Supply Chain Disruptions Demand Smarter Analytics, Not Just More Data, Engineer Argues

By Ugo Aliogo

The global supply chain crisis that dominated headlines in recent years revealed a uncomfortable truth: most organizations were drowning in data but starving for actionable insights. Patrick Okare, a data platform engineer who has spent years building scalable analytics systems for enterprise clients, believes artificial intelligence and machine learning represent more than just technological upgrades – they’re becoming essential survival tools for modern supply chains.
“I’ve worked with systems processing 1.5 million financial records daily, and I can tell you that volume alone doesn’t create value,” says Okare, whose experience spans complex data integrations across finance, retail, and healthcare sectors. “The same principle applies to supply chain data. You can have perfect visibility into every shipment, every warehouse, every supplier interaction, but without intelligent analysis, you’re just collecting expensive noise.”

Okare’s perspective is shaped by his hands-on experience implementing enterprise-grade data pipelines and witnessing how organizations struggle to transform information into operational decisions. During his tenure at Dayforce Canada, where he managed data architectures supporting over 6,500 end-users, he observed how even sophisticated companies often rely on reactive rather than predictive approaches to supply chain management.

“Traditional supply chain analytics tells you what happened last week or last month,” he explains. “But AI and machine learning can tell you what’s likely to happen next week, next month, and more importantly, what you should do about it right now.”

The transformation Okare envisions goes beyond simple demand forecasting. His research into AI-driven supply chain analytics reveals opportunities for what he calls “predictive resilience” – systems that don’t just anticipate problems but automatically adapt to prevent them. Drawing from his experience with automated data quality frameworks that reduced manual processing by dozens of hours weekly, he sees similar potential for supply chain automation.

“When I implemented metadata-driven processing systems, we didn’t just speed up operations—we eliminated entire categories of human error,” Okare recalls. “The same logic applies to supply chain decisions. AI systems can continuously monitor thousands of variables that human analysts might miss or misinterpret.”
However, Okare cautions against viewing AI as a plug-and-play solution for supply chain challenges. His experience managing data migrations and ensuring compliance across multiple jurisdictions has taught him that successful implementation requires careful attention to data quality and organizational readiness.
“Machine learning algorithms are only as good as the data they’re trained on,” he warns. “I’ve seen organizations spend millions on AI systems that produce garbage results because their underlying data was incomplete, inconsistent, or biased. You have to build the data foundation before you can build the intelligence layer.”

The real breakthrough, according to Okare, comes when AI systems move beyond descriptive analytics to prescriptive recommendations. His work with complex enterprise architectures has shown him how automated decision-making can transform operational efficiency, but only when properly designed and implemented.

“It’s not enough for AI to predict that you’ll run out of inventory next month,” he explains. “The system needs to automatically suggest optimal reorder quantities, identify alternative suppliers, assess risk factors, and even execute the procurement process if configured appropriately.”

Despite his enthusiasm for AI’s potential, Okare emphasizes that technology alone won’t solve supply chain challenges. His experience leading technical teams and managing enterprise transformations has convinced him that successful AI implementation requires cultural change as much as technological upgrade.
“The most sophisticated AI system is worthless if decision-makers don’t trust its recommendations or don’t know how to act on its insights,” he concludes. “Organizations need to invest in both the technology and the human capabilities required to leverage it effectively. The future of supply chain management isn’t about replacing human judgment – it’s about augmenting it with intelligent systems that can process information at scales and speeds that humans simply can’t match.”

Patrick Okare
2025-09-20