A Clearer Picture of Production Performance
We built an AI-powered production monitoring system that helps manufacturers understand worker activity, measure production efficiency, and identify potential bottlenecks across the factory floor.
You can't improve what you can't measure.
In large manufacturing facilities, production efficiency depends on more than the number of workers or the speed of an assembly line.
Understanding how work progresses requires visibility into worker activity, movement patterns, and the time spent on different production processes.
But manually observing these activities across multiple production lines is difficult. Supervisors cannot continuously monitor every worker, every movement, and every process transition.
As a result, production bottlenecks and variations in work patterns can remain difficult to identify.
Supervisors could not continuously monitor every worker or movement across multiple production lines, leaving efficiency drops unmeasured.
We turned factory-floor footage into production intelligence.
We developed a custom AI solution that analyzes CCTV footage to understand worker activity and movement across production lines. The system uses computer vision to detect and track workers, analyze their movements, and identify activity patterns throughout production processes, before translating those observations into structured efficiency metrics and production-cycle estimates. The goal was to help manufacturing teams move beyond simply watching the factory floor and toward understanding how work actually happens.
"We designed the solution around one goal: make production activity measurable, so teams can make more informed operational decisions.."
Everything needed to understand activity across the production floor.
AI-powered worker tracking
We developed AI models to detect, count, and track workers across production-line camera feeds.
Worker activity analysis
The system analyzes worker movements and behavioral patterns to provide a clearer picture of activity throughout production processes.
Micromotion analysis
We built fine-grained movement analysis to examine individual work patterns and understand how workers interact with different production activities.
Worker efficiency calculation
Observed activities and movement patterns are converted into measurable productivity and efficiency indicators.
Production cycle-time estimation
The system estimates the time associated with different production activities and transitions, helping identify potential delays and bottlenecks.
Productive and non-productive activity analysis
Continuous video analysis helps distinguish productive activities from periods of non-productive activity.
Multi-camera monitoring
The solution supports analysis across multiple CCTV cameras, providing broader visibility into factory-floor operations.
Industrial productivity dashboard
We developed an analytics experience for monitoring worker activity, efficiency indicators, movement patterns, and production-cycle estimates.
Together, these capabilities connect visual monitoring with structured production analytics in one system.
Four problems, four fixes.
Supervisors had no scalable way to track worker movement across massive facilities.
Deployed AI models across 100+ CCTV cameras to automatically track workers and movement patterns.
Fine-grained work processes and micromotions were difficult to audit.
Built fine-grained movement analysis to examine individual work activity and process transitions.
Productive versus non-productive time was estimated manually and inaccurately.
Continuous video analysis automatically distinguishes productive intervals from delays.
Data from camera feeds was isolated and not translated into actionable metrics.
Created an industrial productivity dashboard converting visual feeds into structured efficiency indicators.
Complex factory data, made easier to understand.
We designed the monitoring experience to make production activity easier to interpret.
The dashboard brings together camera feeds, worker movement patterns, and production analytics to provide a clearer picture of factory-floor operations.
Instead of relying on isolated observations, teams can examine activity across monitored areas and use structured visualizations to understand how work progresses.
The experience focuses on turning complex AI outputs into information that is easier to explore, compare, and interpret.
View activity across monitored production areas.
Explore worker movement patterns across the factory floor.
Review production activity and worker efficiency indicators.
Examine estimated production-cycle durations and activity transitions.
From passive monitoring to measurable production activity.
The project demonstrates how AI-powered computer vision can support production analysis at industrial scale.
The documented deployment environment includes more than 100 CCTV cameras and approximately 500 workers, with an AI processing capability of approximately 15–20 million inference events per day.
What the project has achieved:
- Large-scale monitoring: AI-powered analysis across a documented environment with 100+ CCTV cameras and approximately 500 workers.
- High-volume processing: Capability to process approximately 15–20 million AI inference events per day.
- Measurable worker activity: Conversion of observed movement and activity patterns into structured efficiency indicators.
- Production-cycle analysis: Estimation of activity durations and process transitions to support bottleneck investigation.
- Centralized production intelligence: A unified dashboard for examining worker activity and production performance.
Why this matters to the business
Production efficiency is influenced by countless small activities and process transitions throughout the working day. By making these activities measurable, the system provides manufacturing teams with a more structured way to examine production workflows and investigate potential inefficiencies. The project establishes a foundation for data-informed operational analysis, helping teams understand where closer investigation may be valuable. The documented figures describe system scale and processing capability. We have not assigned a percentage improvement to productivity, labor efficiency, or production output because verified before-and-after measurements were not provided.
What changed
for Square, Bangladesh.
CCTV cameras actively monitored across the facility
Daily AI inference events processed seamlessly
Workers tracked through automated computer vision pipelines
Raw footage converted into actionable efficiency indicators
“The system provides manufacturing teams with a more structured way to examine production workflows and investigate potential inefficiencies.”
Technology that makesa real difference.
We developed an AI-powered monitoring solution that transforms factory-floor video into structured insights on worker activity, movement patterns, and production-cycle behavior.
The system demonstrates how computer vision can extend beyond conventional video monitoring to support industrial productivity analysis.
For manufacturing teams, the value lies in having a clearer, more measurable picture of how work progresses across the production floor.
The outcome is a foundation for more informed operational decisions, built around the activities that drive production performance.
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