Decision intelligence for Manufacturing.
Forecast demand, capacity, and equipment failure and catch yield drift as it happens — so planning, maintenance, and quality run on live plant and supply-chain data instead of last week's MES export.
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.
Three layers of manufacturing decision intelligence.
Plant reporting, predictive models, and live signal on one governed view of operational and supply data.
- OEE and throughput by line
- Scrap and rework tracking
- Inventory and supply visibility
- Demand and capacity forecasting
- Equipment failure prediction
- Maintenance interval optimization
- 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.
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.
Explore related capabilities.
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.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: