How MFG Tech Review evaluates machine monitoring platforms, transparently and consistently, against published criteria.
Every platform is evaluated independently across 12 criteria, each rated on a 0–10 scale where 10 is the best possible outcome and 0 means the capability is absent. Scores reflect publicly available information, direct product research, user reviews, and documented feature capabilities as of early 2026. No platform has paid to be included or to influence their score.
| Category | Scoring Logic | Why It Matters |
|---|---|---|
| Vibration Monitoring | 10=native vibration sensors; 7=third-party sensors supported; 0=unsupported | Primary indicator of mechanical failure in rotating equipment |
| Power Monitoring | 10=dedicated power/current sensing; 5=partial; 0=none | Electrical signature detects motor health and machine state |
| PLC Integration | 10=native PLC; 5=via adapters; 0=bypasses/unsupported | Enables deeper machine data beyond run/stop signals |
| Price | 10=price published and low cost per machine; 7=published, mid cost; 4=published high cost, or buyer-reported only; 0=no public pricing | Total cost of ownership affects ROI timeline for SMBs |
| Contract Terms | 10=no contract month-to-month; 5=annual; 2=multi-year lock-in | Long contracts increase switching costs and risk |
| Ownership Model | 10=software owned outright, or offered as both perpetual and subscription; 6=hardware purchased, software subscribed; 3=nothing owned, subscription or rental only | Choice of how to acquire the system, not a preference for one model |
| Predictive Maintenance | 10=AI-driven PdM native; 5=rule-based alerts; 0=unsupported | PdM is the highest-value application of machine data |
| Open API | 10=documented REST/GraphQL; 5=limited; 0=none | Enables ERP, CMMS, BI, and custom workflow integration |
| Installation Speed | 10=minutes; 7=hours; 4=days; 2=weeks or longer | Long deployments delay value and disrupt production |
| Installation Cost | 10=included; 7=charged, under $1,000 for a five-machine shop; 4=charged, $1,000-$5,000; 2=charged above that, or charged with no figure published | An unquantified install fee is a budget risk, not a rounding error |
| Self-Install Capability | 10=the buyer can install it; 0=a vendor or integrator must | Vendor-performed installs add scheduling, cost and dependency |
| OEE Tracking | 10=native automated OEE; 5=manual/partial; 0=not available | OEE is the universal manufacturing KPI |
The MIS is a 0–100 composite index measuring overall suitability for industrial machine intelligence, weighted across 6 dimensions:
| Dimension | Weight | Components |
|---|---|---|
| Sensor & Data Coverage | 20.0% | Vibration Monitoring, Power Monitoring |
| Predictive Maintenance | 20.0% | Predictive Maintenance |
| OEE Tracking | 15.0% | OEE Tracking |
| Commercial Flexibility | 15.0% | Price, Contract Terms, Ownership Model |
| Integration Depth | 15.0% | Open API, PLC Integration |
| Usability & Deployment | 15.0% | Installation Speed, Self-Install Capability, Installation Cost |
The weights above are chosen, and they are chosen for a particular buyer: a job shop of roughly five to twenty machines, with no IT department, buying machine monitoring for the first time. A different buyer would justify different weights, and a reader who disagrees with ours can see exactly what to disagree with.
They come from shop owner interviews, not from our own judgement. Buyers were given the six dimensions and a hundred points to divide between them. Our own first draft had put commercial terms at thirty per cent, on the reasoning that price and lock-in are the gate a small shop fails at. That is not what buyers said. They put the most on what a system can sense and on whether it warns them before something breaks, less on commercial terms, and spread the rest almost evenly.
Read plainly, that allocation says nothing dominates. Sensing breadth and failure prediction lead by five points over everything else, and the remaining four dimensions are level. A buyer at this size does not think one question settles it, and the weighting now reflects that rather than our assumption.
These weights replace a formula that set them from how much each dimension's criteria overlapped. That formula answered a real question, but it was a statistical property of the rubric rather than a statement about who this site is for. Correlations are still measured from the published scores at build time and still shown here: they run from 0.01 across criteria in different dimensions up to 0.68 for installation speed and self-install capability, so overlap stays visible rather than being absorbed silently into a weight.
Corrections to this rubric change published scores. Each one is dated and explained on the changelog, including the revision that produced the weights above.