Input Data Collection
Pulls live readings from the sources you already have: spectrometers, PLCs, DCS, lab systems, and material or cost databases. No rip-and-replace of existing instrumentation.

We build process optimization software for manufacturing plants that turns live process data — spectrometer readings, PLC tags, sensor feeds — into specific, real-time recommendations operators can act on.
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Most process industries have no shortage of data. Spectrometers, PLCs, DCS historians, and lab systems generate readings constantly. What's missing is the layer that turns that data into a decision at the moment it matters. Instead, plants fall back on:
Manual, experience-based decisions - an operator adds "a bit more" based on habit, not calculation
Reactive corrections after a batch is already off-spec, instead of guidance during the process itself
Inconsistent outcomes shift to shift and plant to plant, because the optimization logic lives in individual operators' heads instead of a system
Overuse of raw materials "just to be safe," quietly inflating cost across every production cycle
This isn't a data problem. It's a decision-support problem - and it's exactly what process optimization software is built to close.

Process optimization software continuously compares your live process data against target specifications and calculates the precise adjustment needed to hit that target at the lowest cost, instead of leaving that calculation to manual judgment.
It is not the same thing as SCADA. A SCADA or historian layer shows you what is happening on the plant floor. Process optimization software adds a calculation and recommendation layer on top of that data — the same category we call SCADAA (SCADA + Analytics) inside our EpsumThings platform: trend correlation, deviation detection, and context-aware diagnostics that explain why a parameter is off target, not just that it is.
We connect sensors and tracking systems across every stage of your production process through EpsumThings. Every furnace, every ladle, every crane, every casting sequence is visible in real time on a single platform.
Pulls live readings from the sources you already have: spectrometers, PLCs, DCS, lab systems, and material or cost databases. No rip-and-replace of existing instrumentation.
Runs your live inputs against defined process constraints — grade specifications, quality targets, safety limits, material costs — to calculate the lowest-cost path to the target outcome.
Surfaces the recommendation to the operator at the point of decision, with the option to accept it or log a reason for overriding it, so the system reflects real operational judgment, not just theory.
Feeds results back into your existing Level-2, historian, and reporting systems, and builds a record of every recommendation, override, and outcome for traceability and continuous tuning.
Every recommendation respects your actual process limits: minimum and maximum ranges, safety thresholds, and specification tolerances.
Every accepted or rejected recommendation is logged with a reason, so adoption and compliance are measurable, not assumed.
Connects to existing spectrometers, PLC/SCADA, and historian systems over standard industrial protocols, rather than requiring a new sensor layer.
Define and maintain your own process targets, grades, and specifications as your product mix changes, without waiting on a vendor.
Every optimization run, input, and outcome is stored and reportable, for operational tuning and compliance review alike.
Including a Ferro Alloy optimization solution for the LRF and grade-refining stage of steelmaking, built on the same optimization engine and extensible to other dosing and mix-optimization use cases.
On the steel melting shop floor of a leading integrated steel plant, ferro alloy additions during the LRF (grade refining) stage were being decided manually, based on spectrometer readings and operator experience, with no system calculating the precise, lowest-cost combination of alloys needed to hit the target grade chemistry. Overuse and underuse of alloys were both common, and both expensive.
We deployed a Ferro Alloy optimization solution that reads live spectrometer data and EAF alloy inputs, calculates the exact alloy additions needed to hit the target chemistry at the lowest material cost, and presents the recommendation to the operator at the point of the tapping decision. When an operator cannot follow the recommendation — for instance, a required alloy is not available on the floor — the reason is logged directly in the system, rather than the decision going unrecorded.
The result is a plant that can now see, heat by heat, whether its alloy additions are optimal, why they were not followed when they were not, and what that gap is costing — visibility that did not exist when the decision lived entirely in an operator's head.
precise, calculated additions replace "add a bit more to be safe," reducing material overuse across every production cycle
chemistry and process parameters land inside target range more reliably, cutting downstream rework and off-spec output
operators get a recommendation at the moment of the decision, not a report after the fact
every recommendation, override, and reason is logged, turning "we think process discipline improved" into something you can actually measure
utilization and override reports show exactly where the process is drifting from target, and why
Process optimization applies anywhere a manufacturing process has a measurable target and a real cost to missing it.
grade chemistry and alloy additions
fuel and reagent dosing, efficiency targets
mix and energy optimization
If your plant already runs on Level-2 automation, PLC/SCADA, or a process historian, our process optimization layer sits on top of what you have — it does not require starting over.
Process optimization software for manufacturing continuously compares live process data — from spectrometers, PLCs, sensors, or lab systems — against your target specifications, and calculates the precise adjustment needed to hit that target at the lowest cost, in real time, rather than relying on manual, experience-based decisions.
If material cost, quality consistency, or grade compliance is being decided by habit instead of calculation, we can show you what a live deployment looks like.