Supply Chain Data Governance Optimization Based on Fuzzy DEMATEL-ISM - Yaoxi Liu; Jinyu Wei; Xiaoxuan Wang (SAGE Open)
Supply chain data governance optimization in the context of digital transformation is an emerging topic. The process of data governance optimization involves complex governance subjects and systems. This study aims to clarify the mechanism of supply chain data governance structure and solve the data governance challenges faced by the whole supply chain, in order to promote the sustainable development of supply chain data governance ecosystem and realize the value-added of supply chain. Based on the theory of information ecology, the index system of supply chain data governance ecosystem is constructed. Further, we employed a combined fuzzy decision-making trial and evaluation laboratory (DEMATEL) and Interpretative Structural Models (ISM) method to explore the index prioritization and hierarchical relationships for data governance optimization. The initial weights of the indexes were determined based on the opinions of eight experts in related fields. The results of the DEMATEL analysis identified 16 key indexes for data governance optimization. Futhermore, based on the ISM model, we developed a multi-layer recursive explanatory structural model showing the structural relationships for supply chain data governance optimization and finally proposed the corresponding optimization path. The proposed model provides a more accurate analysis for each governance party of the supply chain when it comes to digital transformation of the supply chain. It provides authorities with a layered progression of supply chain data governance optimization thinking and the actions to be taken at each layer.