Jim Tompkins

Special Guest Blog Post written by Dr. Johannes Hangl with e2open   AI is no longer an experiment in global trade compliance. It’s already being applied in product classification, document-to-declaration workflows, risk targeting, and sanctions screening. At the same time, regulators and customs authorities are adopting AI themselves. This is raising expectations for data quality, transparency, and governance across the entire trade ecosystem. With the EU AI Act set to apply from August 2026, companies that have not yet implemented human-in-the-loop controls, drift monitoring, and defensible audit trails are running out of time to close the gap.   Where AI is already adding real value today: HS and ECN classification   Product classification has become one of the most practical AI use cases. Modern tools can now suggest harmonized system (HS/ HTS) and export control (ECCN) codes, explain the rationale, and attach confidence scores and audit metadata to each decision. This direction mirrors what customs authorities are doing. Administrations such as German Customs have discussed using machine learning to improve targeting and risk detection. It appears both sides of the border are moving toward data-driven decision support. AI does not remove accountability. It changes how accountability is exercised.   Practical…

Special Guest Blog Post written by Chris Cunnane with InterSystems   Demand sensing and demand forecasting are both crucial aspects of optimizing supply chains, but they do have slightly different functions in their approach and focus. Demand sensing uses real-time data and analytics to identify and respond to immediate demand fluctuations, while demand forecasting uses historical data to predict future demand over a longer period (months or years). Different methods, such as statistical modeling and machine learning, are used to enhance the accuracy and adaptability of these processes. Both areas are crucial for companies when it comes to projecting sales, managing inventory, and coordinating replenishment. In the end, the goal is to accurately predict customer demand by using predictive models to forecast future demand. InterSystems surveyed 450 senior supply chain practitioners and stakeholders to examine key supply chain technology challenges, trends, and decision-making strategies across five key use cases: fulfillment optimization; demand sensing and forecasting; supply chain orchestration; production planning optimization; and environmental, social, and governance (ESG). This blog focuses on demand sensing and forecasting.   Current State of Demand Sensing and Forecasting According to the survey results, when asked how they currently forecast demand, 36% of respondents indicated that…