Detalhes bibliográficos
Ano de defesa: |
2020 |
Autor(a) principal: |
Zanon, Lucas Gabriel |
Orientador(a): |
Não Informado pela instituição |
Banca de defesa: |
Não Informado pela instituição |
Tipo de documento: |
Dissertação
|
Tipo de acesso: |
Acesso aberto |
Idioma: |
eng |
Instituição de defesa: |
Biblioteca Digitais de Teses e Dissertações da USP
|
Programa de Pós-Graduação: |
Não Informado pela instituição
|
Departamento: |
Não Informado pela instituição
|
País: |
Não Informado pela instituição
|
Palavras-chave em Português: |
|
Link de acesso: |
https://www.teses.usp.br/teses/disponiveis/18/18156/tde-16112021-122153/
|
Resumo: |
Assessing the relationship between supply chain performance and organizational culture can help to predict scenarios and improve decision-making. However, this relationship is rarely explored due to the complexity of quantitatively addressing its natural subjectivity. Although soft computing techniques would have the potential to overcome this limitation, they have been rarely applied to this context. Therefore, this study aims to introduce a group decision model to analyze and quantify the causal relationship between organizational culture and supply chain performance based on the combination of fuzzy grey cognitive maps, grey clustering and multiple fuzzy inference systems. Such model is novel in the literature and can provide new theoretical and practical perspectives. The development of this research is based on the SCOR® (Supply Chain Operations Reference) model attributes (SCC, 2017) and Hofstedes (2001) organizational practices. The main contribution is the introduction of a multicriteria group decision-making model that promotes the alignment between organizational culture and supply chain management, internalizing culture as a driver for performance improvement efforts. Another contribution is the integration of the fuzzy grey cognitive maps and grey clustering techniques, also not found in the literature, which increased results reliability by reducing the required inputs to the user and the level of imprecise data in the computational model, enhancing speed of execution. With the conduction of two real application cases in companies from different industrial sectors, results show that the model is able to identify crucial elements regarding the cultural profile and performance of both organizations, aiding prioritization, anticipation and enabling the development of guidelines for action plans |