Academic literature on the topic 'Cost analysis tools'

nÃo hÃ<br>O setor da construÃÃo civil, notadamente o subsetor de edificaÃÃes, tem experimentado, no decorrer dos Ãltimos anos, um crescimento dos custos de produÃÃo acima dos indexadores tradicionais de inflaÃÃo. Adicionalmente a este fato, observou-se um aumento da concorrÃncia e uma consequente necessidade de uma gestÃo de custos mais eficiente e assertiva. O presente trabalho tem como finalidade a anÃlise da relaÃÃo entre a implantaÃÃo de tÃcnicas de planejamento e de controle de custos oriundas do gerenciamento de projetos e os resultados operacionais dos projetos de uma organizaÃÃo do ramo de construÃÃo civil. Trata-se de uma pesquisa exploratÃria com abordagens qualitativa e quantitativa, utiliza como estratÃgia de pesquisa um estudo de caso. Foram utilizados como instrumentos de coleta um grupo focal e anÃlise documental. Seis tÃcnicas foram consideradas de alta importÃncia para os resultados dos projetos da organizaÃÃo: Estimativa bottom-up; Gerenciamento do valor agregado; Estimativa paramÃtrica; Software de gerenciamento de projetos; Software de estimativa de gerenciamento de projetos e previsÃo. Estas ferramentas devem ser priorizadas na implantaÃÃo de gestÃo de custos de organizaÃÃes do setor de construÃÃo civil. Verificou-se que as previsÃes orÃamentÃrias foram mais precisas apÃs a implantaÃÃo de ferramentas de gestÃo de custos. Os desvios encontrados nos projetos que nÃo experimentaram as tÃcnicas de gestÃo de custos sÃo suficientes para comprometer o resultado financeiro dos projetos. O valor da construÃÃo por Ãrea equivalente de construÃÃo permaneceu estÃvel, evidenciando que outros fatores podem influenciar de forma significativa custo unitÃrio.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 81-84).<br>AM expands the design space in an unprecedented manner, as it can allow complex internal geometries, support multiple materials or structural gradients, significantly reduce lead times for small-batch production, and enable mass customization [1]. However, the adoption of AM in industry is hindered by our lack of design knowledge and inability to navigate the myriad considerations required to reliably produce high quality AM components economically. To quantitatively assess the tradeoffs between build rate, resolution and cost for AM processes, we present a physics-based rate and cost estimator for scanning laser based AM. The model takes a mesh representation of the part design as input, and uses a parametrized model of the rate-limiting physics of the build process to estimate the part-specific build time [2] [3]. From this build time estimate, per-part cost is calculated using a quantity-dependent activity-based model [4]. The model thus enables parametric analysis of tradeoffs between part quality (e.g., resolution), throughput, and cost. Additionally, we develop an analytical model to quantify the number of melting cycles the part undergoes during the print process as a metric of print quality. Integrated with our physics-based build time estimator, we articulate the tradeoff between build rate and print quality as a direct function of material properties, machine specifications, and print parameter selection. By conceptualizing and quantifying the relationships between part design, manufacturability, and cost, the computational design and decision-making tools developed here will enable optimal use of AM in real-world, production contexts. Given the complexity of designing for AM, these results produce valuable insight into otherwise complicated relationships between rate, cost and quality for SLM. For industry, this work will enable faster, cost-effective product production by identifying the most desirable print parameter sets.<br>by Kaitlyn Elizabeth Gee.<br>S.M.<br>S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation was to develop enhanced LCCA models using soft computing (mainly fuzzy logic) techniques. The proposed models use available "real-world" information to forecast life-cycle costs of competing maintenance and rehabilitation strategies and support infrastructure management decisions. A critical review of available soft computing techniques and their applications in infrastructure management suggested that these techniques provide appealing alternatives for supporting many of the infrastructure management functions. In particular, LCCA often utilizes information that is uncertain, ambiguous and incomplete, which is obtained from both existing databases and expert opinion. Consequently, fuzzy logic techniques were selected to enhance life-cycle cost analysis of transportation infrastructure investments because they provide a formal approach for the effective treatment of these types of information. The dissertation first proposes a fuzzy-logic-based decision-support model, whose inference rules can be customized according to agency's management policies and expert opinion. The feasibility and practicality of the proposed model is illustrated by its implementation in a life-cycle cost analysis algorithm for comparing and selecting pavement maintenance, rehabilitation and reconstruction (MR&R) policies. To enhance the traditional probabilistic LCCA model, the fuzzy-logic-based model is then incorporated into the risk analysis process. A fuzzy logic approach for determining the timing of pavement MR&R treatments in a probabilistic LCCA model for selecting pavement MR&R strategies is proposed. The proposed approach uses performance curves and fuzzy-logic triggering models to determine the most effective timing of pavement MR&R activities. The application of the approach in a case study demonstrates that the fuzzy-logic-based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the ambiguous uncertainty inherent to the process. Finally, the research establishes a systematic method to calibrate the fuzzy-logic based rehabilitation decision model using real cases extracted from the Long Term Pavement Performance (LTPP) database. By reinterpreting the model in the form of a neuro-fuzzy system, the calibration algorithm takes advantage of the learning capabilities of artificial neural networks for tuning the fuzzy membership functions and rules. The practicality of the method is demonstrated by successfully tuning the treatment selection model to distinguish between rehabilitation (light overlay) and do-nothing cases.<br>Ph. D.

