Optimizing Line Setup to Reduce Interventions and Breakage

You cannot tell by looking. A vial can take pressure it should never have seen and reach the end of the line looking completely acceptable. Nothing chipped. Nothing cracked. Nothing to flag.

So, a clean run confirms one thing: it ran. It does not tell you whether your current setup is the best one available, whether anything changed since the last maintenance window, or where the pressure is coming from when a line does start losing containers.

Those are the questions these four studies were run to answer.

Most force evaluations get run after that day. By then you are reconstructing what happened from evidence that has already moved on. The teams that get ahead of this do the opposite: they capture what the line looks like while it is running well, then compare against that baseline after every changeover, every maintenance interval, every new format. Fewer interventions, faster setup, less time spent working out what changed.

That is the difference between assuming your line is performing and having the data to prove it.

Four use cases, all from real production studies.

Every example comes from a study run on a live line, with the actual data and reports shown on screen. No hypotheticals.

1. Investigation
When something has already gone wrong, force data changes what the investigation can do. Instead of reconstructing what happened from what is left behind, you measure it. Every example comes from a study run on a live line, with the actual data and reports on screen. Some started with a known problem. Some started with nothing more than a line worth checking.

2. Changeover and line setup
Change one parameter, measure how the pressure responds, and the data tells you which setting to keep. Do that once and changeover stops being a judgment call.

3. Blister and packaging operations
Filling gets most of the attention, but the forces do not stop there. The same studies run across primary packaging, secondary packaging, and blister lines, where handling is often rougher and the data is thinner.

4. FAT, SAT, and building a baseline
At FAT, agree on the acceptable force range with the vendor before you accept the equipment. At SAT, prove nothing shifted in transit. After maintenance, prove nothing drifted. Every clean run becomes the baseline for the next comparison.

Bring your own situation. Part of the session is open discussion, and sites apply this differently enough that the comparison is usually the most useful part.

Speaker

Charlotte Gillium, Manager, Operations Support, Manufacturing Intelligence and Technology Transformation, Pfizer

Charlotte has spent twelve years in pharmaceutical operations, starting in Montreal and now based in Ireland. For the past five years she has run force studies with SmartSkin technology across multiple drug product sites, supporting investigations, changeover optimization, maintenance verification and equipment acceptance. She captured every image and dataset in this session herself, on live lines.

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