Approach
I build decision support systems for environments where assumptions fail fast and consequences show up in cash,
service level, and operational stress. The goal is simple: reduce surprise over time.
How I work
- Baseline first: establish what is true right now. No hidden defaults.
- Assumptions explicit: scenarios are labeled and never merged into baseline.
- Constraints modeled: lead times, capacity, cash, MOQ, compliance, and real limits.
- Tradeoffs visible: show what gets better, what gets worse, and what breaks first.
- Decision provenance: preserve inputs, logic, and why a choice was made.
- Outputs executable: decisions translate into actions a team can actually run.
What I do not do
- No motivational framing or “should” statements.
- No pretending the data is clean when it isn’t.
- No one size fits all playbooks that collapse under constraint.