CMMS for Steel & Metals
Protect rolling mills, furnaces and cranes from costly unplanned stops.
Steel and metals plants run on capital-intensive rotating and thermal equipment — rolling stands, reheating furnaces, EOT cranes, hydraulic power packs — where a single unplanned stop can idle an entire line. AssetAI is a generic CMMS that applies one consistent asset, schedule and failure model across all of it, so the discipline you build on one mill stand carries over to the next furnace or crane without re-configuring anything.
Modelling Equipment the Way Your Engineers Actually See It
A rolling stand isn't one asset — it's a stack of assemblies, rolls, bearings and drive components, each with its own wear life and repair history. The five-level EBS (Equipment → Assembly → Sub-Assembly → Component → Part) lets you break a stand down to the individual roll or bearing while work orders and BOM history still roll up to the parent asset. That structure matters when you're deciding whether to keep re-lining a furnace or replace it — the RRR verdict weighs cumulative work-order cost against purchase cost, failure trend, downtime cost and age to give you a data-backed repair/review/replace call instead of a gut decision.
Meter-based PM on the same asset tree handles roll changes and burner hours through run-hour or cycle counters, with readings coming in by QR-scan photo OCR, WhatsApp, the meter screen, or API — every route rejects a reading lower than the last one logged, so a wrong entry can't silently reset a wear counter. For assets where a physical parameter matters more than elapsed time, lubrication and condition-based schedules let you set a monitored parameter (vibration in mm/s, for example) with an alarm threshold, checked against manually logged readings rather than a live sensor feed.
Getting From "It Broke" to "Why It Keeps Breaking"
On a shop floor where operators won't carry app logins, a QR scan on the machine plus a name and company PIN is enough to raise a breakdown — and if it's tagged emergency severity, the work order fires immediately, skipping approval so a line-stopping failure doesn't sit in someone's inbox. What keeps this useful past the first few weeks is that breakdown and corrective work orders can't be closed without a recorded failure cause and remedy — enforced in code, not left to habit. Over time that turns your data into a genuine Pareto: downtime cost per asset (downtime hours × that asset's downtime cost/hour) ranked across the top 8 offenders, which is usually where a chronic hydraulic leak or a repeatedly-failing furnace burner shows up long before anyone builds a spreadsheet to prove it.
What This Isn't, and Why That's Deliberate
AssetAI doesn't ship furnace-specific or crane-specific logic, and it doesn't roll KPIs up to a "line" or "mill" level automatically — every Pareto and downtime figure is per-asset, and line-level views mean filtering the location tree yourself. There's no permit-to-work gate either; safety controls like LOTO, PPE and gas testing print as a checklist on the job sheet, not a sign-off workflow. Inspection records store a free-text regulation name and certificate number rather than a built-in IS or ISO crane/pressure-vessel certification path — worth checking against your own standards obligations, alongside general references like ISO's published standards. If your priority is closing this gap between recorded failures and actual OEE improvement, it's easier to see on your own asset list — book a 30-minute walkthrough or compare plans on pricing.
Rolling mills and reheating furnaces in India run on a mix of permanent operators and contract labour on rotating shifts, and that reality shapes how breakdown reporting and severity handling need to work — not just how assets are modelled.
Getting a Breakdown Logged Without Fighting the Shop Floor
Most CMMS rollouts stall at the point of asking a furnace operator or crane driver to log into an app. AssetAI skips that friction: an operator scans the QR code on the machine, types a name and the company PIN, and the breakdown is logged — no app credentials to issue, reset, or chase down across shift changes. For a line-stopping event, severity matters more than protocol: an emergency-tagged breakdown raises the work order the moment it's submitted, bypassing the usual approval step so a stuck rolling stand or a tripped furnace doesn't sit waiting on a shift engineer's sign-off.
What this deliberately doesn't include is a permit-to-work gate — no permit numbers, issuance, or expiry tracking. What you get instead is a safety-measure checklist (LOTO, PPE, gas test) printed directly on the job sheet, which covers the documentation most plants actually need without bolting on a workflow this platform isn't built to own. If your site runs formal hazardous-work permits under a statutory system, that stays a parallel process — check ISO's standards library and your own standards mapping to see where the boundary should sit.
