Made available in DSpace on 2014-06-12T15:52:15Z (GMT). No. of bitstreams: 2 arquivo3204_1.pdf: 2932974 bytes, checksum: ed70cb5e6dddd387ea04292de409a2f3 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010 / A fim de melhorar a sua competitividade no mercado global, as organizações de software têm se preocupado cada vez mais com a questão de produtividade na execução de projetos. No entanto, para melhorar a produtividade, as organizações de software devem definir uma forma de medí-la. O problema é que a medição da produtividade apesar de parecer ser simples, sua aplicação concreta se mostra muito complexa. Muitos são os trabalhos de pesquisa sobre o tema, no entanto não há convergência sobre a métrica mais adequada de produtividade para as organizações de software. Baseado nos conceitos fundamentais relacionados à processos de produção, áreas de conhecimento social, evidências coletadas em organizações de software reais e análise do estado da arte em medição de produtividade em software, concluimos que a métrica mais adequada para medir a produtividade é específica para cada contexto organizacional, pois envolve estratégia, cultura organizacional, modus operandi, além de interesse e conhecimento daqueles diretamente envolvidos na medição e avaliação da produtividade. Isto explica porque não existe e nem há a possibilidade de existência de uma métrica de produtividade para projetos de software universalmente aceita. Baseado nestas descobertas, sugerimos a adoção de uma abordagem de medir produtividade baseada em valor. A hipótese central que orienta nossa trabalho de pesquisa é que uma abordagem baseada em valor pra medir a produtividade para medir a produtividade de projetos de software é mais adequada que as medições tradicionais. Uma das consequências da validade desta hipótese é que cada organização deve definir seu próprio modelo para a medição da produtividade. Com o objetivo de ajudar as organizações a definir e implementar um modelo próprio de medição de produtividade, um processo sistemático, com uma seqüência bem definida de etapas, entradas, saídas e diretrizes foi proposto. Ele envolve as atividades relacionadas com a definição, implementação e aperfeiçoamento do modelo de medição de produtividade. Além disso, foi baseado em uma extensa revisão dos principais desenvolvimentos relacionados com a medição da produtividade, além de ser influenciado por modelos de referência em engenharia de software, como IDEAL, CMMI, PSM e ISO/IEC 15939. O resultado da aplicação deste processo em uma organização de software produz um modelo de avaliação da produtividade, que considera a idéia de valor com base na visão dos principais stakeholders da organização. Finalmente, o conceito de medição de produtividade baseado em valor é adotado e avaliado em um estudo de caso, envolvendo em uma organização real de desenvolvimento de projetos de software. Em particular, o processo proposto para definição de modelos de medição de produtividade foi adotado e os benefícios, problemas e desafios foram avaliados com o objetivo de avaliar a eficácia do processo em atendar a o seu propósito. As análises do estudo de caso confirmaram que este tipo de abordagem foi de fato mais adequada para a organização estudada e que potencialmente pode ser aplicado a outras organizações de software

A productivity monitoring system is developed to incorporate productivity measurement at various organizational levels within manufacturing plants into the general information system. Classical productivity measures, defined as ratios of inputs and outputs of production, are used in the developed system. In addition to measuring the total and partial productivity, the system compiles the total factor productivity which is often applied in manufacturing to represent operational efficiency. In the developed system, reporting of productivity information is based on the organizational structure such that productivity measures are provided only when the corresponding organizational (work) units exist. Thus, the productivity monitoring system provides not only the responsibility-based productivity information, but is flexible in the aggregation of productivity performances of organizational units. The system is executed on the MARK IV File Management System (Informatics Inc.), and a real-world case is studied. Since the data required in the productivity monitoring system are commonly available and shared by other manufacturing subsystems, the system can be implemented as a subsystem of the general information system. / Ph. D.

This study (1) determines the information managers commonly need to make decisions and initiate actions to improve performance, based on selected case studies, (2) investigates and explains the features and issues involved with how the different versions of TFPM address these information needs, and (3) develops a teaching model of TFPM. Based on the literature review, interviews with experts, and experiences with applications, the features and differences of the available TFPM versions were explained, providing sample applications whenever necessary. Using four selected cases, common user information needs were identified and compared with results of previous surveys. Alternative TFPM applications for each case were developed and evaluated using Archer's (1978) Design Process as implemented with VPC's (1990) PRFORM software. Based on the evaluations of the TFPM applications in each of the case studies, a teaching TFPM model was developed incorporating the features of the available TFPM versions that most appropriately responded to the common information needs. Some other features not portrayed in the available TFPM versions were added to facilitate portrayal, understanding, and acceptance for new users. There are basically two models of TFPM - the Productivity Indices (PI) Model and the Profitability = Productivity + Price Recovery (PPPR) Model. I proved that as implemented with discrete variables, Gollop's Model is equivalent to the PPPR Model. Various versions of these two models feature differences in deflation, aggregation of Outputs, inputs, and/or organizational units, treatment of capital, computation of dollar effects of changes in performance, and how to use TFPM for planning. The common information needs identified were (1) measures of a firm's past performance using physical productivity related to profitability; (2) measures of individual organizational units’ productivity aggregated into plant, division, or firm level productivity; (3) partial measures to explain what factors dr.ve the total performance measures; and (4) evaluations of plans/budgets to ensure performance improvement. Based on the evaluations of possible TFPM versions appropriate for each application, REALST stands out as the most advanced and flexible version. However, it has become too complicated for first-time users. Hence, the teaching TFPM model I have developed is a simplified version of REALST. / Ph. D.

