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Scaling Where It Makes Sense: How Techno-Economic Analysis (TEA) Guides Plant Location, Scale, and Timing

  • Writer: Gustavo Valente
    Gustavo Valente
  • Oct 21
  • 5 min read

Every founder scaling a biotech or foodtech process knows the feeling, lying awake at 2 a.m., running scenarios in their head:


Where should we build?
When will be the right time?
And how big before it’s too big?

Techno-Economic Analysis (TEA) gives you a framework to answer all three, with data, not gut feeling or personal preferences. It translates technical progress into business logic, helping founders make decisions that investors trust.



1️⃣ WHERE — Site & Region Selection


Choosing where to manufacture isn’t about family ties, team preferences, or aspirational zip codes, it’s about what truly drives your unit economics.


In every TEA we run, some of the top three cost drivers are:


  • Labour – Hourly rates vary dramatically across regions, but so do efficiency, shift structures, and overheads. TEA normalises these differences to reveal the true labour cost per kilogram produced.

  • Utilities – Power, steam, water, and waste treatment often exceed labour as the biggest OPEX line.

  • Logistics – Freight, cold chain, and customs delays can erase any savings from cheaper power elsewhere.


Then come the hidden costs: downtime from unreliable utilities, currency fluctuations, long permitting timelines, or import dependencies that stretch cash flow.


A good TEA integrates all of this as a “location layer”, a set of numeric parameters that lets you simulate how each site option (US, EU, LATAM, Asia-Pacific, etc.) affects COGS, CAPEX, and IRR.


That way, the question “Where should we build?” becomes a quantitative decision, not a debate of opinions.



2️⃣ WHEN — Timing the Scale-Up


Timing is one of the trickiest decisions for any startup. Build too early, and you burn capital before demand exists. Wait too long, and you miss your window.


The cost of waiting and the cost of being early are both real, and TEA helps you compare them.


In early stages, using contract manufacturers (CMOs) or pilot partners often looks expensive, but it’s part of your launch cost, not waste. Those first batches validate your product, generate investor data, and help prove market fit. Investors expect this; it’s part of the normal path to market.


A TEA can model this “bridge-to-plant” period, showing how temporary outsourcing affects your burn rate, margins, and payback compared to an early build.


Once your process hits stable yields and repeatability, you can quantify the learning-curve ROI; each additional batch reduces uncertainty and sharpens future CAPEX decisions.

In short, TEA helps founders time their first build as strategically as they choose its size and location.



3️⃣ HOW BIG — Economies of Scale Done Right


“Bigger is better” stops being true once you leave the spreadsheet and enter the real world.

Every TEA models a Minimum Economic Scale (MES), the point where the cost of production stops dropping meaningfully with additional capacity. Beyond that point, doubling your reactor size might reduce your cost per kg by only 5%, while doubling your risk exposure and capital requirements.

Yes, some teams manage to raise incredible amounts of capital, but many don’t survive past that fundraise, because their economies of scale weren’t sustainable once reality kicked in.


The smartest founders test several scale scenarios before committing:


  • A demo or pilot plant, small, fast-turnaround, and perfect for learning from real data.

  • An intermediate scale, sometimes supported by a CMO, to validate automation, reproducibility, and reliability.

  • A full commercial line, large enough to prove long-term economics and investor returns.


By comparing these scenarios in a TEA, factoring in CAPEX, yield risk, and timeline, you can see where your economic sweet spot really lies.


Don't build the biggest plant you can fund, but the smallest one that can prove your business model and scale sustainably.


4️⃣ Pulling It Together — The Decision Map


Once your process data and cost models are ready, TEA becomes more than analysis — it becomes a strategic compass.Here’s how founders use it to navigate the Where,

When, and How Big decisions across different stages.


⚙️ Scenario 1 – “Build Small Now”


You’ve proven yields and have buyers waiting, but scale uncertainty is high.👉 TEA shows: Economies of scale flatten early; a small pilot with fast turnover gives the best $/kg learning ratio.✅ Outcome: Build a 1 L–10 m³ demo line, capture data, and de-risk before committing to a larger expansion.


⚙️ Scenario 2 – “Stay Small Until the Product Is Locked”


Technology works, but product specs (texture, purity, shelf-life) are still shifting.👉 TEA shows: Each formulation tweak can affect downstream costs by ±10–30 %.✅ Outcome: Use CMOs or repeat pilot trials until formulation and QC specs are fixed — then freeze the design and scale confidently.


⚙️ Scenario 3 – “Delay Until Market Traction Is Secured”


CAPEX looks attractive, but demand forecasts are still soft.👉 TEA shows: Plant utilization below 60 % destroys IRR and investor confidence, even with incentives.✅ Outcome: Focus on commercial partnerships first; revisit the build once volume commitments are expected to cover at least 70 % of the target plant capacity.


⚙️ Scenario 4 – “Scale Where Utilities and Feedstock Make Sense”


The same process can yield 25 % lower production costs in a region with cheaper steam, glucose, fuel, or electricity.👉 TEA shows: Utility and feedstock prices outweigh logistics costs by roughly 2×.✅ Outcome: Manufacture near feedstock supply; export the final product.


⚙️ Scenario 5 – “Co-Locate with a Partner or Waste Stream”


A partner’s waste glycerol or CO₂ stream can feed your process.👉 TEA shows: Feedstock credits can cut OPEX by up to 40 %, and shared utilities can reduce CAPEX by ~20 %.✅ Outcome: Pursue a brownfield or industrial-symbiosis model instead of a greenfield site.


⚙️ Scenario 6 – “Automate Later, Not Now”


Automation promises lower labour costs, but your process is still evolving.👉 TEA shows: Automation might add $2 M in CAPEX but save only ~$150 k/year at current

utilization.✅ Outcome: Defer automation to Phase 2; invest first in operator training and robust data capture. I’ve seen TEAs where automation benefits disappeared entirely once regional energy and labour costs were factored in — it’s not always worth it early on.


🧭 The Decision Map


Discover how Techno-Economic Analysis (TEA) helps biotech and foodtech founders decide where, when, and how big to build — with data, not guesswork.

5️⃣ Final Thoughts


At Sustech Innovation, we help founders build Techno-Economic Analyses (TEAs) that clarify not just if a technology works, but where and when it becomes a business.


If you’re preparing for fundraising, site selection, or process scale-up, a TEA can turn your vision into a roadmap investors trust.


👉 Explore our TEA Readiness Checklist to see if you’re ready for your next scale decision.


Gustavo Valente

Sustech Innovation

WhatsApp +52 55 3405 0552


This post summarises lessons from real TEA projects. The detailed models and methods behind these analyses are part of Sustech Innovation’s proprietary know-how and are used under confidentiality in client projects.


 
 
 

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