Sistemas de predição de desempenho de cadeias de suprimento são constituídos por indicadores que visam estimar o desempenho da empresa-foco em decorrência também do desempenho dos indicadores dos fornecedores. Na literatura são encontrados apenas dois modelos quantitativos (GANGA; CARPINETTI, 2011; AGAMI; SALEH; RASMY, 2014) que permitem predizer o desempenho de cadeias de suprimento usando os indicadores do modelo SCOR® (Supply Chain Operations Reference). Uma limitação de ambos modelos é a dificuldade de se ajustar ao ambiente de uso, uma vez que sua implementação e atualização requerem a parametrização manual de muitas regras de decisão. Tanto o uso de redes neurais quanto de sistemas neuro-fuzzy têm o potencial de contornar essa dificuldade por utilizarem um mecanismo de aprendizagem que possibilita a adaptação ao ambiente de uso usando dados numéricos. Todavia, na literatura não são encontradas aplicações dessas técnicas no apoio à predição de desempenho de cadeias de suprimento, tampouco estudos que discutam qual dessas técnicas se mostra mais adequada para lidar com este problema. Diante disso, o objetivo desta pesquisa é construir e a avaliar a adequabilidade de dois sistemas de predição de desempenho, ambos baseados nos indicadores do modelo SCOR®, mas usando alternativamente as técnicas redes neurais e sistemas neuro-fuzzy, para apoiar a gestão de desempenho da empresa-foco e de sua cadeia imediata. A execução desta pesquisa envolveu o uso de simulação computacional e de testes estatísticos. Os resultados mostram que, embora ambas as técnicas apresentem capacidade de predição satisfatória, as redes neurais são mais adequadas em relação à complexidade da definição da configuração topológica, enquanto os sistemas neuro-fuzzy se sobressaíram em relação à capacidade de predição, complexidade do treinamento, quantidade de variáveis de entrada, suporte à tomada de decisão sob incerteza e interpretabilidade dos dados. Outros resultados desta pesquisa estão relacionados à identificação de particularidades do processo de modelagem das técnicas avaliadas, à elaboração de um panorama sobre o uso de técnicas quantitativas na avaliação de desempenho de cadeias de suprimento e à identificação de algumas oportunidades de pesquisa. / Supply chain performance prediction systems are composed by indicators that aim to estimate the performance of a focal company considering also indicators related to their suppliers. There are two quantitative models in the literature (GANGA; CARPINETTI, 2011; AGAMI; SALEH; RASMY, 2014) that enable to predict the supply chain performance using the indicators proposed by the SCOR® model (Supply Chain Operations Reference). Nevertheless, there is a drawback of both models that refers to the difficulty in adapting to the environment of use, since implementation and updating of these models require parameterization of many decision rules that must be done by an expert. The application of artificial neural networks as well as neuro-fuzzy systems can overcome this drawback by using a learning mechanism that enables the adaptation to the environment of use using numerical data on supply chain performance. However, there are neither studies in the literature that propose the use of these techniques in order to support supply chain performance prediction nor studies that discuss which of these techniques seem to be more appropriate to deal with this problem. Thus, the objective of this study is to propose and evaluate the adequability of the two types of performance prediction systems based on the performance indicators of the SCOR® model, and both using alternatively artificial neural networks and neuro-fuzzy systems to support performance management of a focal company and their supply chain. The implementation of this research involved the use of computer simulation and statistical tests. The results show that although both techniques present a satisfactory predictive capacity, neural networks are more appropriate in relation to the complexity of defining the topological configuration, whereas the neuro-fuzzy systems are more adequate regarding the predictive capacity, complexity of the training, amount of input variables, support to decision-making under uncertainty and interpretability of data. Other results of this research refer to the identification of characteristics of the modeling process of the evaluated techniques, as well as to the review on the use of quantitative techniques for supply chain performance evaluation and to the identification of some research opportunities.

