NetBrain — Monte Carlo ROI Simulator

Customer · Monte Carlo ROI Simulator

10,000 scenarios per run · Prepared for discussion · July 2026

The economics of visibility and Agentic NetOps

Every assumption below is a range, not a point estimate. Each run simulates 10,000 scenarios — alternative versions of a year of operations — sampling outage frequency, duration, hourly impact and automation effectiveness to produce a distribution of benefit and ROI rather than a single number to argue about. Adjust any slider live; results update instantly.

Assumptions

Ranges feed the simulation as distributions (triangular / lognormal / Poisson). Defaults are deliberately conservative.

1 · Cost of network downtime

Service-impacting network incidents. NetBrain acts on both failure levers: MTBF (fewer incidents — proactive drift, golden-config and intent checks catch faults before they bite) and MTTR (shorter incidents — automated triage, isolation and root cause replace manual alert analysis). Benefit = prevented incidents at full cost + faster recovery on the rest.

Major network incidents / year (mean)

Typical outage duration — median hrs

Cost per hour of outage — most likely $k

MTTR reduction with NetBrain — most likely %

Incidents prevented (MTBF extension) — most likely %

2 · Discovery, documentation & inventory

Manual mapping, diagram upkeep and CMDB reconciliation — the effort of keeping documentation current by hand. Continuous automated discovery replaces this effort with an always-current source of truth. Benefit = engineer hours displaced × loaded rate.

Network devices in scope

Engineers touching discovery / docs / CMDB

% of their time on it (most likely)

% automated by continuous discovery

3 · Automated troubleshooting

Business-as-usual tickets needing network diagnosis. Agentic triage runs isolation, path checks and config comparison before an engineer opens the ticket — the 'automate NOPs' goal. Benefit = tickets touched × hours saved per ticket × rate.

Network tickets needing diagnosis / yr

Avg engineer-hours per ticket

% of tickets auto-triaged / diagnosed

Time saved on those tickets — most likely %

4 · Compliance verification

Config-vs-policy checking, drift investigation and audit evidence gathering. Golden-config intents run these checks continuously across the estate and produce audit-ready reports. Benefit = manual hours automated × rate.

Manual compliance / audit hours per yr

% automated by golden-config intents

5 · Tool displacement (optional)

If NetBrain replaces incumbent tooling, the displaced licence and maintenance spend comes back. The clawback percentage reflects reality: overlap periods, partial displacement, and multi-year contracts mean rarely all of it returns at once.

We are replacing one or more existing tools

Annual cost of displaced tool(s) — enter actual ($)

Clawback — % of that spend recovered

Costs & rates

Currency

Loaded rate = salary + benefits + overheads, blended across your network engineering locations. Type the actual proposed investment directly — annual licence and support, plus one-off implementation — so the model runs on the real deal size. Implementation counts in payback but not steady-state ROI.

Loaded engineer rate $/hr

NetBrain annual investment — enter actual ($)

AMS managed service — recurring ($)

Year-1 implementation — enter actual ($, one-off)

Assumptions used in this run

Cost of doing nothing — good year

–

Annual benefit — good year

–

Return on investment — good year

–

Engineer time returned — good year

–

Chance it pays for itself

–

share of 10,000 scenarios where benefit exceeds cost

Payback time — good year

–

Distribution of annual net benefit (benefit − investment)

Where the value comes from — good year ($M / yr)

ROI distribution — steady-state annual

Bad year / good year / great year, by value stream ($M / yr)

Reading the numbers. A "good year" is the median of all 10,000 scenarios — half came out higher, half lower. A "bad year" (P10) is one only 1-in-10 scenarios fall below; a "great year" (P90) one only 1-in-10 exceed. Bad, good and great here describe the return on the investment. For the downtime stream that means the platform behaves like insurance: a great year for the ROI is a rough year for the network, when automated diagnosis and prevention are worth the most — and a bad year for the ROI is simply a calm one. "Tool displacement" (optional) credits the recovered spend of tooling NetBrain replaces, sampled around the chosen clawback percentage; when enabled it is also added to the cost of doing nothing, since standing still means continuing to pay it. "Engineer time returned" is the labour side of the benefit — documentation, diagnosis and compliance hours displaced by automation, expressed in hours and full-time-equivalents (1,880 productive hours per engineer-year), with outage-hours avoided shown separately. "Cost of doing nothing" is the status-quo annual exposure with no NetBrain: the full outage impact plus all documentation, diagnosis and compliance effort at the loaded rate. The benefit streams are the share of that exposure the platform removes. Method & sources. Monte Carlo with 10,000 scenarios per run, each representing one possible year of operations. Incident counts are Poisson-distributed; outage duration and cost-per-hour are lognormal (heavy right tail — the occasional very bad day is modelled, not ignored); automation effectiveness and effort shares are triangular around the slider value (±40% spread on the most-likely value; savings capped at 95%). Value streams map to the goals most network operations teams share: reduce MTTR, reduce ticket volume, and automate triage, diagnosis and routine operations. Switching currency converts the monetary inputs at a fixed planning rate of £0.79 per $1 (adjust inputs directly if you prefer a different rate). Cost-per-outage-hour defaults reflect published industry estimates for large financial institutions; the investment figures are whatever is typed in — a formal quote comes only through the normal quoting process — and all other figures are assumptions for discussion, not commitments, and every input can be re-based on your actual incident and staffing data during a scoped POC.