A biotechnology company had developed a feed additive intended to improve the health and growth of livestock. The scientific result was promising, but customers did not buy an ingredient merely because it changed an animal's biology. They needed to know whether the change was worth more than it cost.
The Customer's Problem
Animal producers operate on biological and economic margins at the same time. Feed is a major cost; days to market consume housing, labor, and capital; mortality destroys the value accumulated in an animal; and buyers may pay differently for weight, leanness, fat distribution, or other carcass characteristics. An additive could influence several of those outcomes, and the benefits did not all arrive in the same form.
The product might improve feed conversion, allowing the animal to gain the same weight with less feed. It might accelerate growth and reduce days to market. It might lower mortality or improve the proportion of saleable meat. It could also alter body composition in a way that received a premium from one customer and little value from another.
That made a biological claim such as “three percent better growth” insufficient. Three percent of what, measured over which period, under what diet and housing conditions, and converted into how many dollars per animal? The producer also needed to know whether the result was consistent enough to change purchasing decisions.
Connecting Dose to Dollars
We built a model that joined four layers: dose, biological response, operating consequence, and economic value. Dose-response relationships were not assumed to be linear. A small dose could produce little benefit, a middle range could produce a strong response, and additional product could reach a plateau or even create an adverse result.
For each candidate dose, the model translated trial evidence into expected feed consumption, weight gain, time to market, mortality, carcass value, and other measurable consequences. It then subtracted the additive's cost and exposed the assumptions so that the company and its customers could test different feed prices, animal values, and response curves.
The commercial question was therefore not “Does the additive work?” It was “At what dose does the expected incremental value exceed the incremental cost, and how sensitive is that answer to the customer's operation?” A product could be scientifically effective but economically unattractive at the proposed price; it could also be highly valuable for one production system and marginal for another.
From Biological Claim to Business Case
The client commissioned our development of the model as a sales tool for its high-margin enzyme additives. The work converted a technical claim into a customer-specific business case. Salespeople could explain the value in the producer's own units rather than relying on general statements about animal health. Product managers could see which outcomes drove willingness to pay, and researchers could identify which uncertainties were most important to resolve in later trials.
The model did not replace field evidence. It made the evidence commercially legible. For a feed additive, the strongest proof is not a laboratory effect in isolation, but a defensible chain from dose to biology to operating performance to dollars.
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