Decision Intelligence for Manufacturing

From line reports to plant-floor foresight

A modern plant is instrumented to the millisecond, yet most of that data dies in historians and dashboards that describe what already happened. Downtime is explained after the line stops. Scrap is counted at final inspection. Capacity is planned in a spreadsheet that is stale by the time the shift starts. The signals that could have prevented the loss were all present — just never connected to a decision.

Decision intelligence closes that loop. We build forecasting and detection models on your MES, ERP, sensor, and supply-chain data that project demand into capacity needs, predict equipment failures before they halt production, and catch yield and quality drift while a process can still be corrected. Planners, maintenance, and quality engineers act on what is coming, not on what already cost them.

what_we_build

Three layers of manufacturing decision intelligence.

Plant reporting, predictive models, and live signal on one governed view of operational and supply data.

01 / reportingCORE

BI on manufacturing operational data

Live OEE, throughput, scrap, and inventory views — built on MES, ERP, and sensor streams instead of end-of-shift reports and manual rollups.

OEE and throughput by line
Scrap and rework tracking
Inventory and supply visibility

02 / predictionCORE

Predictive demand & maintenance models

Demand and capacity forecasting alongside predictive maintenance, so production plans and asset interventions are scheduled ahead of the constraint or the failure.

Demand and capacity forecasting
Equipment failure prediction
Maintenance interval optimization

03 / signalSECURE

Real-time yield & quality signal

Streaming detection on process parameters that flags yield and quality drift in real time and routes it to the engineer who can correct the process before scrap accrues.

Process-drift detection
Out-of-spec early warning
Engineer-in-the-loop review

Decisions worth instrumenting in Manufacturing

The models that pay off are the ones a planner, maintenance lead, or quality engineer can act on before the loss is booked:

Demand and capacity forecasting — turn order and pipeline signals into line-and-shift capacity plans so constraints and idle time are seen weeks ahead, not at the daily standup.
Predictive maintenance — score equipment condition from sensor and service history to schedule intervention before an unplanned stop takes the line down.
Yield and quality signal — detect process drift against parameters in real time so engineers correct it before it produces scrap rather than finding it at inspection.
Supply and inventory risk — flag emerging shortages and supplier slippage early enough to expedite, resequence, or re-source before the line is starved.

faq

Common questions.

How does decision intelligence forecast demand and plant capacity?

We forecast demand from order history, pipeline, and external signals and translate it into capacity requirements across lines and shifts, so planners can see where a constraint or an idle line is forming weeks ahead. Plans update as orders and production data flow in, keeping S&OP grounded in current reality rather than a stale spreadsheet.

Can predictive maintenance and yield models reduce unplanned downtime and scrap?

Yes. We score equipment condition from sensor and maintenance data to predict failures before they stop a line, and we model yield and quality drift against process parameters so engineers can correct a drifting process before it produces scrap rather than discovering defects at final inspection.

Act on the loss before it happens.

Thirty-minute briefing for operations, maintenance, and quality leadership. We map where predictive models change the call on the floor and leave you with a roadmap and ROI memo. Response inside 24 hours.

request_briefing → intelligence_overview

Decision intelligence for Manufacturing.