Business & Commerce
Manufacturing
Producing more does not depend on more shifts. It depends on better decisions.
An average plant operates at around 60% of its effective capacity. The world-class standard is 85%. That gap cannot be closed with more hours.
The industry challenge
Four out of ten unplanned stops come from equipment failure. Almost all are detected after the line has already stopped.
Production is planned with models that assume stable demand, and quality is sampled after defective batches leave the plant. AI-powered maintenance and inspection reduce downtime by 30% to 50%, along with scrap, overtime and rework.
The technology to close that gap exists. What is missing are teams that know how to apply it to the plant, not the lab.
At JoyIT, AI-Powered engineering teams integrate Artificial Intelligence into maintenance, quality, planning and IT/OT integration to make plant decisions faster, more visible and more profitable.
Four fronts where AI changes plant outcomes
01.Industrial predictive maintenance
Anticipate failures before a shutdown. Models use telemetry, vibration, power consumption and work orders to turn unplanned stops into scheduled maintenance, without taking equipment off the line for inspection.
02.Computer-vision quality control
Detect defects on the line, not through customer complaints. Computer vision inspects 100% of parts at production speed, instead of sampling that only confirms a problem after dispatch.
03.Intelligent production planning
Plan production around real demand. Forecasting and sequencing size batches, shifts and materials for what the market will need, rather than what it ordered last quarter.
04.Full plant integration (IT/OT)
Connect the plant to the business. SCADA, MES and ERP integration brings shop-floor data into commercial decisions on the same day, with end-to-end batch traceability.
The size of the opportunity, with the source in view
- Stops caused by equipment failure
- Less downtime
- Improvement in defect detection through inspection
Your process recipes are worth more than your machinery.
Parameters, formulas and yields are sensitive data. Training models or sharing production creates risks. We define roles, anonymization and security before intervening.
Our process, with timelines in view
Manufix
A metalworking manufacturing plant with robotic cells operating 24/7.
- UX/UI design for a robotic-cell monitoring platform
- Predictive maintenance using robotic-arm telemetry
- Computer-vision inspection on the assembly line
- Production planning based on real demand
- Integration of robotic cells with SCADA, MES and ERP
- End-to-end batch and cycle traceability
Manufix operated 24 robotic cells without centralized visibility. Torque failures and vibration spikes were detected only after cycles stopped. Defects were confirmed during final quality control, and production relied on quarterly history instead of actual demand. Shop-floor data was isolated from SCADA, MES and ERP, leaving commercial decisions disconnected from real-time plant conditions.
Live robotic-cell monitoring and plant traceability Problem → Solution → Technologies → Result
Do you work with our current MES, or must we migrate?
We work with what you already have, connecting plant data and integrating solutions with your existing MES.
How long does it take to see a result?
We prioritize a measurable pilot on one line or cell and scale progressively after validation.
Our machinery is old and disconnected. Can this still work?
Yes. We assess available data and instrumentation needs, starting where the opportunity is greatest.
Must production stop during implementation?
We deploy in stages and coordinate interventions with plant operating windows.
Do we own the code and models?
Yes. We agree on intellectual property and knowledge transfer from the start.
Do we hire a project or a team?
Both are possible: a defined scope or a specialized team embedded alongside yours.