Solutions · Discrete manufacturing
OEE that actually moves.
Discrete plants — automotive, aerospace, electronics, consumer goods — run between 55% and 75% OEE in 2026, against a Nakajima TPM world-class benchmark of 85%. Closing that gap is mostly a data problem at this point: the sensors are there, the controllers are there, the MES is there. Ipnops Industry is the intelligence layer that turns those signals into autonomous, auditable line operation.
world-class OEE · Nakajima TPM
85%
typical US plant OEE
55–60%
Cognex In-Sight 3800 inspection
1,200/min
documented defect-detection accuracy
99.2%
Use cases
What the platform actually does, here.
Line orchestration + OEE attribution
Per-station OEE with availability / performance / quality decomposed automatically. Bottleneck identification through ProcessSim discrete-event analysis. Operator-visible dashboards with the agent's recommendation already attached to each loss-bucket: this hour's performance loss came from station C-04 cycle drift; here is the fix.
Robotic assembly with cobots
ISO 10218 + ISO/TS 15066 collaborative cells, with speed-and-separation monitoring backed by OpenPose / HRNet pose estimation. OpenVLA or fine-tuned Octo policies translate task descriptions into manipulator trajectories. Cross-vendor (KUKA, ABB, FANUC, UR, Yaskawa) on a common task abstraction.
In-line vision QC
Cognex In-Sight 3800-class line cameras at 1,200+ parts per minute with documented 99.2%+ defect detection accuracy in 2026 industry data. Qwen3-VL or Pixtral runs the vision model — Pixtral on Hailo-10H, Qwen3-VL on Jetson Orin Nano Super or NVIDIA IGX Thor — so adapting to a new defect taxonomy is a prompt change, not a retraining cycle.
Predictive maintenance on rotating assets
Vibration RMS, kurtosis, crest-factor, envelope spectra at the cabinet edge. Acoustic-emission sensors detect bearing wear earlier than vibration alone. PANNs CNN14 + per-asset linear classifier fingerprinted at commissioning. Documented programs reclaim 30-60% of unplanned-downtime minutes by acting on triggers before failure.
Generative-AI-assisted PLC code
Schneider EcoStruxure and Siemens TIA Portal both ship Copilots in 2026 (Schneider native, Siemens with Microsoft). The platform brokers these — operators describe a behaviour change in plain language; the agent generates IEC 61131-3 ladder logic / structured text; ProcessSim validates before commissioning. The platform manages the change-control trail.
Aerospace + automotive specifics
Generative design for composite layup (GAN-optimised fibre architecture), AI-optimised AFP (automated fibre placement) path planning. AI-driven robotic drilling, painting, and assembly per Airbus-class production lines. Deep integration with PLM / CAD pipelines so the agent reasons about both as-designed and as-built parts.
How a deployment runs
From baseline to autonomous in two quarters.
- 01
Quarter 1, weeks 1-4: read-only ingestion of PLC + MES + vision + vibration + acoustic + cobot telemetry. Asset register imported from existing systems. Baselining + anomaly-detection in shadow mode.
- 02
Quarter 1, weeks 5-12: per-station OEE attribution goes live. Predictive-maintenance triggers begin auto-generating work orders for review. Operators approve before the platform acts.
- 03
Quarter 2: cobot dispatch with safety-envelope validation. RL controller trained for any in-scope process loops. ProcessSim digital-twin replay for change-control review.
- 04
Quarter 3+: full autonomous orchestration of the line under the customer's policy envelope. Per-shift OEE replay. Audit pack for ISO 9001 / IATF 16949 / AS9100 reviews.
Models active in this configuration
- TimesFM 2.5
- TFT
- Qwen3-VL
- Pixtral
- OpenVLA
- Octo
- PANNs CNN14
- ProcessSim
- Gemini 3.1 Flash Lite