Much of the OEE software on the market pulls its numbers from machine signals, operator entries, or both. That works for a machine that reports its own state. It works poorly at a station where a person assembles a part, because nothing there reports anything. The loss happens, the OEE number drops, and nobody can say why.
That gap should shape how you pick OEE software. For most assembly and mixed-process lines, the best OEE software is the kind that sees the station itself: cameras that capture every cycle, tie each one to video, and show where the time went. We build that kind of system, so we will tell you where we stand, then give you a checklist that works for any vendor you evaluate.
Your OEE number is probably too high.
Manual tracking flatters a line. TeepTrak’s 2026 State of OEE report found that manual logs overstate true OEE by 8 to 15 points, because short stops and speed losses slip past a clipboard. TeepTrak’s 2026 benchmark, built from deployment data at more than 450 factories in over 30 countries, puts typical automotive and Tier 1 OEE at 60 to 70 percent, against an 85 percent world-class target. If your plant tracks by hand, the distance between your reported number and those benchmarks may be larger than it looks.
What OEE software has to do
OEE multiplies availability, performance, and quality. The math is easy. Capturing the inputs is the hard part. A line loses minutes to a wait for parts, a slow changeover, or a rework loop, and none of those show up if the software only counts what a machine reports. Software that records every cycle turns those small losses into data. Our system captures 100 percent of cycles on the lines it covers, with the video behind each one.
Machine signals versus station-level vision
A machine signal tells you a machine stopped. It rarely tells you why, and it says nothing about a manual station. Station-level vision fills that in. It shows that a station waited on material, that a step got skipped, or that a changeover ran long because of sequence, not equipment. Our automated time studies break each station’s cycle into work, wait, pick, and walk time, so your team can fix layout, material flow, and sequence. Those are process problems, and the data points are in the process.
Machine data still matters, and you do not have to choose. We integrate with MES systems, PLCs, torque guns, and andons, so vision adds station detail to the data you already collect. Vision does not replace sensors for equipment health, such as vibration or temperature. If your biggest loss is a failing spindle, you need machine sensors too.
Here is an illustration of the difference. Say OEE on a final assembly line drops two points on second shift. A machine-signal system tells you availability fell. A station-level system shows that one station waited on a sub-assembly during every cycle of a new variant, and the video shows why the part arrived late. One answer is a number. The other is a fix.
Read OEE by station, shift, and variant.
A single line-level OEE figure hides the pattern that matters. Two stations can average out to a healthy number while one runs overloaded and the other waits. The same line can look fine on first shift and poor on third, or fine on one variant and poor on another. Good OEE software lets you cut the number by station, shift, and variant, so you find the loss where it lives instead of arguing about the average.
What to check before you buy
Every cycle, not a sample: Hourly sampling misses the small stops that add up. Ask each vendor what share of cycles they capture, and on which stations.
A path from the number to the cause: A three-point drop is a headline. You need the station, the cycle, and the footage.
Deployment that fits your plant: Ask how much hardware goes in, how long it takes, and whether it touches your network. Our cameras need no wearables, and the system runs on-site with no cloud integration.
Reports for different readers: IEs want cycle detail. Supervisors want a view they can use during a shift. Plant managers want a rollup. If one tool serves only one of them, someone will build a second report.
Cost as you scale: Ask what happens to price and effort when you go from one line to ten.
Ask where the data lives and what it is for
Cameras raise fair questions on the floor, and your team will ask them. Put the answers in writing before you install anything. Ours: processing and storage happen on the edge device, video stays on-site for up to three months, and nothing leaves your firewall. Our security and privacy approach rests on aggregated metrics about stations and processes, not individuals. We collect no biometric data, use no facial recognition, and offer automatic face blurring. The unit of analysis is the station.
Three mistakes we see
The first is buying a number without a cause. A score that drops without an explanation does not help anyone improve anything. Ask each vendor to walk through your last major loss and show how their system would have found it.
The second is treating the pilot as the whole project. A pilot on one line proves the data matches what your team already knows. It does not prove what scaling costs, so ask about that early.
The third is leaving security to the end. Camera footage and process detail are sensitive, and some automotive and aerospace plants will not let production data leave the building. Settle where data lives before you compare features.
Making the call
Choose software that captures every cycle, explains each loss at the station where it happened, fits your network and security rules, and tells your team plainly what the data is and is not for. Test it on your messiest line, not your cleanest one. A system that explains a bad day teaches you more than one that looks good on a good day.
If your OEE number comes from a clipboard or a machine signal and you cannot say why it moved last Tuesday, Invisible AI can show you the station behind it. We will set up a walkthrough on your own line. Request a demo.