<p>The purpose of this thesis is to develop a model for assessing cost effectiveness of applying lean tools. The</p><p>model consists of eight phases: it starts by understanding customers' requirements using Voice of Customer</p><p>(VOC) and Quality Function Deployment (QFD) tools. In phase 2, the current state of plant is assessed</p><p>using lean profile charts based on Balanced Scorecard (BSC) measures. In phase 3 and phase 4,</p><p>identification of critical problem(s) and generating of improvement suggestion(s) are performed. Phase 5</p><p>provide evaluation of the cost effectiveness of implementing the suggested lean methods based on life cycle</p><p>cost analysis (LCCA) and phase 6 prefers the right alternative based on multiple criteria decision making</p><p>(MCDM). In phase 7 the selected alternative is supposed to be implemented and finally the user should</p><p>monitor and control the process to make sure that the improvement is going as planned. The model was</p><p>verified successfully using a case study methodology at one Swedish sawmill called Södra Timber in</p><p>Ramkvilla, one part of Södra group. Results obtained from the study showed that the production and human</p><p>resources perspectives are the most critical problem areas that need to be improved. They got the lowest</p><p>scores in the lean profile, 63% and 68%, respectively. Using value stream mapping (VSM) it was found that</p><p>the non value added (NVA) ratios for the core and side products are 87.4% and 90.4%, respectively. Using</p><p>the model, three improvement alternatives were suggested and evaluated using LCCA and MCDM.</p><p>Consequently, implementing 5S got the highest score, second came redesigning the facility layout.</p><p>However, it was estimated that 4.7 % of NVA for the side product would be reduced by redesigning the</p><p>facility layout. The recommendations were suggested for the company to improve their performance. The</p><p>novelty of the thesis is based on the fact that it addresses two main issues related to lean manufacturing:</p><p>firstly, suggesting lean techniques based on assessment of lean profile that is based on BSC and QFD, and</p><p>secondly assessing the cost effectiveness of the suggested lean methods based on LCCA and MCDM. This</p><p>thesis provides a generalized model that enables the decision-maker to know and measure, holistically, the</p><p>company performance with respect to customer requirements. This will enable the company to analyze the</p><p>critical problems, suggest solutions, evaluate them and make a cost effective decision. Thus, the company</p><p>can improve its competitiveness.</p>

Thesis (M.S. in Management) Naval Postgraduate School, June 2000.<br>Thesis advisor(s): Mehay, Stephen; Eaton, Donald R. "June 2000." Includes bibliographical references (p. 93-95). Also available in print.

