Optimizing Supply Chain Financial Strategies Based on Data Elements in the China’s Retail Industry: Towards Sustainable Development

Hong Zhang (18113052@bjtu.edu.cn), Weiwei Jiang, Jianbin Mu and Xirong Cheng
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Hong Zhang: School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
Weiwei Jiang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Jianbin Mu: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Xirong Cheng: School of Economics, Beijing Technology and Business University, Beijing 100048, China

Sustainability, 2025, vol. 17, issue 5, 1-19

Abstract: China’s retail industry faces unique challenges in supply chain financing, particularly for small and medium-sized enterprises (SMEs) that often struggle to secure loans due to insufficient credit ratings and collateral in the business environment of China. This paper presents a groundbreaking approach that integrates real-time data elements into financing models, addressing the critical issue of information asymmetry between financial institutions and retail SMEs. By leveraging dynamic data such as orders, receivables, and project progress, our novel framework moves beyond the limitations of traditional asset-based lending, employing advanced data analytics for enhanced credit assessment and risk management. Applying the Stackelberg game theory, we explore the strategic interactions between suppliers and purchasers in the retail supply chain, identifying optimal financing strategies that improve capital flow efficiency and reduce overall costs. Our comprehensive data-driven model incorporates various scenarios, including the traditional supply chain financing model (Model T) and the innovative data-element secured financing model (Model G). The latter further considers risk assessment, risk appetite, volume, and schedule factors, providing a holistic approach to financial decision-making. Through rigorous mathematical modeling and numerical analysis, we demonstrate the effectiveness of our proposed framework in optimizing supply chain financing strategies. The results highlight the potential for data-driven approaches to unlock new financing opportunities for SMEs, fostering a more collaborative and efficient ecosystem within the retail industry. This study presents comprehensive data-driven strategies that unlock new financing opportunities for SMEs, providing a practical roadmap for stakeholders to foster a more collaborative and efficient supply chain financing ecosystem. The significance of studying supply chain finance for small and medium-sized enterprises (SMEs) lies in optimizing financing models to address the financing difficulties faced by SMEs. This helps improve their market competitiveness and promotes resource sharing and collaboration among all parties in the supply chain, thereby achieving sustainable economic development.

Keywords: data elements; China’s retail industry; game theory; supply chain financing; SMEs; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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