NAGARE feeds a manufacturing productivity dashboard with data from the manual stations that machines cannot report on. Output, cycle times, stops and process adherence update live from video, so supervisors act during the shift and managers see the same numbers the floor sees.
Machine data flows into reports automatically. Manual stations rely on handwritten counts and end-of-shift entries, so the screen is accurate where it matters least and late where it matters most.
Our AI process monitoring software, NAGARE™, can populate a manufacturing productivity dashboard with configured observations of output, cycle times, stops and process adherence. Agree which measures and clip links are available, how they are defined and which additional station or machine inputs they require. The productivity impact shown is a NAGARE product figure. A dashboard for a new line needs its own baseline and outcome review.
A manufacturing productivity dashboard is only as good as its inputs. NAGARE measures manual work directly from station cameras and combines it with machine data where you have it.
Cameras record output, cycle times, stops and adherence for each manual station, without manual entry.
Where suitable machine interfaces and comparable event definitions are agreed, assess how those records can be combined with manual-station observations.
Supervisors see the current shift, engineers see trends and causes, and managers see the line against plan.
Send a list of the figures you report today and a video of one station. We will show what a live view would look like.
Get In TouchEach option shows numbers. They differ in how current those numbers are for manual work and whether you can see the reason behind them.
At APA Engineering, NAGARE dashboards visualise process adherence and violations in real time, with productivity tracked across kitting stations and video traceability for every kit.
APA Engineering's published results cover its full kitting deployment, including the real-time dashboards.
Start with a small set that people will act on: output versus target, cycle time against standard, stops by cause and process adherence. An OEE trend is useful where machines dominate, but manual stations need their own measures. A focused screen gets read, while a crowded one gets ignored, so most plants begin with four or five figures and add more only when they are used. Each figure should have an owner who acts on it during the shift.
Most production screens are fed by machines, MES postings or manual entry. That works for automated equipment but leaves manual stations estimated or late. Here the manual work is measured directly from station cameras, so assembly, kitting and packing stations appear with the same detail and freshness as the machines beside them, and the line can finally be seen as one system.
Many plants show a station-level view on a wallboard or screen so the team can see output and stops through the shift. Individual figures are handled with care: an operator productivity dashboard works best for coaching and for learning from differences in method, not for ranking people. The rules for who sees what are agreed with the team before go-live, and they can be reviewed as trust in the data grows.
Agree the available drill-down views during the station demonstration. A useful review path moves from a line-level result to the relevant station, period and cycle or stop, with a clip where it has been retained and linked. That is a workflow requirement to confirm, not a promise of identical menus in every deployment. Check identifiers, timestamps, access and retention so the reviewer can investigate the figure using evidence that actually exists.
The production plan or takt sets the target for each hour and shift, and measured output is shown against it as the shift runs. When a gap opens, the stops and long cycles behind it are listed beside the figure. Supervisors can then decide whether to rebalance, call support or accept the loss while there is still time in the shift to recover.
Where definitions matter across sites, ISO 22400 provides a reference for key performance indicators in manufacturing operations management. Using standard definitions keeps figures comparable between lines and plants. Manual measures such as step time and adherence are defined with your team, so they mean the same thing on every screen and in every report.
Station capture can reduce the delay of later manual entry, but freshness still depends on camera availability, processing, connections and the configured refresh interval. Define how stale data and missing observations are shown so an old value is not mistaken for current output. Agree the role-based views and the shift report or other summaries needed by each team, including which reports are generated and when. Demonstrate these views and updates before rollout.
Send the reports and screens you use today, the stations you most want to see, a short video of each and the targets you work to. Note which figures cause the most debate in meetings. With that, we can design the views, confirm camera positions and show a sample of the live screen before anything is rolled out.
Share your current reports, the stations you care about and a short video of each. We will plan a manufacturing productivity dashboard for that line and explain timing and cost. A first line view is usually live inside 4 weeks.
