Sender Shamiss

It’s Monday morning. A shipment of specialized components is headed to an aerospace OEM when your screening workflow flags a potential “denied-party match” on one supplier. Now you’re digging into records, cross-referencing databases, and your carrier is waiting. If the match is false, the shipment stalls anyway, the team burns time on triage, and the customer’s production line waits. The issue with due diligence screening: is how to keep it from grinding your shipping operation to a halt. The problem: compliance steps that slow shipments In global trade, compliance screening is non-negotiable. Denied-party screening, export controls, trade restrictions—regulations have teeth, and they’re tightening in aerospace, defense, high-tech, medical devices, and telecom. But many teams still screen shipments the hard way: manually, ad-hoc, and late in the fulfillment cycle. The friction is predictable, especially when a shipper verifies a partner’s name against screening lists manually. They get false positives: names that match sanctioned entities but aren’t actually them. A supplier named “Chen Technologies” gets flagged because “Chen” appears on an export-control list. Now your team can’t tell quickly whether this is a real hit. Escalations pile up, shipments stop moving, and your compliance team drowns in tickets. Three patterns of an…

Prospects of supply chain automation have the industry abuzz. It’s even become a major sticking point in the International Longshoremen’s Association contract negotiations with the United States Maritime Alliance. The dockworkers do not want ports to automate processes out of fear they will lose their jobs to machines. Today, there are seemingly endless possibilities for optimization. Terms like generative artificial intelligence and machine learning have become commonplace in discussions about ways to gain efficiencies and reduce costs. Can man and machine work together as businesses leverage automation for optimization? Beyond the Buzz: Understanding the Automation Imperative Machine learning, a subset of artificial intelligence (AI), is described by Business News Daily as a later-stage development in which machines take in data on their own and then analyze it. Automation, on the other hand, is fixed on repetitive tasks; after a job is performed, an automation system “thinks no further.” The Business News Daily article explained that “automation involves an entire category of technologies that provide activity or work without human involvement,” while AI involves “a machine exhibiting and practicing something similar to what we describe as human thinking – that is, the ability to interact in thousands of ways with the…