SUPPLY CHAIN STRATEGIES
Making it reality: the infrastructure for intelligence The underlying data infrastructure built for analytics has to be capable of capturing not just what happened, but how those decisions were reached. This is the domain of Trimble, with positioning, telematics and transportation management systems that sit on the edge of supply chains, capturing the granular process data to feed analytics models.
Jonah McIntire, Chief Platform Officer at Trimble, explains that the infrastructure companies are requesting is evolving:“ There has been a shift towards harvesting or leveraging‘ how work is done’ data rather than just the outcomes.
“ In the past, one might have been asked about shipment-level outcome data: on-time deliveries, cost to serve or tender rejection rates. Now the interest is inclusive of how those outcomes came about – what was evaluated or considered before a tender was rejected.” The result of this is significant. Process-level data, which is the kind that DHL uses to train AI agents and machine learning models, is what Trimble’ s infrastructure is designed to capture. The logic is circular, with better data capture enabling better models, which in turn means better decisions and rich data generated.
Looking ahead, Jonah considers where this trajectory is leading:“ When we look out at the future, say 20 to 50 years from now, people are not imaginative enough to see how AI plus autonomous vehicles or drones will make the sector largely automated and incredibly safe, efficient and effective.”
MYTH BUSTER: THREE COMMON MISCONCEPTIONS ABOUT SUPPLY CHAIN ANALYTICS
•“ We need perfect data before we are able to start” Generative AI is able to process imperfect data at scale. Don’ t let data quality prevent you from starting.
•“ Analytics is a reporting function” Those organisations leading the way have made analytics the foundation of operational decision-making, not a retrospective tool.
•“ We can’ t see beyond our Tier 1 suppliers” The technology to continuously monitor sub-tier suppliers is available now. The question in reality is whether firms are choosing to use it.
For those starting their analytics journey, he also has some pragmatic advice. The most common barrier companies cite, data quality, is perhaps not as big an obstacle as originally anticipated. Generative AI, Jonah argues, can now handle the imperfect, messy data at scale that a human analyst simply can’ t. His advice aims to help those starting to push past the initial hesitance:“ Don’ t sweat the data quality – that’ s the advice I’ d give.”
74 August 2026