The Art of Risk Management: Balancing Trade Exposure for Optimal Results


 

A suite of Global Trade Management (GTM) software empowers businesses to efficiently oversee and optimize cross-border supply chain operations. This comprehensive suite facilitates the management of trade finances, compliance, documentation, shipments, and third-party logistics providers (3PLs) seamlessly. By centralizing workflows, the suite optimizes, automates, and monitors the flow of information, finances, and goods across international trade, addressing complexities like multiple transportation modes, diverse trade parties, languages, time zones, and currencies.

The increasing significance of Cloud/SaaS-based deployments in Global Trade Management (GTM) solutions stands out as a predominant trend in the global GTM software market. While many large enterprises still favor on-premises deployments, the majority of small and medium-sized businesses (SMBs) and large organizations are embracing cloud-based GTM software services. Notably, several major GTM vendors have successfully transitioned to providing cloud-based deployments for enterprise clients.

Additionally, the convergence of Blockchain, Automation, Machine Learning, and Artificial Intelligence (AI) is another notable trend. Blockchain technology is anticipated to address significant challenges in the supply chain industry by offering end-to-end visibility, traceability, transparency, and security.

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Key inquiries addressed in this study include:

  • What is the current competitive landscape in the Global Trade Management (GTM) market?
  • What is the market share held by major vendors in this sector?
  • What are the primary competitive dynamics in both global and regional GTM markets?
  • Who are the leading vendors in global and regional markets?
  • Are there vendors specializing in specific industries?
  • How do different vendors compare in terms of their offerings of cloud-based versus on-premises solutions?
  • What competitive factors influence the market positioning of various vendors?
  • What are the relative strengths and challenges faced by vendors in this market?
  • How do different vendors position themselves competitively across customer segments, ranging from SMBs to large enterprises?

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Machine learning and AI are poised to drive the next generation of supply chain solutions by creating self-aware platforms that integrate human input, data, and technology. Rapid advancements in technologies such as Robotic Process Automation (RPA), blockchain, AI, and deep learning are catalyzing the development of trade management systems, enhancing supply chain operations. Automation and AI tools provide capabilities such as automated product classification, guided classification, data completion, and compliance risk mitigation. These tools also facilitate continuous auditing and analysis of legal content, ultimately reducing delays in customs clearances.

Vendors Covered in the study:

This study includes an analysis of key vendors, including AEB, Bamboo Rose, Cargosmart, Descartes System Group, E2open, Freightgate, LOG-NET, MIC Customs, OCR GTM, Oracle, QAD Precision, SAP, S&P Global, Thomson Reuters and, WiseTech Global.

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