Most multi-site operators can’t answer that with confidence. aeShield brings real-time, enterprise-wide risk management to every Burner Management System you own — so gaps don’t stay hidden until an incident finds them.
If you hesitated on any of those, you’re not alone. Fired equipment risk is typically assessed fired device by fired device, then filed away until the next PHA cycle — a snapshot that’s already stale the moment it’s signed off. What you need is real-time, enterprise-wide risk management for every Burner Management System you own, not a periodic study.
A “risk gap” usually isn’t one dramatic failure. It looks like this:
Each of those, on its own, can look like a little bit of drift — not enough to trip an alarm by itself. Aggregated at each fired device and across your enterprise, though, they add up to a portfolio that no longer meets your own corporate risk criteria. Most companies only find that out during an incident investigation.
Occupancy counts, frequency data, and judgment calls vary study to study, with no shared reference.
In practice, almost no one runs actual blast modeling. Consequence selection comes down to the loudest voice in the room — so identical unit operations end up with wildly different rankings.
Risk analysis runs fired device by fired device, site by site, with no company-wide list to compare against.
The result: funding paralysis. When Boiler #1 comes back “fine” and Boiler #7 — the same unit operation — comes back with a huge risk gap, management has no consistent basis to act on. Even when the gap at Boiler #7 is real, it’s hard to justify spending real money there while an identical unit down the road needs nothing. Without a shared, defensible consequence basis, that inconsistency itself becomes the reason funding is delayed and risk gaps are carried in the portfolio longer than necessary.
A five-step corporate program — same rigor, lower cost, every site.
One inventory of every fired device, every site.
One risk analysis method, applied consistently.
Blast-modeling-based consequence selection.
One design per risk tier, reused fleet-wide.
Ongoing KPIs to flag bad actors early.
Enter chamber volume and loss-of-flame scenario, and see the fatality radius, collapse radius, and resulting BMS SIL targets update live — the same four-input consequence basis every facilitator, every site, can apply consistently.
Consequence selection driven by engineering rigor, not the loudest voice in the room — so the same risk gets the same rank, whoever's facilitating and wherever the unit sits.
Launch calculator →aeShield’s expert system codifies your corporate standard into a reusable, digitally enabled BMS design template for each unit operation — so every similar fired device, at every site, gets the same defensible design instead of starting from a blank page. Fewer engineering hours per site, faster execution, and one consistent basis instead of one bespoke design per fired device.
Bulk-load your instrumentation data, press the button, and in minutes every deliverable is finished — consistent, high quality, and digital from the start. It’s the transition from dumb, static BMS designs to data-driven solutions: ready to be connected via API to the real world, monitoring assumed BMS performance against actual field data and flagging real risk gaps as they emerge, instead of gathering dust until the next audit.
Talk to our team →A site rarely funds an enterprise risk program on its own budget line. What gets funded is a hard, defensible ROI case built on test interval optimization — taken to your Digital Transformation team, not just the site. That's the case that pays for the whole program.
Keep testing on today's schedule — just capture demand rates, bypasses, device failures, and proof-test results as you go.
aeShield's SIL engine turns that real history into statistical, prior-use evidence — not generic industry tables.
Qualifying instruments shift to longer proof-test intervals — fewer tests per year, full audit trail.
All three codes set a genuine, prescriptive minimum — typically a SIL 2 logic solver and a fixed, calendar-based proof-test interval, regardless of your unit's actual occupancy, demand rate, or failure history. That schedule assumes a generic device population, not your equipment's real duty cycle.
This isn’t asking you to test more field devices. It’s about collecting and analyzing the data from the testing you’re already doing — and using it to move off prescriptive, calendar-based intervals onto a data-driven basis. That evidence is what lets you safely optimize test intervals (including extending them where the data supports it) while proving, with the data itself, that you’re still safe.
Build a 12-year NPV model bottom-up from your own crew, rates, and test intervals — and see exactly how the program pays for itself, with savings that compound every year the program runs.
Launch calculator →Hard, compounding savings from testing fewer instruments per year — not a soft, hand-wavy estimate.
Auditable, data-driven test intervals defensible against IEC 61511 and NFPA 85/86/87 — not generic industry tables.
Real-time enterprise risk visibility and live barrier health — the same data powering the savings case.
A thorough overview of BMS unit operation and the process safety hazards behind it — built around a single-burner gas-fired steam boiler and its full operating cycle (pre-fire, purge, light-off, normal operation, post-purge).
Placeholder titles below — swap in your actual white paper files/titles before publishing.
Technical deep-dives for process safety and instrumentation engineers.
What recent PHMSA incident findings mean for your BMS testing basis.
Recorded session on building the compliance case for digital BMS lifecycle management.
Talk to our process safety team, or try either calculator first — no pressure either way.
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