#399 2000 · Unilever · Manufacturing / industrial engineeringproxy-test
Unilever's best mathematicians calculated the theoretically perfect nozzle and it still clogged — so the company stopped calculating and started breeding nozzles instead.
the problem
a design problem is too complex for direct calculation to solve, even by genuine domain experts working from first principles
background
Unilever's detergent manufacturing process forced boiling, high-pressure chemical slurry through a spray nozzle to produce detergent granules, but the nozzles kept clogging and produced inconsistent granule sizes, a persistent production problem. The company's first approach was the obvious one: bring in fluid-dynamics mathematicians to calculate the theoretically optimal nozzle geometry from physical first principles.
That expert-calculation approach failed — the mathematically derived optimal design still clogged in practice, because the real fluid dynamics inside the nozzle were too complex to fully capture in a tractable calculation. Rather than continue refining the theoretical model, Unilever handed the same problem to a different kind of expert: biologists, who approached it with no equations at all.
the move
The biologists treated nozzle design as an evolutionary process rather than a calculation: starting from an existing nozzle, they generated ten random variations, tested all ten against the real clogging and granule-consistency problem, kept whichever variant performed best, generated ten new random variations of that winner, and repeated the cycle for 45 generations.
the payoff
After 45 generations and 449 failed variants, the process converged on a nozzle design over 100 times more efficient than the original — reliably avoiding clogging and producing consistent granules — even though the resulting shape was strange enough that no one on the team could fully explain in physical terms why it worked better than the mathematically 'optimal' design the fluid dynamicists had calculated.
what came after
The Unilever nozzle case is a standard teaching example in innovation and complexity science for the limits of theory-first design on genuinely complex problems, and for evolutionary or 'generate-test-select' optimization as a viable alternative when direct calculation fails — the same underlying method now underlies genetic algorithms in engineering design, machine-learning architecture search, and pharmaceutical compound optimization.
references
- [1]Sex and Soap Powder, Trial and ErrorThe Squawk Point, 2016squawkpoint.com
- [2]What 449 'Failures' Can Teach Us About SuccessSam Thomas Davies, 2020samuelthomasdavies.com