CapEx_Factory_Readiness

CapEx Factory Readiness Command Center (NPI TPM / OPM Portfolio)

A factory readiness + CapEx governance portfolio project: readiness gating, critical-path visibility, CapEx variance tracking, expedite burn analysis, and automated leadership-ready evidence packs generated in CI.

All data is synthetic/anonymized.

Interview reviewer quickstart

1) Live demo (Streamlit): https://capexfactoryreadiness-3t3ngaxnz2fvjf8jqsxkvg.streamlit.app/
2) CI workflow: .github/workflows/capex_readiness_ci.yml
3) Evidence pack outputs: docs/evidence/
4) Templates + samples: docs/templates/ + docs/samples/

Dashboard Preview

(Backup file for high-res viewing: docs/images/dashboard.pdf)

Why this exists (what it demonstrates)

This portfolio project demonstrates how I run complex, cross-functional programs where execution discipline + decision-making across CapEx + facilities readiness + supply chain execution intersect. It translates fragmented operational data into a clear operating view—readiness status, critical path, variance drivers, and expedite risk—so teams can make faster, higher-quality decisions and leadership has consistent visibility.:

Clear “what’s blocking install/power-on?” visibility
Early identification of gate slip risk (open/blocked/high-risk work)
CapEx variance + forecast drift surfaced by program / category / month
Expedite burn tracking by vendor and driver
Repeatable “evidence pack” outputs you can share with leadership

What questions the dashboard answers

What’s on the critical path right now (per program/tool)?
What’s blocking install → power-on → commissioning → SAT?
Where are we burning expedite, and which vendors drive it?
Which gates are most likely to slip, and why?
Where is CapEx trending vs plan/forecast (what’s driving variance)?

Key results (from the included synthetic dataset)

Dataset scale

CapEx plan vs actuals: (program × tool × category × month)
Facility readiness tasks: (dependencies + gates + risk)
Lead-time / expedite lines: (need-by vs promise vs received + expedite spend)
Coverage: 5 programs, 50 tools, 6 categories, 6 vendors, 24 months

Example insights you can demo (synthetic)

Planned CapEx: $561.8M vs Actual: $569.3M → +$7.5M variance
Expedite spend: $7.6M across 1,434 expedited lines
Readiness spread: ~57.5% → ~87.0% across tools with multiple R/A flags

These numbers are computed from the synthetic CSVs checked into data/raw/.

What’s included

1) Streamlit dashboard

Entry point: app.py
Reads from: data/raw/ (synthetic CSVs)

2) Analytics modules (reusable program logic)

src/analytics/readiness.py — readiness rollups + RAG
src/analytics/critical_path.py — dependency-aware critical path per tool/program
src/analytics/expedite.py — vendor burn summaries

3) Evidence pack (auto-generated + CI artifact)

Generated by:

python -m src.tooling.generate_evidence

Written to:

docs/evidence/

Outputs:

docs/evidence/readiness_score_output.md
docs/evidence/critical_path_output.md
docs/evidence/expedite_summary_output.md
docs/evidence/capex_variance_snapshot.md
docs/evidence/gate_slip_risk_output.md

How to run locally

Prereqs

- Python **3.11+**

Setup

python -m venv .venv
# Windows:
# .\.venv\Scripts\activate
# macOS/Linux:
# source .venv/bin/activate

pip install -r requirements.txt

Run the dashboard

streamlit run app.py

Generate evidence pack

python -m src.tooling.generate_evidence

CI / Automation

GitHub Actions — “capex-readiness-ci”

Workflow file:

.github/workflows/capex_readiness_ci.yml

What it does:

Installs dependencies
Runs python -m src.tooling.generate_evidence
Uploads docs/evidence/** as a CI artifact

Data model (synthetic)

Raw inputs:

data/raw/capex_plan_vs_actuals.csv
data/raw/facility_readiness_tasks.csv
data/raw/lead_times_expedite.csv

Optional rollups (if you add them later):

data/processed/

Data dictionary:

docs/data_dictionary/

Program management artifacts

Templates

docs/templates/DECISION_LOG_TEMPLATE.md
docs/templates/RAID_LOG_TEMPLATE.md
docs/templates/WEEKLY_EXEC_UPDATE_TEMPLATE.md

Samples

docs/samples/DECISION_LOG_SAMPLE.md
docs/samples/RAID_LOG_SAMPLE.md
docs/samples/WEEKLY_EXEC_UPDATE_2026-01-02.md

System view

docs/diagrams/system_view.md

Repo structure

data/
  raw/                       # synthetic/anonymized source data
  processed/                 # rollups used by charts 
docs/
  data_dictionary/           # column-level documentation
  diagrams/                  # system views
  evidence/                  # outputs
  images/                    # screenshots / preview PDF
  samples/                   # program artifacts
  templates/                 # program templates
src/
  analytics/                 # readiness, critical path, expedite summaries
  tooling/                   # evidence scripts
  utils/                     # IO helpers
app.py                       # Streamlit dashboard
.github/                     # CI workflow

How to adapt this to real work (safely)

Keep the logic, swap the data (never publish proprietary CapEx, vendor, or facility data)
Replace identifiers with anonymized keys (Program A/B/C, Vendor 1/2/3)
Prefer aggregated metrics over raw transactional exports