Manufacturing
The gap between your target OEE and your actual OEE is where your margin disappears.
Most manufacturers know their OEE target. Very few have a real-time view of their actual OEE. The difference between those two numbers, measured at machine level and shift level, is where production capacity is being lost. We build the systems that make your shop floor visible to the people managing it.
The problem
Where it breaks down.
OEE is a target on paper, not a number in reality
Shift supervisors fill in a paper form at the end of each shift. Someone enters the numbers into a spreadsheet. By Monday morning, the production data is five days old and already an average of averages. Nobody on the plant floor has a real-time view of what is actually happening on any given machine at any given moment.
Quality rejections repeat because the root cause is never isolated
You know your rejection rate. You do not know which combination of machine, shift, operator, batch, and raw material is generating those rejections. Without that specificity, the same pattern repeats next month and the month after.
Your demand plan, production plan, and procurement plan are three separate conversations
Sales gives a forecast. Procurement places orders based on that forecast. Production runs a schedule based on available capacity. None of these connect in real time. The result is finished goods stockouts of high-demand SKUs running alongside overproduction of low-demand ones.
What we build
How we fix it.
OEE and production intelligence
Real-time machine-level and line-level OEE tracking with breakdown by shift, SKU, and production line. Automated downtime capture with reason codes recorded at the source rather than remembered at the end of the shift. The data your maintenance team needs to move from reacting to failures to preventing them.
Outcome
Manufacturers implementing real-time OEE tracking recover production capacity that was being lost to unrecorded downtime and changeover inefficiency. The capacity existed. It just was not measured.
Quality and defect analytics
End-to-end defect tracking from raw material intake through finished goods, with statistical pattern detection that isolates which combination of inputs is generating rejections. Find the root cause of a quality issue in hours rather than weeks. Act before the pattern becomes a customer escalation.
Outcome
Manufacturers implementing quality analytics reduce rework costs. The same rejection pattern stops repeating once the actual cause is visible rather than assumed.
Production planning intelligence
A unified view connecting sales forecasts, current inventory levels, production capacity, and material availability, updated in real time. Your production planner sees the conflicts before they happen. The decision to adjust the schedule is made with complete information rather than the best available guess.
Outcome
Manufacturers reduce overproduction of low-demand SKUs and the stockouts of high-demand ones that result from planning in isolation.
Procurement and supply chain analytics
Supplier performance scorecards, lead time tracking, price variance analysis, and material consumption connected to your actual production schedule. Procurement decisions informed by real demand rather than safety stock assumptions and supplier relationship history.
Outcome
Procurement cycle time drops. On-time material availability improves. The buffer stock carried to compensate for supply chain uncertainty decreases because the uncertainty itself decreases.
Maintenance and operations knowledge assistant
A generative AI system trained on your maintenance manuals, equipment SOPs, quality standards, and historical work orders. A technician on the floor asks what the error code on Machine 4 means and what the recommended corrective action is. The answer appears in seconds from your own documentation, without calling the maintenance manager or waiting for a senior technician to be free.
Outcome
Mean time to resolution on equipment issues drops. Escalations to senior maintenance staff decrease. Shift continuity improves because the knowledge your best technicians carry in their heads becomes accessible to everyone.
Most data engagements start with a conversation about a specific problem.
Tell us what yours is. We will tell you honestly whether we are the right team to solve it.
We respond within 24 hours.