Running—or waiting?
Explore equipment running and idle histories across your production floor. Review whether gaps relate to loading, handling, or other work rather than treating all idle time as waste.
Manufacturing
Understand where production time goes.
A machine stops. A coil changes. The next part waits. Connect camera observations and available machine data to understand the gaps—without another manual time study.
Discuss your shop-floor workflow
Fabrication & prefabrication
You can see the machine. Can you explain the lost time?
Manual logs miss changeovers. Footage takes time to review. A cycle counter tells only part of the story. Give your team a way to connect production events with the evidence needed to investigate them.
What changed during changeover?
Scope capture of coil loading or die and shoe replacement, with agreed start and end points. Compare similar changeovers and return to the footage when one takes longer.
What happens between parts?
Look at part counts, machine cycle durations, and the gaps between successive parts separately. A shorter machine cycle does not, by itself, establish better production flow.
These applications require site-specific setup and validation. Cameras must be able to see the relevant actions; machine signals can add context where accessible.
Production review
Less footage to search. More context for a decision.
The useful output is not another screen to watch. It is a review of what happened, which patterns deserve attention, and where to look next.
Start with a question such as: “Which coil changes took longer than comparable changes, and what does the footage show?”
Patterns help your team investigate. They do not establish the cause of a delay or automatically require an alert or task.
BizStack
Bring machine activity into context.
BizStack brings connected data together around your equipment and operation. Scope camera observations alongside available machine signals and production context, so an event has more meaning than a timestamp alone.
Explore the BizStack platform →Work with the sources you can access.
- Cameras: visible actions and supporting footage, subject to coverage and recording availability.
- Machine signals: operating events where an accessible interface exists.
- Production context: part types, work periods, and changeover definitions needed for fair comparisons.
Connections and event capture depend on available interfaces, permissions, and deployment requirements. This is an analysis workflow, not autonomous machine control.
At your shop
Start with one machine. Define a useful measurement.
A focused evaluation begins with the question your team needs answered—not a promise to monitor every process on the floor.
Agree on the event
Define cycle boundaries, expected handling time, and each changeover type. Choose the work periods and comparable parts before interpreting differences.
Check what is visible
Review camera angles, lighting, occlusions, and recording access. Confirm which machine signals exist and whether they can be connected.
Validate against a baseline
Compare detected events with reviewed observations. Agree on detection reliability and what would make the resulting history and report useful to your team.
If crew counts or process comparisons are in scope, prefer job-, station-, or crew-level measures. Confirm permissions, privacy, and applicable labor requirements before collecting people-related data. Counts alone do not measure individual productivity.
MODE demonstration · Parking occupancy
See activity history become a review.
Our parking demonstration shows occupancy history and an AI-generated report reviewing a heatmap image. See the source material, findings, and checks needed before acting.
This demonstrates the observation-and-review approach—not factory cycle-time measurement or changeover detection. Integrated recurring BizStack Agent reporting remains planned until demonstrated.
Explore the MODE parking demonstration →
Which production gap do you want to understand?
Tell us which machine or changeover matters, how you measure it today, and what a useful review would show. We’ll discuss the data and a practical first evaluation.