The relationship between supply chain performance and organizational culture: a fuzzy grey cognitive maps based decision model

• Autor(a) principal: Zanon, Lucas Gabriel
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: https://www.teses.usp.br/teses/disponiveis/18/18156/tde-16112021-122153/
• topic: fuzzy grey cognitive maps; grey clustering; cultura organizacional; fuzzy grey cognitive maps; fuzzy inference; gestão de desempenho da cadeia de suprimentos; grey clustering; group decision-making; inferência fuzzy; organizational culture; supply chain performance management; tomada de decisão em grupo

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