.png)
Fresh Futures Global Collabathons: Summary Report
- 4 days ago
- 8 min read
Key Findings
In March and April 2026 hundreds of participants from 22 countries across two global sessions surfaced a consistent set of problems — and validated a clear direction for solving them. Here is what the industry told us, and what is moving as a result.
The pilots participants voted to advance:
Freshness Indicators and Predictive Insights — connecting harvest data and IoT sensor data through Smart Data Escrow so every supply chain actor receives predictive information before product arrives, not after it is rejected.
Smart Digital Link Fair Exchange — a conditional data exchange mechanism that lets growers and retailers share data with each other without either party going first or giving up control.
Food Service Freshness and Dynamic Incentives — a measurement and incentives platform that ties commercial terms to actual quality performance, replacing anecdote with data.
Watch the videos below then continue with the full 2026 Collabathon Report
Our Global Scope
Two sessions on March 31 and April 16, 2026, designed to reach every major time zone in the fresh produce industry. Hundreds of participants across 6 continents showed up: growers, distributors, retailers, logistics providers, standards organizations, technology companies, and food service operators.
The goal was not to present at people. It was to put real pilot concepts in front of practitioners from across the supply chain and let the industry tell us what was worth building, what was missing, and who would actually do the work.
The sessions were structured in three rounds. In the first, participants reviewed pairs of pilot concepts and voted on which one to advance. In the second, the winning pilot got refined — gaps identified, assumptions challenged, roles clarified. In the third, participants built out the process flow together on a shared board, mapping events, people, and unanswered questions in sequence.
What the Industry Said
Before the pilots, the context. Last October, retailers came into a closed-door session and said what they don’t usually say in mixed company: that they’re flying blind on freshness, that data creates compliance burden without delivering value, that commercial structures make it nearly impossible to reward good performance, and that they’ve heard promises before. The Collabathons were the industry’s chance to respond.
Across both sessions and every room, three tensions kept resurfacing.
Data stops at every handoff.
Every actor generates it. Growers capture maturity and handling data at harvest. Sensor providers track temperature, humidity, and shock events through transit. Distributors log receipt conditions. Retailers record what sold and how long it lasted. None of it connects. By the time anyone can act on a problem, the product is already rejected and the conversation has turned to chargebacks. Participants from retail, logistics, and growing operations all described versions of the same situation: the warning signs were there, earlier in the chain, but nobody upstream ever saw them.
Quality is rewarded with words, not money.
In food service especially, a supplier can deliver exceptional product for years and still face the same price negotiation as a supplier who cuts corners. There are no standardized metrics. There’s no data layer that translates performance into commercial terms. Quality becomes anecdotal, and when budgets tighten, the lowest price wins. Participants named this as a structural problem, not a relationship one. The incentives aren’t broken because people don’t want to do the right thing. They’re broken because there’s no measurement system that makes doing the right thing financially visible.
Standards exist. The problem is adoption.
This came up with some force. More than once, participants pushed back on the idea that the industry needs new standards. The question, fairly put, was: if stakeholders aren’t following the standards we already have, why would something new be any different? The answer the group worked toward was that the problem isn’t the standards themselves. It’s that “good” means something different depending on who’s buying, what region you're in, and what the commercial relationship looks like. Harmonization needs to be practical — getting existing standards followed consistently, not adding another layer of requirements.
A few other things surfaced worth naming:
A participant described how quality loss on their supply chain happens not at sea but in last-mile ground distribution and inadequate backup power at destination — a reminder that the supply chain looks very different depending on where you’re standing in it.
Another described how economic volatility makes year-over-year demand data nearly useless for forecasting, creating a chain reaction that affects grower specifications, timing decisions, and pricing agreements far upstream.
A distributor described food service demand swinging dramatically week to week and asked a reasonable question: how do you create quality agreements when demand itself is unstable?
These were the same problem showing up in different contexts.
What Each Room Worked On
Three pilot themes ran across both sessions. Each room voted on which concept to advance, then spent the remaining rounds stress-testing it.
Freshness Indicators and Predictive Insights
The most broadly voted pilot across both sessions. The concept: connect freshness data captured at harvest and packing with real-time condition data from IoT sensors throughout transit, so that every actor in the chain receives the information relevant to their role before the product arrives — not after it has been rejected.
The scenario that landed hardest: two pallets, same grower, same variety, same pack date, same truck. One arrives in perfect condition. The other gets rejected. The data to answer what happened exists — captured at harvest, in transit, at the dock. It just never connects. This pilot is about closing that loop.
Participants spent the build rounds mapping the full journey from harvest to retail receiving, and the conversation sharpened around several open questions. What maturity indicators actually predict downstream failure? How do you link sensor data to specific lots? What triggers an alert, and who needs to see it? One insight that came up repeatedly: the technical work is only part of it. The harder work is trust — getting trading partners to share data they have always treated as proprietary.
Getting growers credit for quality, not just volume, was named as the real incentive for participation on the supply side.