The purpose of this study was first to determine factors associated with intellectual reputation, specifically among selected biomedical departments worldwide within the university setting. Second, the study aimed to examine intellectual reputation in relationship to doctoral graduates’ productivity in the biomedical sciences and in relationship to organizational biomedical advancement and productivity. Third, the study aimed to visualize a spatial relationship between intellectual reputation and local organizational biomedical advancement and productivity in the United States and the United Kingdom. Finally, a simulated research-based model was proposed for understanding hospital productivity. The study used quantitative analysis in order to achieve these goals. The Geographic Information System (GIS) and Geocommons were used to visualize possible relationship between universities and hospitals in different regions. The findings from this study suggest that the university’s research intensity, having a Nobel Laureate on staff, Hirsch Index of the most prominent researcher on staff, scientific patent, scientific publications, and affiliation with multiple countries are good predictors of intellectual reputation. Correlation analysis suggests that university intellectual reputation is associated with doctoral graduates’ productivity. When examining the relationship between the university and hospitals, university intellectual reputation was positively correlated with hospital biomedical advancement, r= .445, p =0.001. Hospital productivity was significantly correlated with university intellectual reputation, r= .322, p =0.001. University intellectual reputation was significantly correlated with hospital capacity to absorb knowledge (r= 0.211, p =0.005) and knowledge spillover (r=.242, p =0.001). Regression analysis reveals that hospital capacity to absorb knowledge and knowledge spillover are good predictors of hospital biomedical advancement, F (2, 176) = 52.637, p = 0.001. Hospital capacity to absorb knowledge, affiliation with a university, intellectual reputation of the affiliated university, and distance between the hospital and the affiliated university were shown to be good predictors to hospital productivity, F (4, 106) = 11.115, p = 0.001. Visual examination of the hospitals suggests that when the universities publish at a large quantity, this tends to influence the hospitals within the area to publish a large quantity as well. Additionally, hospitals that are more productive tend to cluster around universities with higher intellectual reputation. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

In this work I integrate some of my recent research developments in the theory and practice of Productivity and Efficiency Analysis of Economic Systems. In particular, I present some new theoretical relationships between various measures of efficiency and productivity, propose new solutions to some aggregation problems in efficiency analysis and apply the existing theory and the new findings to empirical analysis in Industrial Organization. / Graduation date: 2003

Reliable engineering productivity measurement is a critical element of predictable project performance and continuous improvement. Despite the fact that engineering costs have risen to levels approaching 20 percent of total project cost on some industrial projects, engineering productivity is less well understood and has received less study than construction productivity. Furthermore, engineering productivity is a critical determinant of the final cost and schedule performance of a project (Chang et al. 2001). For these reasons, metrics for assessing productivity to drive improvement are essential, especially considering trends toward offshore engineering. Applicable industry standard engineering productivity measurements must first be established and then applied to present day work processes before significant improvement and predictability of performance can be established (CII 2001). Over the years, a number of different approaches for engineering productivity measurement have been proposed. These approaches are discussed and the development of the CII Benchmarking and Metrics approach, a direct measurement approach, is presented for this research. This research: (1) identifies critical issues for the implementation of engineering productivity measurement; (2) develops an Engineering Productivity Measurement System (EPMS) based on real project data; and, finally (3) recommends a framework for future studies. / text

published_or_final_version / Public Administration / Master / Master of Social Sciences

No description available.

Research in strategic performance measurements has focused mostly on large organisations. In the last few years, there has been a widespread adoption and implementation of balanced strategic performance measurements that no longer narrowly focus on financial measurements but include other non-financial measures. Again, such improvements have focused on large organisations. This study aims to assess strategic performance measurement practices in the Small and Medium Enterprises within the construction industry. The Balanced Scorecard is used as a generic measurement framework to ascertain the current strategic performance measurements within this sector. The four perspectives of measurement; namely, financial, customer, internal process and learning and growth are used to determine the generic measurements within the construction industry. These are then used to determine to what the extent Small and Medium Enterprises in the construction industry have adopted the measurements outlined in these four perspectives. The nature and extent of strategic planning and perceived relevance of various sets of balanced measurements were also assessed. A survey was conducted in the form of a questionnaire in order to obtain primary data from a selected sample group. Using qualitative and quantitative techniques, the data was analysed to get a clear picture of current practice. From the results obtained from the respondents in the sample group, it seemed that there was some strategic planning within this sector although the process was mostly unstructured. The results also showed that the owner-manager is still solely responsible for strategic planning with little or no inclusion of other managers or employees.

This research provides a basis to explore the relationship between Just-In-Time (m) and performance of the manufacturing system using a systems approach. Specifically, a conceptual model that captures system-wide performance of the manufacturing system was built using system dynamics in an effort to investigate the relationship between JIT and performance of the manufacturing system. The model was designed and built to measure total-factor productivity (TFP) which represents a global performance measure of the manufacturing system. TFP captures the integrated, synergistic effect of the system variables and functions on global system performance. A major part of the research was devoted to building and testing the system dynamics model. The model consists of four sectors: production-inventory, labor, machine, and material. The labor, machine, and material sectors represent the input classes used to produce the finished goods. The production-inventory sector contains a variable representing the finished goods (output of the system). It also contains the TFP variable which relates all the input classes and the output, and calculates the TFP value simulated under a specific system state. / Ph. D.