Transform shop-floor telemetry into autonomous predictive action. We design and deploy production-grade machine learning, deep reinforcement learning, and computer vision systems to optimize line capacity, eliminate unplanned downtime, and stabilize high-mix operations.
AI agents require environments to learn and validate without disrupting live operations. We use our AnyLogic and Simio digital twins as training testbeds for reinforcement learning policies and rapid neural surrogate evaluations.
Estimate Remaining Useful Life (RUL) and identify vibration or thermal anomalies before failure stops the line. We shift operations from calendar PMs to condition-based servicing.
Replace rigid dispatch heuristics with Deep Reinforcement Learning (RL) agents that balance high-mix workstation queues and minimize setup matrix penalties in real time.
Deploy lightweight Convolutional Neural Networks (CNNs/YOLO) at edge workstations to classify surface flaws, dimensional variances, and assembly completeness at takt speed.
Recurrent time-series models optimize multi-echelon safety stocks and anticipate supplier lead-time shocks across complex bills of materials (BOM).
Computer vision 3D pose estimation automates traditional REBA/RULA postural scoring and Methods-Time Measurement (MTM) without disruptive physical stopwatch audits.
Train physics-informed neural surrogates on thousands of stochastic simulation runs to deliver sub-second scenario answers for floor supervisors during active shifts.