The Lux Take
Technoeconomic analyses (TEAs) are valuable for biomanufacturing scale-up, but only when they distinguish validated data from assumptions and separate optimized projections from early operating scenarios.
Biomanufacturing companies, from startups to corporates, often present attractive production cost ($/kg) projections in discussions with investors, partners, and other stakeholders. However, many fail to achieve these targets as processes scale. Teams typically develop early TEAs before validating key assumptions around operating capacity, downstream processing, utilities, waste management, feedstock procurement, and commercial-scale performance. Although this uncertainty is inherent to early-stage development, developers often treat these models as reliable predictors of future production costs. Consequently, stakeholders may place greater confidence in early cost projections than the underlying data warrant, creating misaligned expectations as projects advance toward demonstration and commercial production.
To identify common sources of disconnect between modeled and realized economics, Lux interviewed five experts across engineering firms, in silico TEA development platforms, and large-scale biomanufacturing operations to understand the most common missteps companies make when applying TEAs to biomanufacturing.
Early TEAs are often fixed forecasts rather than early decision support tools.
Many developers approach TEAs with a single objective: determining whether a product can be produced at a target production cost. Interviewees consistently identified this mentality as one of the most common ways early stage models are misapplied. The most useful TEAs do not provide precise future production costs early in development. Instead, they help determine whether a process is directionally viable, identify which variables have the greatest impact on economics, and highlight where additional data generation should be prioritized.
“TEAs can be a good leading indicator of ‘is this possible’ and inform what you need to pay attention to the most. Essentially, proceed or not.” – Industrial biotechnology scale-up advisor
As processes progress from laboratory development to pilot, demonstration, and commercial scales, assumptions around feedstock pricing, process performance, downstream recovery, utility consumption, labor requirements, waste generation, and facility design will undoubtedly change. Early TEAs therefore contain substantial uncertainty by design. Rather than focusing on a single cost projection, experienced TEA developers use sensitivity analyses and scenario modeling to identify which assumptions matter most and where development efforts should be concentrated.
Interviewees repeatedly emphasized that the value of an early TEA lies less in predicting an exact future production cost and more in identifying where additional technical, operational, or commercial work is required. Strong models can help teams uncover hidden assumptions, prioritize data generation, and identify process bottlenecks long before they become expensive scale-up challenges.
“TEAs are great because they expose whether the client has a process problem, an R&D planning problem, or a communication problem.” – Biomanufacturing engineering and consulting firm
Rather than asking whether an early TEA proves a process can achieve a specific cost target, companies should focus on understanding which assumptions drive the result and what data must be generated next to improve confidence in the model. The strongest TEAs are not those that project the lowest production cost, but those that clearly identify where uncertainty exists and how that uncertainty can be reduced through additional development work.
Developers often rely on models of fully optimized facilities, overlooking elevated early operating costs.
Another common theme across the interviews was the tendency to model mature, optimized facilities rather than the realities of early commercial operation. While optimized plant economics remain important for understanding long-term viability, they often fail to capture the operational inefficiencies that characterize the first several years of production. As a result, many companies develop expectations around production costs that may eventually be achievable, but not on the timelines assumed in early models.
Interviewees consistently noted that commercial facilities never reach target operating capacity immediately after commissioning. Instead, operators often spend months to years optimizing processes, modifying equipment, refining operating procedures, training staff, and addressing unforeseen bottlenecks. These realities are particularly pronounced in biomanufacturing, where biological variability, sterility requirements, contamination risk, and process complexity introduce additional operational challenges.
“Most TEAs that people put together are for an optimized plant, but there are upfront start-up costs and capital modifications. You’re going in and fixing a bunch of equipment in the first six months, but with lower operating capacity for at least 12–18 months, despite typical 3-month assumptions.” – Commercial-scale biomanufacturing company
Several interviewees emphasized that facilities frequently require months to years to approach modeled operating assumptions. During this period, many fixed costs remain largely unchanged despite lower production output. Sterility infrastructure, steam generation, associated utilities, quality control systems, and labor requirements often resemble those of a fully operational facility even when production volumes are substantially lower. As a result, costs do not scale proportionally with output in the way many early models assume.
