Advanced technological solutions are emerging to tackle the growing challenges of safety and security in global supply chains. Shippers are increasingly turning to artificial intelligence (AI) to improve their understanding of logistics partners and manage the vast amounts of data generated by supply chains.
With the internet flooded with data, AI is proving essential in sifting through this information to discard low-quality data and focus on valuable, trusted sources. This is particularly beneficial for companies managing long supply chains with distant or small suppliers. AI can also help recognize regional events or activities that may have affected supply chains, adding an extra layer of insight into potential risks.
One example of the role AI can play is in the transportation of goods like barbecue charcoal, which, when improperly handled, can pose safety risks such as fires on board ships. AI can help identify regional suppliers of such goods, often small local operations, that may be linked to potential hazards.
In addition to addressing localized risks, AI can provide broader insights into larger companies, tracking events such as managerial changes, financial developments, and mergers or acquisitions, which could affect supply chain operations. By analyzing global data and cross-referencing it, AI systems can offer early warnings about distant partners, fostering trust among businesses.
One company offering data-mining services is Semantic Visions (SV), based in Prague, Czech Republic. SV has developed an AI-powered system designed to gather data on specific companies and regions, providing valuable insights for businesses seeking to improve their supply chain security.
SV’s system processes up to 2 million data points daily, including news articles, blogs, open-source data, and proprietary information. The company continuously enriches its database of companies and cross-references this information with relevant global events and their relationships. According to SV COO Julius Rusnak, the system is designed to identify the source of data and ensure it is relevant to specific companies.
The system operates in multiple languages, supported by a multilingual team capable of understanding regional nuances. The data collected is not solely analyzed by machines; SV also employs human expertise to interpret complex, global information.
However, there are challenges, as smaller suppliers in less developed countries may not have an online presence, limiting AI’s ability to collect data on them. Despite this, SV’s technology relies on a vast database of over 600 predefined global events, which range across various sectors, including environmental, social, and governance issues.
SV’s AI system is designed to detect patterns in data, recognizing opportunities and identifying potential risks based on semantic structures in the information. This constant monitoring of online sources helps provide an early warning system for incidents such as port congestion, oil spills, or transportation delays.
The goal is to offer businesses filtered, structured information, effectively eliminating irrelevant data and providing actionable insights. According to Rusnak, while AI tools can present valuable data, it remains up to the user to interpret the findings.
For example, the company’s Russian reports showed a shift in sentiment toward Ukraine, a significant change that analysts could interpret in the context of geopolitical events. Although the system provides data, it is not designed to make final conclusions, leaving interpretation in the hands of industry experts.
As supply chain complexities continue to grow, AI-powered systems are likely to play an increasingly important role in helping businesses navigate and manage the challenges posed by global logistics.
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