Every deviation has a dollar cost
Your quality system tells you a batch deviated. It does not tell you what that deviation cost, and it does not tell you in time to save the batch. FyndEm Quality predicts deviations from the data your PI, PAS-X, LIMS and MES already collect — and prices every one against your own cost rates.
FyndEm Quality, deviation economics
Four questions your Director of Manufacturing is clamoring for answers to
His systems cannot answer them. Not because the data is missing — because it is read one parameter at a time, one batch at a time, and priced only after the quarter closes.
How would you know this batch is drifting, before release testing tells you?
SPC charts watch one parameter against one limit. Deviations rarely arrive that way. They emerge from interactions that single-parameter, single-batch monitoring is structurally blind to — and release testing is the first thing that isn’t.
What did last quarter’s deviations actually cost — driver by driver?
Yield shortfall, make-up batch materials, investigator hours — and more besides. Real costs, accruing in different cost centers, reconciled by finance at quarter-end as a single number nobody can take a decision against.
Which campaigns are trending toward your best batches, and which toward failure?
You know what a gold-standard batch looks like when it finishes. What you cannot see is which campaigns are converging on it and which are diverging — across bioreactors, downstream skids and shifts, where the pattern only exists when batches are compared to each other rather than to a spec.
How many six-figure deviations could have been prevented — across sites?
A major deviation consumes sixty-plus hours of drafting, SME review and regulatory documentation before a single corrective action is taken. The ones predicted early are never written at all. And across two or three sites the same preventable pattern usually repeats — which no single site’s quality review is positioned to see.
Four builds, one engine
The same predictive engine and the same cost model, calibrated to what a deviation actually costs in each process. All four are built.
Biologics
Built on SAP BTPCHO bioreactor drift, viable cell density and titer trajectory, glycosylation shifts — predicted hours or shifts before downstream release testing flags them. Monoclonal antibody, vaccine, and cell & gene therapy, where lot-of-one economics mean a single deviation carries the full batch cost.
White paper →Plasma Fractionation
Cohn-fraction yield, chromatography selectivity and viral inactivation deviations — surfaced across pooling, Fr. II+III and IVIG or albumin downstream processing, where a pool that fails carries every donation in it.
White paper →Solid Dose & API
Granulation, compression, coating and API step deviations, predicted against innovator-equivalent gold standards — for generics and biosimilars where comparability margins are tight and a failed campaign is a lost filing window.
White paper →Corrugated
Board and converting line deviations, with the cost model re-weighted to what matters on a converting line — substrate waste, downtime and re-run rather than batch yield. The same prediction, priced in the units the plant already reports.
White paper →Value from the data your plant already generates
- Protected yield, from predicting process drift hours or shifts before release testing confirms it
- Avoided make-up batches, by surfacing the deviation while the batch can still be saved rather than repeated
- Lower investigation and CAPA cost, through audit-defensible narratives drafted from deviation context for your existing eQMS
- Defensible plant economics, with every deviation priced across its real cost drivers — per deviation, per batch, per plant — at your own cost rates
Every prediction is actionable
Every FyndEm Quality prediction carries specific actions, a lead time, a confidence level and a dollar impact. The dollar impact is what lets your team prioritize — and it comes from your own facility’s cost configuration, not from industry estimates.