This pilot is already underway. The Collabathon rounds added detail, identified gaps, and produced new volunteers.
Smart Digital Link Fair Exchange
The standoff this pilot addresses is familiar: growers want point-of-sale data from retailers. Retailers want harvest and handling data from growers. Neither shares first. The grower worries about giving up negotiating leverage. The retailer worries about exposing performance gaps. Both hold their cards close, and both lose the value that shared data could create.
The pilot’s answer is conditional exchange. Both parties commit to terms before either reveals anything. A neutral resolver ensures neither can game the system. Verifiable credentials allow both sides to trust what they receive. No one goes first. No one controls everything.
The room’s most pointed debate was around neutrality. One participant put it plainly: there’s no such thing as a neutral party. The response, worked out in the room, was that neutrality doesn’t require impartiality — it requires certification. Escrow in real estate works the same way.
The pilot is building that infrastructure for sovereign data.
What also emerged was the relationship between this pilot and the freshness indicators work. Several participants noted that in order for a predictive insights system to function, you first need to solve the permissions problem for data access. The two pilots are not competing — they are complementary. One creates the data. The other creates the conditions under which it can be shared.
Food Service Freshness and Dynamic Incentives
Food service has different quality expectations than retail, and commercial dynamics that make consistent quality hard to sustain. A supplier delivers exceptional produce — fresh, on spec, handled correctly. The operator appreciates it. But at contract renewal, the conversation is still about price. Because there's no data, no standardized measurement, no mechanism that translates performance into commercial terms.
Quality becomes anecdotal, and when budgets tighten, the lowest price wins.
This pilot creates the measurement layer that makes incentives possible. Quality data from the field, sensor data from transit, and receipt data from the operator all flow into a shared insights platform that calculates performance against agreed metrics. The output: standardized quality metrics that food service operators actually care about, incentive calculations showing whether the supplier hit the targets, and commercial terms that can reflect actual performance.
The room’s hardest question was also the most important one: what is quality, exactly, in food service? The conversation surfaced how much variation exists — between white tablecloth and chain accounts, between commodities, between regions. One participant described receiving field quality reports that routinely don’t match what arrives at the dock, compounded by food service demand that can vary fivefold week to week. Standardizing quality metrics across that kind of variability is genuinely difficult. The group’s view was that the pilot needs to answer that question by running it, not by resolving it in theory first.
The room also named this plainly: when money is on the line, measurement has to be trusted by both sides.
That's the hard part, and this pilot is designed to surface where trust breaks down so the industry can build the infrastructure to support it.
The Pilots Are Patterns, Not Finished Programs
One thing worth repeating from the sessions: none of these pilots is a closed design. They are templates. The point of running them is to generate specific learnings — what data actually matters at each handoff, what triggers an alert, where trust breaks down, which commercial terms can realistically tie to quality data — and then feed those learnings back into the technical working groups so that the next company to run a pilot doesn’t have to start from scratch
.
The common infrastructure underneath all three pilots is worth naming: GS1 identifiers, GS1 Digital Link, and Smart Data Escrow — a protocol that lets participants share data with specific parties without sharing it with everyone. You control who sees what, under what conditions, without giving up ownership. That architecture is what makes these pilots interoperable rather than one-off experiments.
The cycle the sessions kept returning to: pilots generate real data, real data informs standards, standards lower the barrier to entry for the next pilot. It only works if the pilots run. That's why the volunteer lists matter.
Getting Involved
Pilots are progressing. Volunteers have been identified across all three pilot themes from both sessions, and the next step for each is moving from mapped concept to operating environment. If you indicated interest on the board and haven’t heard from us, you will.
We are actively looking for companies to sponsor and lead pilots. If you’re ready to fund a pilot and shape how it runs, we want to hear from you! Contact us at info@scotf.org. If you’d like to be considered as a participant when a relevant funded pilot opens, submit your interest and we’ll be in touch when a fit emerges.
Working groups are active and open. If your expertise belongs in one of these conversations, the groups want to hear from you. And if you came to one of these sessions and left thinking this work is worth being part of more formally — as a working group member, a steering committee participant, or a pilot sponsor — the best next step is a direct conversation.
The Collabathons were the gut-check. The report is the record. It documents what the industry said in October, what the global sessions validated, and — with unusual candor — where the gaps are that no pilot has yet claimed.
Continue with the full 2026 Collabathon Report
Actions available for you
Interested in joining a pilot? Pilots are funded programs — if you’re ready to sponsor or co-sponsor, contact us at info@scotf.org to start the conversation. If you’d like to be considered as a participant when a relevant pilot opens, please also reach out and we’ll be in touch when a fit emerges.
Not ready for a pilot? You can still get involved:
Join a technical working group — free and open to all, either join our existing Shelf Life Prediction and Dynamic Incentives and Harmonized Standards and Smart Data Escrow or join the waitlist for the Full Circle Integration, Innovation & Technical Programs working groups.
Join a steering committee — for organizations ready to lead and shape the direction of this work.
Comments