Saving a Pharmaceutical Giant: How We Redesigned Production to Handle 385% Capacity Overload

When a leading LATAM pharmaceutical manufacturer reached out, their projected demand growth was about to make their network physically impossible to operate: by 2032, granulation at their main site would hit 385% utilization and coating 352%, with 17+ resources above 100%. Their secondary plant had blister packaging at 293%. An expansion site sat nearly idle. Anything above 100% utilization is not inefficiency: it is demand that cannot be met, at any amount of overtime.

Why the obvious fix was impossible

The instinct was to modernize the main plant. The analysis showed why that could not work: a city-center site with no room to expand, infrastructure below modern GMP standards, and legacy equipment running at roughly half the productivity of current technology. Rebuilding in place would have cost as much as building new capacity elsewhere, with worse outcomes and years of operational disruption.

The finding: five dedicated routes instead of shared lines

We proposed consolidating all solid-dose production into the underutilized expansion site, redesigned around five dedicated production routes organized by batch size, replacing a pooled model where a single granulation train served everything from 50 kg to 650 kg batches, with changeovers eating the productive day. Five candidate architectures were stress-tested through Monte Carlo simulation before one met every criterion.

The impacts

• Peak utilization: 385% → 78%. Every route operates at 50–78% through 2032, with zero resources above 85%: healthy margins instead of physical impossibility.
• 67% fewer batches. Lot-size reformulation cut annual batches from 2,422 to 789 while holding inventory targets: fewer changeovers, lower quality risk, radically simpler scheduling.
• The secondary plant rescued without new investment. Transferring five high-volume product families brought its blister packaging from 293% to 72%.
• Financial viability, not just capacity. The phased investment plan was sized against simulated demand: capital deployed where the simulation showed it pays, not where intuition pointed.

The methodology

Monte Carlo simulation with 100 scenarios per configuration and ±30% demand variation around an already aggressive growth case. Every recommendation was presented as a full distribution, not an average, so the client could see that even the most pessimistic scenario remained operable.

Lessons for the industry

• Dedicated routes beat shared resources when the product mix is diverse: changeover cost outweighs utilization gains.
• Lot-size optimization is often a stronger lever than new equipment.
• Location constraints can make modernization impossible, not merely expensive.
• Network thinking is non-negotiable: solving one plant's crisis must not create another's.

Facing similar constraints? Contact us to discuss capacity and financial-feasibility modeling for your network.