Prepayment Model of Supply Chain Financing Based on Internet of Things and Machine Learning Algorithm

Area of Science:

• Business and Economics
• Technology and Innovation

Background:

• Global trade is increasingly de-regionalized, diminishing traditional trader advantages.
• Traders face intense competition and shrinking profit margins, threatening industry survival.
• Declining dependence on traders necessitates new business models for their continued viability.

Purpose of the Study:

• To explore the challenges faced by traders in the modern globalized economy.
• To propose innovative strategies for traders to adapt and thrive.
• To highlight the significance of emerging technologies in transforming the trade industry.

Main Methods:

• Analysis of current trade dynamics and the evolving role of intermediaries.
• Exploration of technological solutions, specifically the Internet of Things (IoT) and machine learning (ML).
• Development of adaptive strategies for traders to maintain profitability and relevance.

Main Results:

• Traditional trader advantages are eroding due to globalization and reduced regional barriers.
• The integration of IoT and ML presents significant opportunities for innovation in trade.
• New operational models are essential for traders to navigate competitive pressures and ensure survival.

Conclusions:

• Traders must innovate by adopting new technologies like IoT and ML to remain competitive.
• Adapting business models is crucial for sustained profitability and survival in the contemporary trade environment.
• The study underscores the transformative potential of technology in reshaping the future of trade.