Isolating the Furnace or Stand That's Actually Costing You Money
Every asset in the registry carries a downtime cost/hour, and AssetAI multiplies that against logged downtime hours to rank the worst eight assets on a Pareto chart. In a steel plant with multiple rolling stands, reheating zones, and EOT cranes feeding one line, this is how you find the actual offender instead of guessing from gut feel or the loudest maintenance request. It's asset-level by design, not line- or mill-level — if you need a rolled-up cost view across an entire finishing line, you build it by filtering the location tree yourself rather than relying on a pre-built line KPI. That's a deliberate trade-off in favour of a consistent CMMS engine over bespoke steel-industry rollups, and it pairs naturally with whatever OEE tracking or TPM programme you're already running on the same lines.
AMC and warranty precedence works the same way underneath every asset in the Pareto — in-warranty and under-AMC assets auto-stamp the service call and pre-fill the vendor, so a mill-stand bearing failure under warranty doesn't get logged and costed as if it were an open-market repair. See the full breakdown of how this fits together on features, compare plans on pricing, or book a demo with your own asset list to see the Pareto populate against real downtime.
CMMS for Steel & Metals FAQs
How do I track downtime cost for my worst-performing assets so I know which mill stand or furnace to replace first?
AssetAI computes downtime cost per asset by multiplying downtime hours by that asset's hourly downtime cost, then ranks your top 8 offenders in a Pareto view. This isolates which specific mill stand or furnace is draining the most production value, so you can justify capital decisions with real data. You set the downtime cost/hour when you create the asset in the asset registry, and the system updates the ranking every time a work order is closed—no manual calculation needed.
Can I set up automatic alerts when vibration or other monitored conditions hit a danger level?
Yes—AssetAI supports condition-based (predictive) schedules where you define a monitored parameter (like vibration in mm/s) and set an alarm threshold per schedule. When the condition crosses that threshold, the system flags it so you can act before failure. This works generically for any sensor data you feed into the schedule; you configure the threshold, not the CMMS logic.
What happens if a furnace or crane fails on the night shift—do the work order get stuck in approval?
No. Emergency-severity breakdowns bypass approval workflows entirely and raise the work order immediately at submit, so a line-stopping failure doesn't wait on sign-off. Operators report the breakdown by scanning the machine QR code and entering their name and company PIN—no login required, so shop-floor staff can report instantly without credentials. The work order is live the moment they hit submit.
How do I decide whether to repair or replace an aging furnace?
AssetAI computes an RRR (Repair/Review/Replace) verdict per asset from four inputs: cumulative work-order cost versus purchase cost, recent failure trend, downtime cost, and age. This gives you a structured view of whether repair spending is approaching replacement value, whether failures are accelerating, and what downtime is actually costing. The verdict is one data point—you make the call—but it organizes the facts you need. Learn more about how to interpret this for capital planning in our use cases.
How do we get nameplate data into the system without manual typing for every machine?
The asset registry includes nameplate OCR: photograph the rating plate on your mill stand, crane or hydraulic unit, and the system auto-fills serial number, year and capacity from the image. You still enter commercial and warranty data by hand, but OCR eliminates the tedious copying of nameplate specs, which cuts data-entry errors and speeds up asset setup. Upload a photo when you create the asset, and the OCR fields pre-populate.
Does AssetAI have steel-mill-specific maintenance rules built in?
No. AssetAI is a generic CMMS that applies one schedule, asset and failure model to whatever you load in—it has no furnace-specific, crane-specific or steel-specific logic. You define what preventive maintenance schedules, lubrication intervals and condition thresholds make sense for your equipment and compliance requirements, then the system tracks and reminds you. This flexibility means you control maintenance standards, not the software.
How do I handle scheduled maintenance during steel production campaigns without stopping the line?
Schedule preventive work during planned campaign breaks or shift changeovers. AssetAI lets you flag equipment as "maintenance-scheduled" so operators know it's coming down. Create work orders in advance tied to your production calendar, then execute them during your actual downtime windows—no guesswork on timing.