Scaling Alt Protein production: lessons from high-tech manufacturing
Previously, I wrote about the importance of the protein transition and the role that data can play in it. Over the past few months, I spoke with 20+ organizations in the plant-based food value chain, and I'd like to share my observations.
The Challenge: Rapid Growth Can Lead to Chaos and Complexity
In the Netherlands, we're seeing a new phase of Alt Protein manufacturing. Nutreco (cell-cultured meat), Revyve (plant-based ingredients), and Those Vegan Cowboys (precision fermentation) all face the same challenge: scaling up their production. From experience, I know this is a critical moment where the right data strategy and infrastructure can make the difference between smooth growth and costly chaos.
Do You Recognize These Signs?
If you work at a scale-up in the plant-based sector, you might recognize these warning signs:
Teams manually export data to Excel for basic analyses, multiple versions of this data circulate, and it's difficult to find the "truth"
It takes days to update financial forecasts, different sources say different things. Much time is spent cleaning, merging, and reporting on this data.
R&D teams can't easily access production data. Machines produce data in various formats stored in different places. As a result, researchers spend much time repeatedly solving similar problems like cleaning data, downloading files, and performing checks. This leaves less time for adding value through actual "research."
Sustainability metrics like CO2 emissions are difficult to track, while regulators and consumers are demanding this information.
Quality controls happen in isolated systems, while quality assessment is formed by combining these systems. This involves a lot of manual work and "estimation" which, ironically, reduces the quality of the assessment.
The Solution: A Future-Proof Data Platform
A central data platform is key to effective growth. In the food industry, there's still catching up to do in this area. Fortunately, we don't need to reinvent the wheel - there's a proven approach to setting up such a platform.
A well-designed data platform works according to a standard architecture with four core functions:
Collecting data from all sources within the organization
Cleaning data for consistent analyses
Enriching data by combining sources and applying analyses
Making data available to analysts, researchers, BI tools, and other users
Setting up this infrastructure was an expensive investment a few years ago but can now often be achieved within a few weeks.
Lessons from High-Tech Manufacturing
The plant-based food industry faces similar challenges as the high-tech manufacturing industry did several years ago. And we can learn from that. At multiple manufacturing scale-ups, we saw their data volume and variety quadruple in one year - growth that was well managed because the data infrastructure was solid.
Once a data source is connected, the entire organization can use it (in a secure way). Automation ensures manual processes are a thing of the past, and talent has room to focus on real value creation.
At these organizations, everyone now works on a central data platform where all sources come together. Whether you work in R&D, Finance, or Operations: the right data is always accessible and up-to-date. This leads to:
Faster innovation cycles for both product and production process
Reliable procurement and inventory management
Increased efficiency across all teams. Searching for and cleaning data takes significantly less time.
Data-driven decision-making at all levels
My colleague Stijn wrote about these and other use cases.
The Parallel with Plant-Based Food Production
The data infrastructure that has become standard in high-tech manufacturing is rarely seen in plant-based food. Yet there's every reason for it, given additional challenges such as:
Strict food safety regulations
Complex sustainability reporting
Track & trace requirements
CO2 emission monitoring
Transparency about ingredient origins
Food producers can now make this catch-up relatively cheaply; with the advent of LLMs and major investments in data tooling, we can speak of a leapfrogging effect.
Start Today
The protein transition is at a tipping point. While the sector is professionalizing rapidly, the gap between leaders and laggards is growing. Tomorrow's winners are the companies that get their data infrastructure in order today.
Are you facing the challenge of scaling up? If you want to tackle it yourself, this might be a good starting point: Embracing data & AI: a practical guide to getting started.
I'm also happy to think along with you. Send me an email (jelle@wolk.work), a message on LinkedIn, or schedule a 30-min call right away.
Together, we'll make the protein transition not just faster, but also data-driven.
