Most commercial planning tools were designed for a stable world. That world is gone. Mondra CEO Jason Barrett on the data problem sitting underneath margin pressure, stock gaps and supplier tension, and what to do about it.
There is a version of this piece where I talk about supply chain resilience in the abstract. Where I use words like "volatility" and "disruption" and "complexity" and leave you nodding along without being any better off.
I've read enough of those. I'm not going to write one.
What I want to talk about is a more specific, more uncomfortable problem. The tools most commercial teams use to plan, buy and develop products were designed for a world where things mostly stayed put. Where ingredient costs moved gradually, where supplier relationships were stable year on year, and where the biggest variable in your planning assumptions was consumer demand.
That world ended a while ago. And most planning infrastructure hasn't caught up.
The problem isn't effort. It's what your tools can actually see.
I talk to buyers, category planners and product developers regularly. Good, sharp people, who work hard. Almost universally, they are operating with a narrower view of their supply chains than the risks they're being asked to manage actually require.
Most commercial planning tools were built around supplier-level or category-level data. They tell you what your tier 1 suppliers are doing. They give you a picture of cost at a category level. That was fine when the things most likely to disrupt you were your direct relationships.
It isn't fine now, because disruption doesn't start at tier 1. It starts two or three steps back, at the level of raw ingredients and the farms and regions that produce them. By the time it reaches your direct suppliers, it's already in your cost base, your availability and your margin.
If you're a buyer, you may be negotiating based on cost assumptions that were accurate three months ago and aren't now. The supplier sitting across the table from you knows this. You don't have the data to challenge it or confirm it so you're playing a game of poker with an incomplete deck.
If you're a category planner, your range decisions are built on supply availability that could shift materially before the range even lands. The question you're trying to answer "what should this category look like in six months" depends on inputs your current tooling doesn't give you.
If you're in product development, you're designing around ingredients whose price trajectory and availability will look very different by the time your product reaches a shelf. You're making formulation decisions in the dark about a significant portion of your cost base.
None of this is your fault. It's a data problem.
What the missing layer actually is.
The thing most commercial teams are missing isn't another dashboard. It isn't a better report. It's ingredient-level intelligence, mapped to your actual products, updated as conditions change, and connected to the decisions you're already making every day.
That's a more precise way of describing what a digital twin of your supply chain does. Not a buzzword, an actual working model of every SKU you sell, built from the ingredient up, with the risk signals that matter embedded in it.
Mondra maps the complete supply chain of every product a food retailer sells. Every ingredient, every node, from farm to finished product. We use satellite telemetry, farm-level ground truth data from around 500,000 interviews a year, economic and financial signals, and real-time event monitoring. The result is a living, dynamic model of your product portfolio that updates as the world changes, rather than sitting static until someone runs the next report.
This matters because food supply chains are not static. Products change. Supplier relationships evolve. Climate events, geopolitical shifts, commodity cycles, energy costs. All of it is moving all of the time. A point-in-time view, however detailed, starts going out of date the moment it's produced.
What commercial teams actually need is the ability to see what's coming before it arrives, and to model the impact of their decisions before they make them.
What this looks like in practice.
A retailer we worked with recently used ingredient-level supply chain modelling to map exposure across a core category. They weren't looking for a headline risk. They were trying to understand, at product level, where they were most exposed to sourcing volatility and what the cost and availability implications looked like under a range of scenarios.
What they found surprised them. The highest-risk nodes weren't where they'd expected. Tier 1 looked relatively stable. Two levels deeper, there were concentration risks and geographic exposures that were not visible in their existing tooling at all.
They acted on it before disruption occurred. Sourcing changes were made in time to protect both availability and margin. The projected reduction in disruption exposure across that category was over 15%.
That is the difference between reacting and planning. And it's increasingly the difference between the commercial teams that protect margin and the ones that explain to the board why Q3 missed.
Why this is a now problem, not a future one.
I want to be honest about why there us more urgency than two or three years ago.
Trading conditions have genuinely changed. Tighter margins, more volatile input costs, more complex supplier relationships, and a regulatory environment that is adding reporting requirements at the same time as commercial pressure is intensifying. Most businesses are being asked to do more with less, and to make better decisions faster, with tooling that hasn't fundamentally changed.
At the same time, the capability to do this properly now exists at a scale it didn't before. AI-driven supply chain modelling, the kind that can fill data gaps intelligently and keep product-level records current as conditions change, is no longer the exclusive territory of the very largest retailers with the biggest internal data teams.
This is genuinely accessible technology now. The barrier is awareness, not cost or scale.
The commercial teams that will win.
The businesses I see getting ahead of this aren't doing anything exotic. They're making one foundational change. Treating supply chain intelligence as a commercial input rather than a risk function output.
When carbon, cost, availability and supplier risk are all visible at product level, connected to the same data layer, and updated continuously, they stop being separate problems that different teams manage in isolation. They become one question. "What decisions should I make today?" (With full visibility of what they'll cost and what they're exposed to…)
Category planning, buying decisions, product development, supplier negotiations. All of it gets sharper when you can see the whole picture.
That's not a distant ambition. For the retailers and suppliers we work with, it's what a normal Tuesday looks like.
Jason Barrett
CEO and Founder
Jason Barrett is the CEO and co-founder of Mondra, where he leads strategy, partnerships, and coalition-building to accelerate climate impact at scale. With 20+ years in digital transformation, he previously co-founded a $50m tech business and is recognised as an award-winning systems thinker. Jason launched the BRC Mondra Coalition in 2022 to address Scope 3 emissions across the UK food sector using AI and automated Life Cycle Assessment (LCA).
