DHL software tool predicts supply chain risks | 2017-05-25


German transport and logistics giant Deutsche Post DHL Group has launched a software tool that can predict supply chain risks by using machine learning algorithms to analyze online data and social media posts , the company announced on Wednesday.

The Supply Watch product can function as a stand-alone tool or as an extension of DHL’s Resilience360 supply chain risk management platform, a data mining tool that allows users to assess the hotspots of their supply chains, visualize potential risks and mitigate potential disruptions. by adjusting factors such as production schedules, marketing budgets or inventory positions.

Both platforms can detect disruptions to a company’s supply base, such as the 2016 bankruptcy of Korean container line Hanjin Shipping Co. Ltd. or the recent global “WannaCry” ransomware attack, before they cause financial loss or lasting reputational damage, DHL said. .

To detect potential supply chain disruptions, Supply Watch monitors 140 risk categories, including financial metrics, mergers and acquisitions, environmental damage, supply shortages, quality issues, and labor disputes. The system works by using machine learning (ML) and natural language processing (NLP) technologies to analyze data from up to 30 million posts from more than 300,000 online and social media sources, a declared DHL. It looks for danger signs from sources such as crime, misconduct, quality defects, and supply chain perils such as shortages, capacity constraints, and delays.

Early warnings allow supply chain managers to “focus on early risk mitigation and audit activities of their most relevant suppliers and third parties,” said Tobias Larsson, Head of Resilience360, DHL Customer. Solutions and Innovation, in a press release. “The insights and transparency customers get through Supply Watch is another example of how digitization can benefit end-to-end supply chain operations, creating resilient supply chains and enabling businesses to be more competitive.”


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