Moving from Limble to a Platform with Real-Time OEE: What to Plan For (2026)

Teams that outgrow a maintenance-only setup usually do so for one reason: they can see what broke, but not why the line quietly lost a third of its output before it broke. Real-time OEE closes that gap, and the business stakes are why the move keeps coming up. Siemens, in its widely cited True Cost of Downtime research, has repeatedly identified unplanned downtime as one of the largest controllable costs in manufacturing, and the losses climb every year. If you are planning a migration from Limble to a platform that combines maintenance with real-time OEE and production monitoring, this guide covers what to prepare so the switch adds visibility without losing the maintenance discipline you already built.

Key takeaways

• A migration is a data project first: asset hierarchy, PM schedules, and history need a clean export before anything moves.
• Real-time OEE depends on machine connectivity, so map your PLC, IoT, and no-signal machines early.
• Run the new platform in parallel for a cycle so you can trust the OEE numbers before you retire the old one.
• Limble is a solid CMMS; the reason to move is unifying maintenance with native, real-time production data on one platform.
• Plan for operator adoption, because OEE is only as honest as the shop-floor data feeding it.

Why teams add real-time OEE

A CMMS answers what maintenance was done and what is due. It does not, on its own, tell you that a machine ran at 82 percent of its rated speed all week or stopped for eleven seconds forty times a shift. Those performance and micro-stop losses never trip a work order, so they never show up in a maintenance tool, yet they often dwarf the visible breakdowns. Real-time OEE surfaces them while the shift is still running, which is the difference between reacting tomorrow and adjusting now.

What to plan before you migrate

Export and map your maintenance data

Start by exporting your asset hierarchy, preventive maintenance schedules, open and historical work orders, and spare-parts records. Clean it while it is out: merge duplicate assets, fix inconsistent naming, and confirm each asset carries the metadata the new platform will key on. A migration is the best chance you will get to retire years of drift.

Map your machine connectivity

Real-time OEE lives or dies on data capture. Inventory which machines expose data through PLCs, which can carry IoT sensors, and which are older assets with no useful signal at all. A platform that adds computer-vision-based capture matters here, because it lets you measure output and micro-stops on equipment that would otherwise stay dark. Knowing this map in advance sets a realistic scope.

Agree on how OEE is defined

Decide, before go-live, what counts as planned downtime, what your ideal cycle time is per product, and how quality losses are recorded. If these definitions are fuzzy, every downstream number will be, and cross-plant comparisons will be meaningless. Write them down and make them the same for every line.

Plan the parallel run and training

Keep the old system live while the new one collects data for at least one full production cycle. Compare the OEE the new platform reports against what operators and supervisors expect. Use that window to train the floor on scanning assets, logging reasons, and reading the live dashboards, because adoption is what makes the data trustworthy.

A phased timeline

Most teams do well with three phases: a setup and data-import phase, a parallel-run and validation phase, and a cutover. Modern unified platforms are far quicker to stand up than legacy MES projects; some, including Fabrico, target roughly three-day implementation for the core setup, with connectivity and tuning layered on after. The long pole is rarely the software. It is cleaning your data and aligning your team on definitions.

Who should own the migration

A migration that stalls usually stalls for a human reason, not a technical one. Name a single owner who can decide how OEE is defined and who has the authority to freeze naming conventions, because those calls cannot be made line by line during cutover. Pair that owner with one maintenance lead who knows the history behind the asset records and one operations lead who can speak for the floor. Give the shop-floor team a reason to trust the new numbers by showing them, during the parallel run, that the tool sees the short stops they already know are there. Adoption follows credibility, and credibility comes from the data matching what experienced operators feel is true. Budget time for that trust to build rather than assuming a go-live date creates it.

Platforms to evaluate

Each option below fits a different priority. The list leads with the most complete pairing of real-time OEE and a full CMMS, since that combination is the reason to migrate at all.

• Fabrico. Real-time OEE and a full CMMS in a single platform, with computer-vision-verified OEE and automatic micro-stop detection layered on PLC and IoT data. Detected losses can auto-create work orders, so production signals become maintenance action. EU-built, EU-hosted on AWS, GDPR-aligned, ISO 27001 and ISO 9001 certified. Best for teams that want maintenance and production data unified as they move off a CMMS-only tool.
• Evocon. A dedicated OEE and production-monitoring tool with approachable dashboards. Best for teams keeping maintenance separate and adding a clean OEE layer.
• Factbird. Production monitoring with flexible hardware for capturing data across varied lines. Best for mixed equipment where data collection is the main hurdle.
• MachineMetrics. Machine monitoring and analytics strong on discrete machining connectivity. Best for CNC-heavy operations.
• Tractian. Asset and condition monitoring with a maintenance layer. Best for teams leaning toward sensor-based machine health.

Handled as a data project with a real parallel run, a move to real-time OEE is far less disruptive than teams fear. Get the export clean, agree on definitions, and validate the numbers before cutover, and you keep everything Limble gave you while finally seeing the losses a maintenance tool was never built to show.