ensure its use for the good of mankind, safeguard systems are more important than ever. Current Market solutions are wide ranged but have a large number of disadvantages, some of which include high cost, constant updates, and incomplete efforts. The rising need of a cost effective, efficient, and integrated radioactive safeguard system serves as motivation for the solution outlined in this thesis. The thesis outlines a solution structured around the three pillars of the international safeguards program, namely, visual surveillance and motion detection, containment analysis, and non-destructive analysis. The hardware around each of these pillars work together with a clean and user-friendly application to provide a secure safeguards system that is both flexible and extensible.