Mark Meister

Manufacturers across chemicals, life sciences, and consumer packaged goods lose more than $120 billion annually through inventory write-offs, but those losses are often the end result of material risks that began months earlier. Forecast changes, supplier delays, quality events, aging inventory, and shifting production priorities can quietly compound into shortages, excess inventory, scrap, and lost margin when they aren’t identified and addressed early enough. The State of Material Risk & Recovery explores how leading batch manufacturers are moving beyond traditional inventory management to identify risk earlier, coordinate decisions across functions, and protect the value of materials throughout their lifecycle. Discover the Prevent, Mitigate, Recover framework and how AI-enabled decision intelligence can help teams connect signals across systems, understand root causes, prioritize the highest-impact actions, and intervene before operational risk becomes financial loss. Download the report to learn how to: Spot material risk earlier, before it turns into excess and obsolete inventory, production disruption, or a write-off. Close the gap between ERP visibility and actionable decisions by understanding not just what happened, but why and what to do next. Apply the Prevent, Mitigate, Recover framework to improve material readiness and unlock more recovery opportunities. Use AI to turn scattered signals into…

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…