When asked about the elevated costs in the initial stages of plant operation relative to a fully optimized facility model, one expert estimated:
“Costs could be 25% higher in the first year. For example, energy costs are going to be very inefficient in the first year as you’re repeatedly starting stuff up and shutting stuff down.” – Commercial-scale biomanufacturing company
Interviewees also highlighted operational assumptions that appear reasonable on paper but become difficult to achieve in practice. Turnaround times, labor requirements, and maintenance schedules can all influence annual production capacity, and even small deviations from modeled assumptions can have outsized effects on facility economics when repeated across dozens or hundreds of batches per year.
“There’s over-optimism going into TEAs about how a plant will actually work. If you’re doing batch, what’s the actual turnaround time? Your optimized model might say, ‘run this for 43 hours + 10 hour cleaning,’ but does that align with labor? Or, in practice, do you have to run for 3 days and lose 20% of your plant efficiency up front?” – Industrial biotechnology scale-up advisor
“It takes time to get turnaround down from 24 to 12 hours, and some of the assumptions from early stage developers don’t take that into account.” – Industrial and food biotech consultant
The strongest commercial-scale TEAs therefore evaluate two separate realities: what a facility may ultimately achieve once optimized, and what it is likely to experience during its first year(s) of operation. Modeling both scenarios provides a more realistic framework for capital planning, commercialization timelines, and partner expectations than relying on steady-state economics alone.
Many TEAs focus on productivity while failing to fully account for external and indirect cost drivers.
Many early TEAs look at the most direct or obvious levers to impacting process economics such as yield, titer, productivity, conversion efficiency, and downstream recovery. These metrics are essential, but they do not fully determine commercial viability. Interviewees emphasized that production costs are often shaped by assumptions that sit outside the core bioprocess, including feedstock procurement, logistics, labor, utilities, waste management, taxes, transportation, and supply chain variability. When these inputs are treated as fixed values or generic placeholders, TEAs can appear more precise than the underlying data support.
Feedstock assumptions are the clearest example. Early TEAs often rely on benchmark pricing or market averages, but commercial feedstock economics depend on contract structure, procurement strategy, supplier reliability, quality specifications, storage requirements, transportation distance, and regional availability. A model may include a feedstock price, but that number often remains provisional until a company has negotiated supply terms and validated whether the material can be delivered consistently at the required quality and volume.
“You don’t actually know your feedstock costs until you’ve negotiated a contract around it.” – Industrial biotechnology scale-up advisor
Even after contracts are established, feedstock economics remain exposed to external variability. Events affecting supply, quality, transportation, import costs, and regional availability can materially change the economics of a project over time. Interviewees noted that many TEAs account for technical uncertainty more explicitly than commercial or supply chain uncertainty, even though these external variables can alter the assumptions behind the model as much as process performance does.
“Models often underestimate the likelihood of challenging scenarios like poor harvest cycles, droughts, wars, and environmental disasters.” – Commercial-scale biomanufacturing company
The same issue applies to costs outside the core process boundary. Yield, titer, productivity, and recovery rates often receive the most attention because they are closest to the technology, but commercial production costs also depend on indirect fixed costs and site-level operating requirements. Many crucial, noncore process metrics are treated as secondary priorities, but collectively, they shift economics materially as projects move toward demonstration and commercial operation.
“Some of the main things that are overlooked are indirect fixed costs, including labor, marketing and sales, taxes, transport, and logistics.” – Commercial-scale biomanufacturing company
“All water and wastewater treatment is a massive blind spot.” – Biomanufacturing engineering and consulting firm
The strongest TEAs therefore make model boundaries and assumptions on maturity and uncertainty explicit. Rather than only refining direct process metrics, companies should identify which external and indirect cost assumptions are still provisional, which are most likely to change, and which could materially alter the projected production costs. In many cases, understanding the range of possible outcomes around feedstocks, logistics, utilities, waste management, and indirect costs is more valuable than increasing precision around a process model built on assumptions that remain commercially unvalidated.\
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