Mesoeconomics: Analyzing Global Supply Chain Networks Using Big Data
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Mesoeconomics represents a critical intermediate layer of economic analysis, bridging the gap between microeconomics and macroeconomics. With the growing complexity of global supply chains, traditional economic frameworks and data analysis techniques have proven insufficient. The increasing availability and use of big data technologies have highlighted the relevance of mesoeconomic approaches in analyzing networked economic interactions across international supply chains.
The research aims to explore how mesoeconomic frameworks can be effectively utilized to analyze global supply chain networks through big data analytics. It seeks to identify the advantages of applying mesoeconomic analysis in understanding the structure, flow, and dynamics of supply chains, as well as the challenges associated with data integration and analytics.
The research employs a qualitative and conceptual analysis of mesoeconomic theory in the context of supply chains, supplemented by a review of current big data analytics methods used by companies. The analysis includes an examination of various data sources (primary and secondary), network structures, and classification techniques that support the processing and interpretation of large-scale data related to goods and services flows.
The findings suggest that mesoeconomics, when combined with big data analytics, offers a robust framework for capturing the complexities of global supply chains. This approach enhances the understanding of relationships among products, producers, and processes, enabling more efficient and informed decision-making. However, challenges such as data quality, interoperability, privacy, and security remain significant barriers to the full realization of big data’s potential in supply chain analysis.
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