Este trabalho apresenta e discute uma proposta baseada na teoria dos conjuntos fuzzy para predizer o desempenho da empresa-foco em cadeia de suprimentos modelada de acordo com os relacionamentos causais entre medidas de desempenho propostas pelo SCOR (versão 8.0). O uso de sistemas de medição de desempenho para gerenciar o desempenho de cadeias de suprimentos apresenta algumas limitações tais como a dificuldade de interpretação de resultados de natureza qualitativa, assim como a complexidade de um sistema tradicional de medição de desempenho lidar adequadamente com os relacionamentos de causas e efeito entre métricas de desempenho de diferentes processos de negócios ao longo da cadeia de suprimentos. Por outro lado, a lógica fuzzy, uma técnica apropriada para lidar com situações de incerteza e subjetividade, configura-se como uma alternativa interessante. Utilizando uma abordagem de pesquisa quantitativa descritiva, assumiu-se a hipótese de que um modelo de simulação quantitativo poderia ser construído para explicar o comportamento de processos operacionais. Os resultados da simulação mostraram-se bastante consistentes à metodologia SCORmark, proposta pelo Supply Chain Council. Análises estatísticas dos resultados da simulação, baseados no Método de Superfície de Resposta, também confirmaram a relevância dos relacionamentos causais incorporados no modelo. Em geral, os resultados reforçam que a proposição da adoção de um modelo de simulação baseado em lógica fuzzy e nas métricas do SCOR parece ser uma abordagem possível para auxiliar os gerentes no processo de tomada de decisão do gerenciamento do desempenho em cadeias de suprimentos. / This paper presents and discusses a proposal based on the theory of fuzzy sets to predict performance of focus company in a supply chain modeled according to causal relationships among performance metrics proposed by SCOR (version 8.0). The use of performance measurement systems to manage performance of supply chains presents some limitations such as difficulty of interpretation of results of qualitative nature as well as the complexity of having a conventional performance measuring system that can adequately handle cause-and-effect relationships of metrics of performance of different business processes of a supply chain. On the other hand, fuzzy logic, a technique suitable for dealing with uncertainty and subjectivity, becomes an interesting alternative approach. Using a descriptive quantitative approach, the research was based on the assumption that a quantitative simulation model can be built that explain (at least in part) the behavior of operational processes. Results of simulation were very much in line with those of the SCORmark methodology (SCC). Statistical analysis of the simulation results based on surface response method also confirmed the relevance of the causal relationships embedded in the model. In general, the findings reinforces the proposition that adoption of a simulation model based on fuzzy-logic and on metrics of the SCOR model seems to be a feasible approach to help managers in the decision making process of managing performance of supply chains.

Este trabalho apresenta e discute uma proposta baseada na teoria dos conjuntos fuzzy para predizer o desempenho da empresa-foco em cadeia de suprimentos modelada de acordo com os relacionamentos causais entre medidas de desempenho propostas pelo SCOR (versão 8.0). O uso de sistemas de medição de desempenho para gerenciar o desempenho de cadeias de suprimentos apresenta algumas limitações tais como a dificuldade de interpretação de resultados de natureza qualitativa, assim como a complexidade de um sistema tradicional de medição de desempenho lidar adequadamente com os relacionamentos de causas e efeito entre métricas de desempenho de diferentes processos de negócios ao longo da cadeia de suprimentos. Por outro lado, a lógica fuzzy, uma técnica apropriada para lidar com situações de incerteza e subjetividade, configura-se como uma alternativa interessante. Utilizando uma abordagem de pesquisa quantitativa descritiva, assumiu-se a hipótese de que um modelo de simulação quantitativo poderia ser construído para explicar o comportamento de processos operacionais. Os resultados da simulação mostraram-se bastante consistentes à metodologia SCORmark, proposta pelo Supply Chain Council. Análises estatísticas dos resultados da simulação, baseados no Método de Superfície de Resposta, também confirmaram a relevância dos relacionamentos causais incorporados no modelo. Em geral, os resultados reforçam que a proposição da adoção de um modelo de simulação baseado em lógica fuzzy e nas métricas do SCOR parece ser uma abordagem possível para auxiliar os gerentes no processo de tomada de decisão do gerenciamento do desempenho em cadeias de suprimentos. / This paper presents and discusses a proposal based on the theory of fuzzy sets to predict performance of focus company in a supply chain modeled according to causal relationships among performance metrics proposed by SCOR (version 8.0). The use of performance measurement systems to manage performance of supply chains presents some limitations such as difficulty of interpretation of results of qualitative nature as well as the complexity of having a conventional performance measuring system that can adequately handle cause-and-effect relationships of metrics of performance of different business processes of a supply chain. On the other hand, fuzzy logic, a technique suitable for dealing with uncertainty and subjectivity, becomes an interesting alternative approach. Using a descriptive quantitative approach, the research was based on the assumption that a quantitative simulation model can be built that explain (at least in part) the behavior of operational processes. Results of simulation were very much in line with those of the SCORmark methodology (SCC). Statistical analysis of the simulation results based on surface response method also confirmed the relevance of the causal relationships embedded in the model. In general, the findings reinforces the proposition that adoption of a simulation model based on fuzzy-logic and on metrics of the SCOR model seems to be a feasible approach to help managers in the decision making process of managing performance of supply chains.