<p>The purpose of this thesis is to develop a model for assessing cost effectiveness of applying lean tools. The model consists of eight phases: it starts by understanding customers' requirements using Voice of Customer (VOC) and Quality Function Deployment (QFD) tools. In phase 2, the current state of plant is assessed using lean profile charts based on Balanced Scorecard (BSC) measures. In phase 3 and phase 4, identification of critical problem(s) and generating of improvement suggestion(s) are performed. Phase 5 provide evaluation of the cost effectiveness of implementing the suggested lean methods based on life cycle cost analysis (LCCA) and phase 6 prefers the right alternative based on multiple criteria decision making (MCDM). In phase 7 the selected alternative is supposed to be implemented and finally the user should monitor and control the process to make sure that the improvement is going as planned. The model was verified successfully using a case study methodology at one Swedish sawmill called Södra Timber in Ramkvilla, one part of Södra group. Results obtained from the study showed that the production and human resources perspectives are the most critical problem areas that need to be improved. They got the lowest scores in the lean profile, 63% and 68%, respectively. Using value stream mapping (VSM) it was found that the non value added (NVA) ratios for the core and side products are 87.4% and 90.4%, respectively. Using the model, three improvement alternatives were suggested and evaluated using LCCA and MCDM. Consequently, implementing 5S got the highest score, second came redesigning the facility layout. However, it was estimated that 4.7 % of NVA for the side product would be reduced by redesigning the facility layout. The recommendations were suggested for the company to improve their performance. The novelty of the thesis is based on the fact that it addresses two main issues related to lean manufacturing: firstly, suggesting lean techniques based on assessment of lean profile that is based on BSC and QFD, and secondly assessing the cost effectiveness of the suggested lean methods based on LCCA and MCDM. This thesis provides a generalized model that enables the decision-maker to know and measure, holistically, the company performance with respect to customer requirements. This will enable the company to analyze the critical problems, suggest solutions, evaluate them and make a cost effective decision. Thus, the company can improve its competitiveness.</p>

Energy is increasingly becoming more important in today&rsquo<br>s world. Ascending of energy consumption due to development of technology and dense population of earth causes greenhouse effect. One of the most valuable energy sources is hydro energy. Because of limited energy sources and excessive energy usage, cost of energy is rising. There are many ways to generate electricity. Among the electricity generation units, hydroelectric power plants are very important, since they are renewable energy sources and they have no fuel cost. Electricity is one of the most expensive input in production. Every hydro energy potential should be considered when making investment on this hydro energy potential. To decide whether a hydroelectric power plant investment is feasible or not, project cost and amount of electricity generation of the investment should be precisely estimated. This study is about cost estimation of hydroelectric power plant projects. Many design parameters and complexity of construction affect the cost of hydroelectric power plant projects. In this thesis fifty four hydroelectric power plant projects are analyzed. The data set is analyzed by using regression analysis and artificial neural network tools. As a result, two cost estimation models have been developed to determine the hydroelectric power plant project cost in early stage of the project.

Historically, transportation asset management has focused on roadways and bridges, but more recently, many agencies are looking to extend their programs to ancillary assets such as traffic signs and guardrails. This thesis investigates the state of practice of managing these assets in order to assess the data and system needs for successful program implementation, and further reviews the opportunities for making a business case for formal management procedures based on quantified benefits of managing ancillary assets. The asset classes, selected from a review of asset management literature, include culverts, earth retaining structures, guardrails, mitigation features, pavement markings, sidewalks and curbs, street lights, traffic signals, traffic signs and utilities and manholes, with data as an information asset. Findings from a literature review showed that a number of agencies have made substantial efforts to manage their ancillary transportation assets; however, methods and practices vary. Specific state and municipal agencies identified from the literature review were surveyed for further details on their practices. The survey results show significant knowledge gaps in data collection cost estimates, and cost savings from the implementation of a transportation asset management program for ancillary assets. Finally, this work evaluates the opportunities to quantify the benefits of ancillary transportation asset management, indicating several challenges due to a lack of the data needed. The results obtained highlight the current state of practice, revealing opportunities and challenges for improving the management of ancillary transportation assets.