1. Introduction
The 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.