Manifest 2026 in Las Vegas reflected a decisive shift in how supply chain visibility is discussed and deployed. The conversation has moved on from “can we get visibility?” to “can we run this, at scale, without it becoming another thing the operation has to babysit?” Across operator panels, investor sessions and hallway chats, the emphasis was on disciplined economics, deployment reality and measurable operational impact.
The concept is practical and grounded in what’s already there. Handhelds and tablets deployed across warehouses, yards and vehicles already contain Bluetooth radios that can continuously detect nearby BLE sensors and labels. By activating those radios through software, those devices become mobile hotspots in a distributed tracking network that extends coverage without installing new fixed infrastructure.
Launching at Manifest felt timely because so many of the sessions we attended circled the same core reality: supply chains are messy, exception-driven environments and any “intelligence layer” has to survive that. Below are a few themes that kept resurfacing throughout the week.
Despite all the AI conversation, a lot of the industry still runs on Excel spreadsheets, phone calls and highly localised know-how. That isn’t a criticism, it’s a reminder that deployment is the hard part.
Two implications came up again and again. First, for new visibility and AI platforms to stick, they need strong change management. The people doing the work; drivers, site leads, inventory teams have to feel the benefit in their day-to-day, not just see a dashboard or a business case.
Second, one solution doesn’t fit all. Logistics is full of niche problems with specific constraints, whether that’s direct-to-consumer chocolate delivery, cold chain handoffs or contractor-driven pickup models. The winners will be the systems that can adapt to those realities without demanding heavy process change or fixed infrastructure.
Session: Redefining what’s possible in supply chain visibility — with Joyce Cruts (Acer), Mathew Elenjickal (FourKites), Owen Nicholson (SLAMcore), David Zingery (Midmo.ai) and Rachel Holt (Construct Capital).
What stood out in this session wasn’t a call for “more data”, it was the push for operational outcomes. The discussion kept coming back to three practical realities:
From there, the panel naturally reinforced a bigger shift we heard across the week: the maturation of visibility. Location tracking is increasingly viewed as foundational rather than differentiated. The strategic advantage now lies in how organisations convert sensor data into timely intervention.
Across other sessions too, we heard a clear progression: basic tracking → contextual sensing → predictive intelligence that helps prevent disruption rather than simply report it. The strongest returns come from intercepting the small percentage of failures that create outsized operational and financial impact.
This reflects a broader movement toward systems that drive action, not dashboards. Continuous sensing has to integrate with automation engines, workflow triggers and decision-support tools that guide frontline teams in real time. In practice, that means designing for the messy reality of logistics networks, where data quality, coverage gaps and adoption determine whether “visibility” turns into performance.
Session: The Marriage between supply chain and technology: what’s optimising our decision making — led by Scott Luton from Supply Chain Now.
The most grounded thread was not “AI will replace people”. It was that the supply chain is a human system operating inside physical constraints and technology only wins when it strengthens decision-making without adding burden.
A few points that landed because they matched operational reality:
That same “decisioning on the frontline” idea appeared again in other sessions. Visibility is useful, but the real shift is visibility plus decision support. If the system does not reduce judgement-load for operators, it tends to remain a pilot or become shelfware.
Session: Turning products into data: bringing true visibility to the last mile — featuring perspectives from Mars Snacking, Trident Seafoods and Avery Dennison.
One of the most useful reframes in that session was that “cold” isn’t a single state. It’s product-specific and fragile. The risks change by product type, tolerance band, seasonality, route and dwell time. The breakdowns tend to occur at operational edges: cross-docking, transfer points and store handling, where variability is highest.
That’s where “turning products into data” starts to matter. If condition history and handling context travel with the shipment itself, routing decisions, packaging strategies and replenishment planning can adjust dynamically. To make that viable in real logistics environments, sensing models need to be flexible and infrastructure-light, because these environments are fluid.
Session: The intelligence no one talks about but everyone depends on — hosted by Dot Ai, with speakers including Ed Nabrotzky (Dot Ai), Amir Khoshniyati (Wiliot) and Chad Baker (Wurth Industry USA).
The focus was practical: how real-time data, predictive analytics and intelligent monitoring translate into asset performance, reduced downtime and faster decisions. A consistent point was the dependency you can’t get around: you cannot build dependable intelligence on unreliable signals.
That reinforced a theme we heard across the show: AI is only as valuable as the data foundation feeding it. Supply chains generate constant physical variability; dwell time, route deviation, temperature swings, handling events. When those signals are captured consistently, AI can augment human expertise and trigger earlier intervention. Without that structured, continuous data foundation, the value of AI remains constrained.
Session: From theory to operations: how investors evaluate supply chain technology — with Ty Findley (Ironspring Ventures), Trevor Adey (McVestCo) and Amit Chaturvedy (SE Ventures).
The discussion was a useful reality check: investors are no longer underwriting visibility as a novelty. They are underwriting deployment, unit economics and the ability to scale beyond pilots.
That investor framing mirrored what operators told us in conversations: not every pallet, case or reusable asset can justify tracking hardware. Fixed reader deployments introduce blind spots and capital intensity. Cellular trackers work for certain scenarios but can face scaling constraints in high-volume environments.
There is growing preference for deployment models that scale without heavy hardware investment and that align with asset value. Solutions that can be activated quickly and expanded incrementally are more likely to move from pilot programmes into broad operational use.
This pragmatic stance reflects the margin pressures that define logistics and retail operations. Sensing layers must respect those constraints while delivering measurable performance gains.
Some of the most instructive conversations we had were with cold chain and perishable freight forwarders operating in highly dynamic conditions. Many receive urgent pickup requests with minimal notice, rely on contractors and cannot pre-install infrastructure or pre-assign tracking hardware because shipment profiles change constantly.
When we demonstrated that a contractor could download the Blecon Agent onto their existing Zebra device, apply a disposable Bluetooth label at pickup and establish immediate shipment visibility, the response was decisively positive. Tracking could be initiated at the moment of need using the device already in the driver’s hand, without altering established workflows or introducing advance planning complexity.
This approach resonated because it reflects the operational reality of logistics, which is fluid, time-sensitive and defined by exceptions rather than idealised processes.
Manifest 2026 reinforced that supply chain performance is increasingly shaped by the convergence of product-level sensing, infrastructure-light deployment, disciplined economics and AI systems powered by high-quality data.
When frontline devices participate in a distributed sensing fabric, visibility gaps narrow. When assets carry contextual intelligence, decision-making accelerates. When deployment models match real-world workflows, scalable adoption follows.
Supply chain leaders are advancing solutions that combine operational practicality with measurable impact and that focus will define the next stage of performance across logistics and retail networks.
To learn more about the Blecon Agent or to launch a proof of value pilot visit: www.blecon.net/agent
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