Workstation Productivity Monitoring on Your Existing CCTV

AI software watches your existing CCTV and detects whether each manual workstation is active (work happening) or idle, measuring active-vs-idle time and cycle patterns continuously across the line. Instead of an occasional stopwatch study, you get always-on, anonymized data per station, which reveals where the bottlenecks are and whether the line is balanced. It reads the process at the station, not the individual, and needs no wearables — the cameras you already have become the measurement.

Yes. With AI analytics, a camera pointed at a manual station can classify it as active or idle by recognizing work activity — motion, hand movement, part handling — in the station's zone, and timestamp how long each state lasts. This turns existing cameras into a continuous productivity data source without any device on the worker. Accuracy depends on camera view and how visually distinct the task is, confirmed during setup — some manual work is more visually ambiguous than others.

A manual time-and-motion study is a detailed but occasional snapshot done by an engineer with a stopwatch — accurate for a one-off, but not continuous. Wearables give continuous data but require a device on every worker, are costly, and are often intrusive or union-blocked. Camera-AI gives continuous, line-wide active/idle and cycle data on the cameras you already have, with no device on people and an anonymized, station-level view — ideal for finding bottlenecks and balancing the line day to day.

No — it is designed to watch the process, not the person. It measures the station's activity (active vs idle, cycle, throughput) and reports it anonymized at the station or line level, not as a named individual's productivity score. It does not use facial recognition, can be configured to store activity status rather than identifying video, and is meant for line-balancing and continuous improvement — not for ranking or policing workers. If you want per-individual oversight, that is a different product; this one measures the station.

By showing active-vs-idle time and cycle patterns for every station continuously, it makes line imbalance visible: you can see which station is the bottleneck the whole line waits on, and which stations sit idle because they are starved or over-resourced. That lets industrial engineers rebalance work content, move people, or redesign the station with data instead of guesswork. It is the always-on version of a line-balancing study, across every shift.

Yes — that is a core advantage. It uses your existing cameras and puts no device, tag, or wearable on any worker, which removes the cost, the maintenance, and the intrusiveness that block wearable rollouts (and often the union objection too). Each station just needs a camera with a clear view of the work area, confirmed by an assessment. Nothing is worn, clipped, or carried.

After setup for each station's task, active/idle detection is reliable enough to drive line-balancing and CI decisions, but the honest framing is that it is a station-activity estimate, not a certified time study. Accuracy depends on camera angle, lighting, and how visually distinct working looks for that task — some manual work, such as subtle fine-motor tasks, is harder to classify than others, which is confirmed on your own footage. It is built for continuous line-balancing insight, not per-unit certified metrology.

Machine monitoring measures a machine's running/stopped state. Workstation productivity monitoring measures activity at a manual station — an assembly bench, packing table, or inspection desk — where there is no machine to instrument and the human work itself is the thing to measure. Same cameras, different subject: one watches the machine, the other watches the station's activity. Many plants use both.

Employee monitoring is broad, individual-focused workforce oversight. Workstation productivity monitoring is station-focused and anonymized: the reporting unit is the station or line, the purpose is line-balancing and CI, and no individual is named or scored. The clearest test is the output — if it ranks a named person, that is employee monitoring; if it shows which station is the bottleneck, that is this. This page deliberately measures the station, not the individual.

It suits manual work areas whose activity is visually detectable — assembly benches, packing and kitting tables, inspection desks, sorting lines, and manual work cells — across electronics, automotive components, packaging, textiles, and general assembly. It is especially valuable for labour-intensive lines where wearables are impractical or union-blocked and time-studies are too occasional. A quick assessment confirms which stations are good camera candidates, since some tasks are more visually ambiguous than others.