Sina Nezhadian

Sina Nezhadian · Quality & program leadership for complex hardware

I turn recurring customer failures into fixes that hold.

Root-cause problem solving, customer quality and delivery on safety-critical hardware programs.

Most quality problems are solved at the wrong level. I find the one the customer is actually living with.

Sina Nezhadian
LAX Automated People Mover
$4.9B
Driverless people mover
BART Fleet of the Future
775‑car
Rail fleet (BART)
ICBB · 2024
Black Belt
Lean Six Sigma Black Belt

Alstom · BART Fleet of the Future · LAX Automated People Mover · Lean Six Sigma Black Belt

Case studies

The problem, what I did about it, and how we knew it worked.

Writing

Field notes on finding causes, controlling change and measuring what matters — from rail fleets to AI hardware.

Flagship study · Root cause · Customer qualityHow the best tech companies find the real cause — and what it means for customer qualityAmazon’s COEs, Google’s blameless postmortems, Anthropic’s and Meta’s public incident write-ups, Toyota’s A3 and Ford’s 8D — what each actually publishes, the pattern they share, and how I would apply it on a customer-quality team.Read the study →Research: Productive Compute →

How I solve problems

  1. 01

    See the problem from the customer’s side

    A complaint arrives as a symptom, and the first instinct is to fix the symptom. I start with what the customer is living with — when it happens, how often, what it costs them — because that decides which problem is worth solving.

  2. 02

    Find the cause the system is hiding

    A failure that keeps coming back is usually protected by how the work is organized: a handoff nobody owns, a test that checks the wrong thing, a fix that treats one unit instead of the fleet. That is where I look first.

  3. 03

    Close it only when the evidence holds

    Writing the corrective action is the easy part. I treat a problem as closed when the evidence shows the failure has stopped, not when the paperwork says it should have.

Also building

DeciBrief is an AI tool I built and launched that turns messy notes into a clear recommendation, options, trade-offs, ranked risks, unknowns and next actions.

Before building it, I validated the problem in 50+ one-on-one conversations with former colleagues and industry connections, and set one success metric: time to a clear decision.

It runs on the same discipline as an 8D: state the real problem first, weigh the options openly, and name what happens next.

Visit decibrief.com →

Get in touch

Happy to compare notes on customer quality, root cause or a hardware program that needs steadying.

snezhadianpm@gmail.com