Manufacturing

Why FRIZB AI Supervisor

Unlock Hidden Intelligence from the Infrastructure You Already Have

Hidden Risk Detection

Frizb continuously observes people, equipment, and materials to reveal unsafe behavior, abnormal machine states, and unnoticed process hazards long before they escalate into injury, downtime, or production loss.

Unsafe Interaction Mapping

Frizb tracks human and machine movement around lines and cells to detect dangerous proximity patterns, revealing high risk moments that normally go unseen and helping supervisors act before an incident unfolds.

Improper Handling Detection

Frizb identifies incorrect lifting, tool misuse, and unsafe hand placement near machines, enabling early correction and reducing the likelihood of strain injuries, equipment damage, and hazardous contact situations that disrupt production.

PPE Compliance in Context

Frizb recognizes missing or incorrect PPE only when specific risks are present, such as detecting shield absence during active welding, eliminating false alerts and ensuring safety enforcement is precise and trusted by workers.

Unsafe Motion Awareness

Frizb identifies erratic forklift turns, uncontrolled pallet movement, and unexpected mobile equipment acceleration that create hidden hazards, giving teams the visibility needed to intervene early and prevent harm.

Safety Incident Prevention and Standards Alignment

Frizb supports OSHA, NFPA 70E, and functional safety expectations by detecting unsafe conditions, preventing hazardous states, and guiding operators to maintain safe operation long before any violation or incident occurs.

Arc Hazard Pre Indicators

Frizb identifies visual signs of electrical stress near cabinets or panels by detecting abnormal glow, smoke traces, or unusual technician behavior, helping prevent escalation and supporting safe electrical maintenance procedures.

Lockout Verification View

Frizb confirms the presence or absence of visible lockout materials during servicing activities to reinforce proper lockout practice, reducing the chance of accidental energizing and meeting procedural expectations.

Hot Work Oversight

Frizb observes flame, spark, and tool motion to verify proper hot work practices and detect unsafe preparation lapses, providing early warnings aligned with industrial fire safety expectations and reducing exposure to error.

Safe Path Monitoring

Frizb checks that technicians and operators maintain required distance zones during machine operation, helping reinforce safe pathways and preventing inadvertent entry into hazardous regions around industrial equipment.

Forecasting Machine and Process Failures

Frizb identifies visual precursors to machine drift, mechanical stress, and abnormal workflow behavior, enabling teams to intervene before failure affects throughput, quality, or equipment integrity.

Mechanical Stress Indicators

Frizb detects unusual vibration, wobble, or misalignment in rotating equipment and moving parts, identifying early signs of mechanical degradation that often precede unplanned stoppages and expensive repairs.

Cycle Deviation Alerts

Frizb monitors cycle consistency across repetitive tasks and detects small timing drifts that signal performance decline, allowing supervisors to adjust calibration or maintenance schedules before output is compromised.

Material Jam Early Signals

Frizb identifies slow buildup, irregular accumulation, and minor hesitation in conveyors or chutes, signaling early stage jams and helping teams prevent costly blockages across manufacturing lines.

Thermal Behavior Changes

Frizb identifies unusual heat signatures or localized discoloration on surfaces, machines, or product flow, revealing stress points and enabling predictive maintenance before excessive heat causes failure.

Never Before Visibility Into Plant Operations

Frizb converts cameras into a live operational blueprint, revealing how people, machines, materials, and product flow interact across lines, cells, stations, aisles, and staging areas in real time.

Workforce Movement Blueprint

Frizb visualizes technician distribution and movement patterns across shifts, capturing where time is spent, where congestion forms, and where gaps reduce throughput or cause hidden delays.

Material Flow Anomaly Detection

Frizb identifies irregular pallet routing, stalled carts, or unusual material accumulation, helping teams uncover silent inefficiencies that slow production and create downstream variability.

Station Utilization Tracking

Frizb observes usage duration, idle periods, and transition delays at each station, revealing how real workflow differs from the documented plan and enabling adjustments that improve productivity.

Cross Area Coordination View

Frizb shows how activities in one area affect others, revealing cascading impacts of slowdowns, miscoordination, or resource imbalance across lines and shared equipment.

Unified IT and OT Intelligence Through Vision AI

Frizb fuses visual data with OT signals, IT systems, and machine logic to create a single contextual intelligence layer that understands what is happening and why it is happening across the plant.

Work Order to Activity Sync

Frizb compares actual executed steps with digital work orders, highlighting deviations, skipped tasks, or missing materials that impact quality or create rework, improving precision in manufacturing execution.

Vision Triggered Machine Logic

Frizb converts visual events into machine control signals through PLC integration, enabling slow downs, interlocks, and protective actions driven by real world conditions rather than static rules.

Contextual Quality Checks

Frizb connects visual cues with product specifications to verify tool usage, material correctness, and sequence validity, reducing quality escapes and making inspection more reliable.

Automated OT Documentation

Frizb captures visual evidence during machine events, alarms, or shifts, linking them to OT logs to create a complete, contextual incident trail that improves troubleshooting and clarity.

Evidence Driven Continuous Improvement

Frizb captures real activity across shifts and days, comparing differences in performance, dwell time, takt time, resource use, and workflow drift to uncover root causes and guide informed improvement.

Dwell Time Variance Tracking

Frizb compares dwell patterns across batches and shifts, revealing delays caused by resource shortages, waiting steps, or inconsistent execution, enabling targeted fixes that directly improve throughput.

Takt Time Drift Identification

Frizb identifies subtle takt time deviations that normally go unnoticed, highlighting operator, equipment, or material factors that influence cycle performance and affect production stability.

Cross Shift Performance Gaps

Frizb highlights differences in behavior between shifts, teams, or supervisors, showing when inconsistencies impact throughput or quality and giving leaders measurable evidence for corrective coaching.

Root Cause Evidence Trails

Frizb connects bottlenecks to video backed moments, allowing teams to see exactly why a slowdown happened and implement improvements based on indisputable operational facts.

Evidence-Driven Strategy

Compare operational activity across days to uncover variations that affect flow, productivity, and resource use. Link these differences to root causes such as inventory bottlenecks and machine downtime to guide smarter decisions.