Averages describe a plant nobody works in.
Plant-level averages smooth away exactly what matters: the one machine behaving unlike its twin, the shift that quietly runs better, the recipe that only misbehaves on one line.
MANUFACTURING ANALYTICS · PLANT MODEL
Manufacturing analytics that ends in an action, not a report
Analytics projects rarely fail on maths. They fail because nobody changed a decision because of them.
THE SHORT ANSWER
Manufacturing analytics turns production, quality and maintenance data into an understanding of how the plant actually behaves. The version that pays for itself does three things: it works on the data already recorded, it compares each machine with its own history rather than a plant average, and it ends with a proposed decision a human approves.
WHY MOST ANALYTICS STAYS DECORATIVE
The data project comes before the question.
Eighteen months of warehouse, lake and pipeline work, and the first answer produced is a chart the plant manager already suspected.
Insight without ownership dies in the meeting.
A finding that does not turn into a scheduled action, with a name and a date, is an observation. Plants are full of observations.
The market builds another reporting layer.
Atherya builds one model of your plant, and makes it answer questions.
WHAT ATHERYA DOES INSTEAD
One brain, not another dashboard.
Maintenance, scheduling and production intelligence run on the same model of the plant, so an insight on one side is already a constraint on the other.
Context per machine, not per plant.
Each machine gets its own learned normal, per recipe and per shift. That is what turns a statistical anomaly into a statement a maintenance lead recognises.
Evidence attached to every claim.
Which signal, which history, which comparable event. Analytics you can audit is analytics your team will act on.
The answer arrives as a proposal.
Atherya proposes the plan change — maintenance window, sequence, allocation — against the live production plan, and waits for your sign-off.
READ FROM ONE REAL PLANT — BEFORE INSTALL
6years of raw history6Mreadings · 42 machines · 5 families141machine × recipe signatures98 → 7open questions closed from data
Rubber moulding, northern Italy. Figures from a real onboarding, read from existing data before any installation. Anything beyond them is a scenario, and we declare it as a scenario.
WHAT THE FIRST READ PRODUCED
- Six years of raw history, roughly 6 million readings, 42 machines across 5 asset families, read before any installation.
- 141 machine × recipe signatures learned, and 98 open questions about the plant reduced to 7 that genuinely needed a person.
- 60.8% of the bottleneck concentrated on the presses; the plant's real average utilisation was 47%; the same recipe delivered 5.9% more output at night.
What makes Atherya different
Most predictive maintenance tools score signals. Atherya builds an operational memory of the plant first, then reasons on top of it — with the evidence in plain sight and the decision left to a person.
Operational memory
Every event, anomaly, intervention and shift becomes shared memory. What was a log yesterday is experience tomorrow, and the model reads new signals against it.
Déjà Vu: it has seen this before
Not just "anomaly detected", but when it already happened, how similar it was, how it evolved and which action worked. The senior maintainer's memory, available to everyone.
Context that kills false alarms
Machine defects, weak points, how the crew actually works and the environment around the line. A deviation that is normal for that machine, that shift or that season stays quiet.
Living FMEA
FMEA, manuals and procedures become an active part of the reasoning: causes, effects, sensors and suggested actions are connected, instead of sitting in a document nobody opens.
Explainable, not magic
Every alert arrives with the signals involved, the comparable history, the failure mode and a confidence level. Data, interpretation and decision stay separate and verifiable.
It acts, with your sign-off
Atherya does not stop at the warning: it proposes the maintenance window against real orders and shifts, and replans when something changes. Nothing is applied until a person approves it.
On the data you already have
It works on PLC, SCADA, MES, ERP, maintenance records and feedback as they are — fragmented and legacy included. Sensors are added only where no existing signal carries the degradation.
One brain, not a maintenance silo
Maintenance, production and planning read the same operational state, so a predicted failure immediately becomes a scheduling question instead of a separate dashboard.
QUESTIONS, ANSWERED
Straight answers.
What is manufacturing analytics?
The analysis of production, quality, maintenance and process data to understand and improve how a plant performs. It ranges from descriptive reporting to models that predict behaviour and recommend a decision.
Do I need a data lake first?
No. Most plants already hold enough in PLC/SCADA tags, a SQL historian, the MES, the ERP and the CMMS. Atherya reads those sources directly; a first read produces answers before any platform migration.
How is this different from BI dashboards?
BI reports what happened, on the dimensions someone chose in advance. A plant model learns each machine's normal behaviour and can say what changed, why it matters and what to schedule because of it.
Is the analysis explainable?
Yes — every finding carries its evidence: the signal that moved, the history it moved against, and comparable past events. Scenarios are always declared as scenarios, never as measured results.
What does an engagement start with?
An export of the data your machines already produce. We read it and return your plant's story: the real bottleneck, the hidden capacity, the machine with a quirk nobody catalogued.
YOUR PLANT · YOUR DATA · THE PROOF
Judge the analytics on your own export.
No installation, no migration — the first read runs on the data you already have.
Discover Atherya