Supply chain resilience hinges on structured data governance

The rapid digitalization of global industries, amplified by the proliferation of the industrial internet and data-driven platforms, has turned data into a critical resource for supply chain coordination. However, the surge in data volume, complexity, and sensitivity has exposed systemic vulnerabilities in data governance. Fragmented platforms, unclear standards, and cross-border data asymmetries have hindered transparency, compliance, and risk control across supply networks.

In this context, a new study provides critical insights into how enterprises can effectively optimize data governance frameworks. The research presents a hybrid decision-making model designed to address the multifaceted governance challenges of modern supply chain ecosystems. Their paper, titled “Supply Chain Data Governance Optimization Based on Fuzzy DEMATEL–ISM”, was published in SAGE Open.

The study leverages a combined Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methodology to identify and structure the core factors influencing supply chain data governance (SCDG). Using expert-based evaluations and information ecology theory, the researchers construct a comprehensive index system that maps out how foundational digital practices can scale into systemic governance efficiency. The result is a strategic, tiered approach to supply chain data governance that directly supports sustainable digital development.

What drives the urgent need for supply chain data governance optimization?

According to the study, supply chain data governance is not just a technological necessity but a foundational enabler of collaborative innovation and strategic integration. The researchers argue that only a well-structured, multi-stakeholder governance model can support the sustainable evolution of digital supply chains. Without such optimization, enterprises risk inefficiencies, data breaches, and weakened decision-making in the face of rapid technological change.

To capture these dynamics, the study develops an index system based on information ecology, incorporating variables such as data quality, standardization, sharing mechanisms, regulatory alignment, and stakeholder trust. These variables are then assessed for influence and dependency using expert insights, forming the foundation for the quantitative modeling that follows.

How does the Fuzzy DEMATEL–ISM model identify key influencing factors?

To navigate the complexity of interrelated governance indicators, the researchers employ the Fuzzy DEMATEL approach to measure the causal relationships between factors. DEMATEL allows for the quantification of influence levels, showing which governance elements are primary drivers and which are downstream effects. Sixteen core indicators are ultimately selected, ranging from technical infrastructure and platform interoperability to cross-organizational coordination and compliance systems.

Once the influence matrix is established, the ISM methodology is applied to structure these indicators into a tiered hierarchy. At the foundational layer are elements such as data quality management, digital infrastructure maturity, and legal standardization. These base-level components are shown to directly affect mid-level factors like risk response mechanisms and data transparency. At the top of the model sit strategic outcomes such as ecosystem synergy and value co-creation.

This recursive, multi-layered structure offers a clear roadmap for supply chain actors, guiding them from foundational reforms to high-impact strategic goals. By mapping out this progression, the study delivers not only a diagnosis of current governance gaps but also a prescriptive pathway for optimization.

What are the practical implications for industry and policy?

For enterprises, the model offers a structured approach to prioritize governance initiatives. By focusing on foundational drivers such as standardized protocols and technical interoperability, companies can build the conditions necessary for successful data sharing and collaboration. This, in turn, enhances their ability to participate in digitally integrated supply networks.

For policymakers, the study underscores the importance of cross-sectoral and cross-border data governance frameworks. Ensuring consistency in data standards, privacy norms, and cybersecurity requirements is essential to enabling seamless collaboration between supply chain partners. Regulatory clarity and infrastructure investment become crucial leverage points in facilitating digital transformation across industries.

Moreover, the study’s structured model is adaptable to a range of industries and geographies. Whether applied in manufacturing, logistics, or agrifood supply chains, the approach offers a replicable strategy for reinforcing resilience, improving transparency, and ensuring long-term sustainability.