While there is a concern that Sustainable Tourism has not been entirely adopted in practice (Graci, 2008), this thesis shows that Sustainable Tourism has been implemented by South Korean Tour Operators under the name of Fair Tourism. Fair Tourism is a rising trend as Sustainable Tourism in South Korea, and discussion on this new sector of the industry has increased in recent years (S. Gil Lee, 2016). This thesis adds to that discourse with three research objectives: 1) Define the concept of Fair Tourism. 2) What activities constitute Fair Tourism from practitioners’ viewpoint? 3) Examine how Fair Tour operators manage their sustainable supply chain based on the SCOR model.<div><br></div><div>To understand practitioners’ perception of Fair Tourism, this study has applied social constructionism, which recognized that human beings construct meanings through individual interaction (Walker, 2015). Semi-structured interviews with fifteen Fair Travel operators and thematic analysis have been applied for methodology (Elo & Kyngäs, 2008; McIntosh & Morse, 2015). This study has two significant findings. First, the findings have revealed a generally accepted definition of Fair Tourism by both researchers and operators. The definition includes Ensuring Economic Contribution to The Destination, Environmental and Social Sustainability, and Mutually Respectful Relationship. This generally acknowledged definition has helped Fair Tour operators develop the sector. Second, this thesis found that operators genuinely respect their suppliers. Operators regard their suppliers as partners who share responsibilities and benefits, leading to satisfactory outcomes for all partners (Macaulay et al., 1999). Their relationship with partners is long-term and deep rather than transactional.</div><div><br></div><div>Theoretically, this thesis contributed to the finding that Sustainable Tourism can be practicable under the name of Fair Tourism, demonstrating practitioners’ replies that are 100 percent consistent. This study applied a novel approach, focusing on the operators’ point of view, while previous studies on Fair Tourism focused on defining the term and the industry's demand (Byun, 2016; M.-K. Kim & Cho, 2019; Shin et al., 2018). Given that Supply Chain Operations Reference (SCOR) model has been developed for the analysis of the manufacturing industry, this study has advanced this manufacturing performance measurement framework and applied it to the tourism industry.</div>

In today's economy of global markets, supply chain networks, supplier/customer relationship management and intense competition; decision makers are faced with a need to perform decision making using tools that do not accommodate the nature of the changing market. This research focuses on developing a methodology that addresses this need. The developed methodology provides supply chain decision makers with a tool to perform efficient decision making in stochastic, dynamic and distributed supply chain environments. The integrated methodology allows for informed decision making in a fast, sharable and easy to use format. The methodology was implemented by developing a stand alone tool that allows users to define a supply chain simulation model using SCOR based ontologies. The ontology includes the supply chain knowledge and the knowledge required to build a simulation model of the supply chain system. A simulation model is generated automatically from the ontology to provide the flexibility to model at various levels of details changing the model structure on the fly. The methodology implementation is demonstrated and evaluated through a retail oriented case study. When comparing the implementation using the developed methodology vs. a "traditional" simulation methodology approach, a significant reduction in definition and execution time was observed.