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.

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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/

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Dashboard Preview

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

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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

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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)?

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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/.

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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

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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

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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

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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/

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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

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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

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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

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Roadmap (optional next upgrades)

• Add scenario planning (forecast vs commit vs stretch)
• Add “gate readiness” go/no-go criteria checks per milestone
• Add a KPI page: OTD, lead time percentiles, expedite rate, variance aging
• Add tests for schema validation on input CSVs

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Languages & Tools

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License

See LICENSE.