The series explains the thinking. This page answers the questions plant managers, production directors, operators and maintenance managers actually ask before saying yes.
Key takeaways
The first line is typically live on data collection within weeks, and the first prioritized action queue follows as soon as a baseline exists, usually within the first one to two months on that line.
Rollouts proceed line by line, so value starts on line one while line five is still being connected. You do not wait for a big bang.
Less than the paper and spreadsheets it replaces. Machines, sensors and cameras carry most of the data load automatically.
Operators classify stops with a few taps at the machine, in their own language. Technicians receive and close work orders on mobile, scanning the asset QR code. The plant manager's part is a daily glance at a ranked queue and a decision on each item.
No. The ERP stays exactly where it is; Fabrico draws order, BOM and material context from it through integration rather than competing with it. Existing point tools can run in parallel during transition.
What we do replace, deliberately, is the gap between systems: the manual carrying of insight from one tool into action in another. That gap is the whole subject of the decision layer.
The customer owns its data, full stop, with export available at any time.
Data is segregated per customer, and Fabrico operates certified information security and quality management systems (ISO 27001 and ISO 9001). Enterprise deployment options and data residency are agreed per contract.
No. Cameras point at product flow and mechanisms, and their job is to explain stops, not to score individuals.
Shift and crew level patterns are used to improve procedures and training, never for individual performance measurement. We put this in writing, because a system operators experience as surveillance gets fed garbage data, and adoption dies with it.
Every recommendation passes a human approval gate: the accountable person accepts, edits or rejects it, with the reason logged. Nothing executes itself.
Wrong recommendations are rejected in seconds, and the rejection itself teaches the system a constraint it did not know. Predictions are also publicly accountable: expected impact is recorded up front and compared with the measured result, which is the discipline context is built to support.
No. That is exactly why there are three capture paths: direct PLC connection where it exists, retrofit IoT and optical sensors where it does not, and computer vision over the line for what sensors miss, including manual stations.
Legacy equipment is usually where the largest hidden losses live, so covering it is the point, not an afterthought.
No. The evidence base is built from clean, structured data going forward, starting on day one. Whatever usable structure exists in your history is imported, but nothing waits on an archaeology project.
A vendor grading its own homework is worthless, so the measurement is designed to be auditable. Expected impact is logged before the action; realized impact is measured by the machines afterwards.
Line by line rollout means not yet covered lines act as a control group, separating the platform's effect from changes in volume and mix. Everything is normalized per unit produced and expressed in euros, and the ledger is yours to inspect. This is the same logic behind OEE, applied to the value of the software itself.
Subscription, scaled to sites and modules, so it is operating expense rather than a capital project.
Because the outcome ledger makes delivered value visible in euros, the pricing conversation can be grounded in verified results rather than promises. Specifics are agreed per deployment.
An onboarding team maps your asset hierarchy with your people, agrees the downtime reason codes and the per product standards for the first line, and connects the first data sources. Operators get a short hands on introduction at the machine, not a classroom day.
By the end of the week the first line is producing validated data, and the baseline that everything else builds on has started forming.
Want to see the decision layer on your own line? Book a demo and we will walk your team through what it would surface on your floor.
Fabrico is a manufacturing operations platform combining OEE monitoring with computer vision, a full CMMS, MES capabilities and production planning in one data model.
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