Factory Flow Intelligence uses computer vision and existing factory cameras to continuously analyze how production moves across lines, cells, equipment, material areas, and plant pathways. It identifies conditions such as bottlenecks, blocked or starved stations, idle time, micro-stoppages, work-in-process buildup, congestion, material shortages, and unnecessary movement. The objective is not to score individual workers. It is to understand the production system and reveal the conditions reducing productivity, throughput, and capacity.
Use existing security cameras or production cameras to observe manufacturing operations without disrupting production.
Physical AI continuously analyzes movement throughout the factory to identify bottlenecks, waiting, idle time, congestion, material movement, queues, downtime, and production interruptions.
Factory Flow Intelligence works alongside MES, SCADA, Andon, maintenance, and scheduling systems to combine production events with visual operational context.
Operations and continuous improvement teams receive objective visual evidence showing where productivity is being lost and where process improvements can have the greatest impact.
Production reports may show that output was missed, but they rarely reveal where queues formed or which station created the constraint.
How DeepHow helps: Continuously identify where production slows and determine whether the bottleneck shifts by product, shift, or operating condition.
Machine systems may record a stop without explaining whether the cause was equipment, materials, congestion, or delayed restart.
How DeepHow helps: Provides visual context before, during, and after downtime and micro-stoppage events.
Queues, waiting, and station imbalances can reduce capacity and extend manufacturing lead time.
How DeepHow helps: Compares activity, utilization, blocked conditions, and waiting across connected stations.
Late replenishment, unnecessary handling, poor routes, and forklift congestion can quietly interrupt production.
How DeepHow helps: Analyzes how materials, carts, containers, forklifts, and work-in-process move through the facility.
Manual observation cannot consistently cover every line, shift, product run, or plant.
How DeepHow helps: Automates observation and evidence collection across longer production periods and more operating areas.
Identify recurring production constraints and increase throughput without immediately adding capacity.
Reveal the conditions surrounding production interruptions, reduce flow-related downtime, and accelerate recovery.
See where stations are overutilized, underutilized, blocked, or starved so teams can rebalance work more effectively.
Identify excessive buffers and inventory accumulation before they increase lead time or consume floor space.
Detect delayed replenishment, unnecessary travel, repeated handling, and inefficient material routes.
Measure shutdown, cleaning, tooling, setup, validation, waiting, and restart conditions.
Establish a baseline and verify whether process, layout, or material-handling changes reduced waiting, congestion, downtime, or travel.
Determine whether lost output comes from true equipment capacity constraints or correctable flow problems before investing in additional equipment.
Factory Flow Intelligence helps manufacturers optimize production by continuously monitoring how work, materials, and equipment move throughout the factory. It identifies recurring bottlenecks, blocked and starved stations, downtime, and micro-stoppages that reduce throughput while tracking work-in-process (WIP) accumulation, material replenishment delays, and internal logistics congestion. Operations teams can analyze changeovers, compare production flow across shifts, lines, products, and facilities, and uncover the conditions limiting capacity, productivity, and operational performance. By providing continuous visual insight into factory flow, DeepHow enables faster problem identification, more effective continuous improvement initiatives, and data-driven decisions that improve throughput without adding unnecessary capacity.
Use AI to identify bottlenecks, optimize material flow, reduce downtime, and continuously improve factory performance.
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