1. IntroductionThe new round of technological revolution drives the strong rise of emerging technologies, industrial digitalization has become an important engine of the fourth industrial revolution (Nar et al, 2020). In the digital era, the deconstruction and restructuring of the industrial system can catalyze the transformation and upgrading of the supply chain (Kaminski et al, 2017). At the same time, the ”digital infrastructure” with industrial Internet as the core has emerged, providing key support for the collaborative innovation of supply chain (Broo, Bravo-Haro & Schooling, 2022). Under the new development pattern, the digital transformation of supply chain has become an inevitable trend of a country’s economic development (Martens & Zscheischler, 2022). However, the supply chain market demand for data capacity and quality is not consistent with the state of the data, so supply chain data governance research is urgent.Compared with other industries, the traditional manufacturing supply chain has been exposed to many problems that need to be solved under the impact of digital transformation, such as numerous data calibers, obstructed data circulation, unclear data quality and hidden data security, due to the complicated and variable nodes and significant differences in operation modes (Reinartz & Wiegand, 2019). Modern supply chain organizations are paying more and more attention to data governance, and data governance around maximizing the release of data value is a necessary way to promote the value-added of supply chain and promote the transformation and upgrading of manufacturing industry.However, data governance parties are still facing governance dilemmas such as slow progress, low layer of governance technology and inadequate governance system (Fothergill et al, 2019). Therefore, it is necessary to explore the underlying logic behind supply chain data governance and clarify the structural mechanism of supply chain data governance. These will not only help broaden the research ideas of supply chain data governance optimization, but also facilitate the overall process of data governance. Against this background, the aim of this study is to address the below-mentioned objectives.(1)To find out the composition of indexes for data governance in the supply chain environment.(2)To clarify the structural system of supply chain data governance optimization..(3)To propose the corresponding governance optimization paths to improve the effectiveness of data governance.Furthermore, Supply chain data governance optimization is a dynamic, stable and sustainable complex cycle system (Hazen et al, 2018). It formed by the interaction of governance subject, governance technology and governance environment with data as the core and the supply chain as the carrier (Li, 2017). In view of this, the study constructs the index system of supply chain data governance ecosystem from the perspective of information ecology. We focuses on the mechanism of action among indexes in different dimensions of supply chain data governance, and determines the importance degree of each index of supply chain data governance ecosystem by applying the fuzzy DEMATEL method, and then identifies the key indexes. On this basis, the structural levels of key indexes are divided by applying the ISM method to build a multi-layer recursive explanatory structural model of supply chain data governance optimization. The model reveals the optimization structure of supply chain data governance and proposes the corresponding optimization path of supply chain data governance.The remainder of the paper is organized as follows. In Section 2, the literature review is presented followed by Section 3 and its subsections which build a supply chain data governance ecosystem index system based on information ecology theory. Next, Section 4 presents the details of the fuzzy DEMATEL-ISM methodology and the stepwise approach that contains some steps. Thereafter, in Section 5, a multi-layer recursive explanatory structure model for supply chain data governance is proposed to analyze the governance structure in a hierarchical manner, and the data are presented in Tables 2–4, Tables A1-A3 and Fig. 2 is a explaination of the structure diagram. Section 6 proposes the corresponding optimization path followed by Section7, which concludes our study.