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Manufacturing data collection is the process of capturing machine and production data directly from the shop floor, including cycle times, downtime events, machine status, and OEE (Overall Equipment Effectiveness). It can be done manually, with paper logs and spreadsheets, or automatically, using sensors, PLCs, and IIoT (Industrial Internet of Things) devices. Automated collection typically captures a data point every few seconds, compared to a single end-of-shift entry from a manual log, which is why most manufacturers moving past 10-15 machines eventually switch to an automated system.
Manufacturing data falls into a few core categories. Most monitoring platforms, including Caddis, track 25 or more of these metrics simultaneously across every connected machine.
Without this data broken out by category, it's hard to tell whether a machine is slow because of the process, the operator, or the equipment itself.
Manual collection is where most shops start, and it has real limits. An operator logs downtime reasons on a whiteboard or a paper traveler, and someone transfers that into a spreadsheet at the end of the shift or week.
The problems show up at scale:
Automated data collection solves this by pulling data straight from the machine or a connected sensor, with no one needing to remember to write anything down. Caddis, for example, captures machine state changes and downtime events within seconds through non-invasive current transducers, direct PLC connections, IIoT gateways, or an API, whatever fits a given machine, including equipment that's decades old with no built-in digital interface. Most machines are connected and reporting data within a couple of hours, with no rewiring or IT involvement required.
Non-invasive sensors clamp onto a machine's power line and detect run, idle, and off states from electrical current draw. This works well for older or legacy equipment that has no digital output at all.
For machines with a programmable logic controller (PLC), data collection software can read directly from the control system, pulling cycle counts, alarms, and state changes without any additional hardware.
IIoT gateways sit between machines and the network, aggregating data from multiple sensors or protocols and sending it to a central platform. This is a common approach on mixed shop floors with equipment from different manufacturers and eras.
Newer machines often support standard industrial communication protocols. Connecting through OPC-UA or MQTT lets a monitoring platform pull data natively, without extra hardware.
For fully digital environments, an API connection lets a monitoring platform exchange data directly with existing systems, including ERPs like SAP or NetSuite, or business tools like Slack and Microsoft Teams for alerting.
Most plants end up using a mix of these methods rather than standardizing on one, since a shop floor built up over 20 or 30 years rarely has uniform equipment.
The value of manufacturing data collection comes from speed. A downtime event logged and reported in under five seconds gives a supervisor time to intervene during the shift. The same event discovered in a weekly report only explains what already happened.
Real-time visibility supports several decisions that manual logs can't:
A few questions narrow the decision quickly:
Caddis was built around these constraints specifically for small and mid-size manufacturers: 25+ tracked metrics, sub-five-second latency, flexible connectivity and integration options across sensors, PLCs, IIoT gateways, and API, plus native integration with SAP, NetSuite, Dynamics, Slack, and Teams. Shops can also test it directly on their own floor with a free 60-day pilot on up to 10 machines before committing to a rollout.
Most failed data collection projects don't fail because of the technology. They fail because of how the project was set up.
Collection is the first step, not the end goal. Once machine state, downtime, and OEE data are flowing in real time, that data needs to reach the people who can act on it, a supervisor deciding whether to pull a technician mid-shift, a plant manager reviewing which machine lost the most time this week, or a maintenance lead scheduling service before a bearing fails instead of after.
This is where dashboards, automated alerts, and reporting turn raw numbers into something a team actually uses. A downtime event that triggers a text message to a maintenance technician within seconds is far more useful than the same event sitting in a database until someone runs a report on it.
Manufacturing data collection is the process of capturing machine and production data, such as cycle times, downtime, machine status, and OEE, either manually through logs and spreadsheets or automatically through sensors, PLCs, and IIoT devices.
Machine state (run, idle, off) and downtime events with reason codes deliver the fastest return, since they immediately expose where production time is being lost.
Non-invasive current transducers and sensors can detect machine state from electrical current draw, making it possible to collect data from older machines with no digital interface at all.
Most machines can be connected and reporting data within a couple of hours per machine, using a self-install approach that doesn't require rewiring or IT involvement.
No. Data collection platforms feed accurate, real-time machine data up into an ERP or MES rather than replacing them. Those systems are only as reliable as the shop floor data flowing into them.
Manual logs and spreadsheets can get a shop through its first few machines, but they run out of accuracy fast once short stops, multiple shifts, and dozens of machines enter the picture. Automated, real-time data collection is what turns machine activity into decisions a plant can actually act on the same shift it happens.
If you want to see what real-time machine data collection looks like on your own floor, book a free demo with Caddis Systems.