The digital twin that deferred expansion and saved $1M

The challenge

The existing operation's capacity was hard to pin down. Rooms, incubators, people and production stages each competed for time in their own way, and material arrivals, batch variability and maintenance changed what the site could actually deliver.

Leadership needed to know whether the next increment of supply called for more building, a different way of running the existing site, or both. Answering that meant finding the constraints under realistic conditions and testing what each change would do to throughput, to queues and to the time needed to supply a patient.

Opyflow's work

The digital twin is a discrete-event simulation of the manufacturing process, which Opyflow built to show how batches move through the operation and how long they occupy each resource. Process specialists helped define the sequence, the resources each step requires, the variability and the operating rules.

The team tested shift patterns, incubator configurations, staffing levels and production-start rules. The model showed where an equipment bottleneck or a scheduling restriction held back output while other resources still had time available, where an extra unit would only move the bottleneck somewhere else, and what happened when the line accepted more work than it could absorb: growing queues and longer lead times.

Each scenario tied expected output to the resources needed to deliver it, in production support and logistics as well as in the manufacturing rooms. Opyflow wrote the findings up as operating recommendations and a full-capacity assessment, and set out the assumptions and mitigations clearly enough to stand up to regulatory review.

What changed

With practical capacity modelled at 30% above the previous estimate, and the number of equipment units set by scenario, the investment decision changed and the second construction phase was deferred. The 30% is modelled; the deferral and the $1M saving are realised. The model stayed with the client's team, and changes in staffing, scheduling or demand are tested in it before they reach the manufacturing floor.

What made the model useful to an investment decision?

Each capacity estimate came attached to an operating configuration, meaning the equipment, people, shifts and scheduling rules it would take. That let leadership weigh a further construction phase against particular changes inside the existing facility, and see what each would do to lead time.