Eco-efficiency improvements in manufacturing is a controversial subject for researchers, practitioners as well as policy makers. The widely accepted definition of "doing more with less" is not accurate enough to guide the design of improvements that can deliver products in a sustainable way. The outcome of these challenges is evident through significant environmental performance variations across various levels of manufacturing operations. The study is driven by the complexity of manufacturing systems and sought to offer design support for practitioners that aim to improve eco-efficiency. A maturity model has been developed in this work that simulates the influence of manufacturing practices on eco-efficiency. The model takes the form of a maturity grid (PMGE) that overlooks practices at process, management systems and top-management levels and incorporates 15 dimensions of performance overall. Evidence shows that practices tend to evolve from reactive to proactive as manufacturing systems mature and embrace eco- efficiency as a systemic property. It was also found that mature companies achieve improvements in energy and resources by relying on existing internal capabilities. Tools to facilitate research and intervene with practitioners in real-life problems were developed and tested. The researcher combined research findings and tools into a maturity-based method (PMGEM) for eco-efficiency improvements. The method intends to help practitioners plan and design eco- efficiency improvements aligned to existing internal capabilities and adopt a more proactive behaviour to environmental challenges. PMGEM was ultimately applied in two case studies with ultimate goal to help practitioners resolve real-life challenges. The applications were positively commented and encourage further work in this field. The researcher envisages that methods such as PMGEM are deeply needed in manufacturing to support practitioners approach complex concepts such as eco-efficiency. Simplification and decomposition techniques with a clear intended use can facilitate the implementation of ambitious improvement strategies for sustainable development.

Submitted by Maicon Juliano Schmidt (maicons) on 2015-07-15T14:12:15Z No. of bitstreams: 1 Iberê Guarani de Souza.pdf: 3328129 bytes, checksum: 6f2250512f214402473b148d4ad6e48d (MD5) / Made available in DSpace on 2015-07-15T14:12:15Z (GMT). No. of bitstreams: 1 Iberê Guarani de Souza.pdf: 3328129 bytes, checksum: 6f2250512f214402473b148d4ad6e48d (MD5) Previous issue date: 2014-01-31 / Nenhuma / As empresas buscam, a cada dia, melhorar sua eficiência produtiva e, com isso, aumentar sua lucratividade e competitividade. Para tanto, há diversas formas de descobrir os fatores críticos de competitividade que podem estar presentes nos mais diversos setores de manufatura. Logo, o uso de técnicas robustas para avaliar e medir esses fatores torna-se essencial para o suporte à tomada de decisão. Este estudo tem o objetivo de analisar a influência dos processos de melhoria contínua e de aprendizado em termos de eficiência e volume de produção em uma empresa de manufatura. Para atingir o objetivo proposto, a pesquisa realiza um estudo de caso utilizando a Análise Envoltória de Dados (DEA), combinada com o teste de Regressão Linear e o teste de ANOVA. Nesta etapa, formula-se um modelo conceitual com quatro hipóteses principais e oito hipóteses secundárias. Para avaliação da eficiência DEA, o modelo utiliza retornos variáveis de escala (VSR) com orientação a insumo, considerando as principais matérias-primas utilizadas pela empresa com base no custo variável total. O teste de Regressão Linear efetua a avaliação do impacto do processo de melhoria e de aprendizado na eficiência (DEA). Por sua vez, o teste de ANOVA avalia as médias da eficiência de cada linha de produção para cada ano analisado. O estudo realiza-se de forma longitudinal, com avaliação de seis anos de produção de manufatura. Os resultados da pesquisa mostram que apenas uma das linhas de produção aumentou a eficiência ao longo do tempo. Além disso, indicam que duas linhas de produção tiveram impacto das ações de melhoria no volume de produção. Logo, as variáveis referentes aos programas de Kaizen, às horas de treinamento e à experiência dos funcionários influenciaram significativamente o modelo. Verifica-se que os projetos voltados à melhoria contínua e ao aprendizado não foram suficientes para aumentar a eficiência em duas importantes linhas de produção. Além disso, o estudo elucida que o volume de produção impacta negativamente a eficiência de uma das linhas de produção. Com a análise, é possível identificar quais fatores são representativos para aumentar a eficiência produtiva. Logo, conclui-se que a atualização tecnológica constitui um fator importante a ser seguido pela empresa estudada. / Everyday, companies seek to improve their productive efficiency and, thus, increase their profitability and competitiveness. For both, there are several ways to discover the critical factors of competitiveness that may be present in various manufacturing sectors. Thus, the use of robust techniques to assess and measure these factors is essential to support decision making. This study aims to analyze the influence of the processes of continuous improvement and learning in terms of efficiency and production volume in a manufacturing company. To achieve the proposed objective, the research conducts a case study using Data Envelopment Analysis (DEA), combined with the Linear Regression test and the ANOVA test. At this stage, a conceptual model with four main hypotheses and eight secondary ones is formulated. To evaluate the DEA efficiency, the model uses Variable Scale Returns (VSR) with input orientation considering the main raw materials used by the company based on the total variable cost. The Linear Regression test performs the evaluation of the impact of the improvement process and learning efficiency (DEA). In turn, the ANOVA test evaluates the average efficiency of each production line for each year analyzed. The study is carried out longitudinally, by reviewing six years of manufacturing. The survey results show that only one of the production lines increased efficiency over time. In addition, the results indicate that two production lines have been impacted by the actions of improvement in the volume of production. Therefore, the variables related to Kaizen programs, to the hours of training and to employees ́ experience significantly influenced the model. It can be concluded that the projects focused on continuous improvement and learning were not sufficient to increase efficiency in two major production lines. Furthermore, the study shows that the production volume negatively impacts the efficiency of the production lines. With the analysis, it is possible to identify which factors are representative to increase production efficiency. Therefore, it can be conclude that the technology upgrade is an important factor to be followed by the company studied.