A Power BI dashboard + case-study portfolio that turns supply noise (PO lines, commits, shipments, holds) into exec-ready decisions across supplier performance, inbound logistics, shortages, and factory readiness.
🚀 Open Power BI Report • 📊 Download PBIX • 🧩 Template (PBIT) • 📘 KPI Glossary • 🗂️ Dataset Schema • 📚 Case Studies
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This repo is built like a real Ops/TPM program—not a generic dashboard.
- Executive Control Tower: KPI scorecards + trends + drilldowns (supplier/site/commodity/part)
- Root-cause visibility: commit volatility, late-risk density, and top late-$ drivers
- Readiness lens: shortages early-warning + critical-path hotlist behavior
- Cost governance: expedite burn & premium freight signal (where data exists)
- Story-driven proof: 5 realistic case studies with KPI baselines → recovery actions
Use case: WBR/MBR exec readouts, shortage war-room, supplier QBRs, and factory readiness reviews.
Main file: powerbi/supply_chain_dashboard.pbix
Template: powerbi/supply_chain_dashboard Template.pbit
What a hiring manager can do in under 2 minutes
- Filter by Supplier / Site / Commodity / Program / Month
- Identify late-risk concentration (heatmap) and top drivers (pareto/treemap)
- Diagnose commit drift & reschedule churn before it becomes line-down risk
- Drill into the part-level hotlist to drive owner + ETA recovery actions
| Dashboard Preview | Supplier Heatmap | Pareto / Treemap |
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Below are sample outputs you can replace after refresh with your data.
| KPI | Value | Definition |
|---|---|---|
| OTIF % | 13.7% | On-time AND in-full |
| Past Due $ | $9,365,982 | Open qty × unit price (need-by < today) |
| Expedite Spend | $1,611,166 | Freight cost where expedite_flag = true |
| Avg Commit Slip | 12.6 days | Delivery date − promise date |
| Reschedule Rate | 66.7% | % lines with reschedule_count > 0 |
| Quality Hold Rate | 8.8% | % lines with quality_hold_flag = true |
So what? (decision outcomes)
- Focus recovery on the top late-$ drivers instead of chasing every red line
- Convert commit volatility into a measurable risk signal (and fix the root cause)
- Treat shortage risk as a time-phased readiness gate (next 6 weeks), not a surprise
- Ingest: CSV extracts (POs, shipments, optional inventory/quality)
- Model: Star schema + date table + measures (OTIF, past due, slip, expedite, risk)
- Visualize: Exec control tower + drilldown pages
- Operationalize: Hotlists + owners + ETAs + recovery actions (captured in case studies)
└─ supply-chain-control/
├─ data/
│ ├─ processed/
│ │ ├─ dim_calendar.csv
│ │ └─ fact_supply_chain_flat.csv
│ ├─ raw/
│ │ ├─ inventory_snapshot.csv
│ │ ├─ po_lines.csv
│ │ ├─ quality_events.csv
│ │ └─ shipments.csv
│ └─ schema/
│ ├─ data_dictionary.md
│ └─ schema_star.md
├─ docs/
│ ├─ assets/
│ │ ├─ app.js
│ │ └─ style.css
│ ├─ case-studies/
│ │ ├─ case-study-01-otif-recovery.md
│ │ ├─ case-study-02-expedite-cost.md
│ │ ├─ case-study-03-shortage-early-warning.md
│ │ ├─ case-study-04-commit-health-reschedule.md
│ │ ├─ case-study-04-ppv-should-cost.md
│ │ ├─ case-study-05-line-down-prevention.md
│ │ └─ case-study-05-quality-hold-recovery.md
│ ├─ data/
│ │ └─ processed/
│ │ └─ fact_supply_chain_flat.csv
│ ├─ images/
│ │ ├─ case01.png
│ │ ├─ case02.png
│ │ ├─ case03.png
│ │ ├─ case04.png
│ │ ├─ case05.png
│ │ ├─ dashboard_preview.png
│ │ ├─ Pareto_Example.png
│ │ ├─ README.txt
│ │ └─ Supplier_Heatmap.png
│ ├─ index.html
│ ├─ kpi_glossary.md
│ └─ README.txt
├─ powerbi/
│ ├─ supply_chain_dashboard Template.pbit
│ ├─ supply_chain_dashboard.pbix
│ └─ supply_chain_dashboard.pptx
├─ .gitignore
├─ LICENSE
└─ README.md
---
- Open:
powerbi/supply_chain_dashboard.pbix - Click Refresh
- Validate visuals + KPI totals
- Drop CSVs into
data/raw/ - Power BI Desktop → Transform data
- Update file paths/parameters to point at
data/raw/ - Close & Apply → Refresh
- Publish to Power BI Service
- Replace the top link:
🚀 Open Power BI Report (add link)→ paste your report URL
If this repo is public, use sanitized/demo data only.
Your CSVs should support fields like:
PO lines
po_number, line_id, supplier, commodity, site, part_number, open_qty, unit_price, need_by_date, promise_date, reschedule_count
Shipments
shipment_id, carrier, mode, lane, ship_date, delivery_date, expedite_flag, freight_cost
Inventory snapshot (optional)
part_number, site, on_hand_qty, safety_stock, snapshot_date
Quality events (optional)
part_number, supplier, quality_hold_flag, ncr_id, event_date, disposition
See: data/schema/data_dictionary.md
Each case study is written like a real ops readout: baseline → driver analysis → actions → results.
-
OTIF Recovery for Long-Lead Subsystems (Chambers & RF)
docs/case-studies/case-study-01-otif-recovery.md
-
Expedite Spend Reduction + Lane Discipline
docs/case-studies/case-study-02-expedite-cost.md
-
Shortage Early-Warning for Build Readiness (Next 6 Weeks)
docs/case-studies/case-study-03-shortage-early-warning.md
-
Commit Health + Reschedule Churn (Volatility Control)
docs/case-studies/case-study-04-commit-health-reschedule.md
-
Line-Down Prevention via ASN + Dock-to-Stock + Kitting
docs/case-studies/case-study-05-line-down-prevention.md
- Add risk scoring: late $ × criticality × lead time × volatility
- Add owner/ETA workflow for part hotlist (recovery tracker page)
- Add Exec export pack (PDF snapshot for weekly readouts)
- Add PPV / should-cost drilldown as a dedicated page (case study tie-in)
This is a demonstration project for portfolio. If you'd like to extend it:
- Fork the repository
- Create a feature branch
- Add enhancements (new models, visualizations, data sources)
- Submit a pull request
Let's connect! Whether you have a question or just want to say hi, feel free to reach out.
| Platform | Link |
|---|---|
| 👤 Name | Sourabh Tarodekar |
| ✉️ Email | sourabh232@gmail.com |
| linkedin.com/in/sourabh232 | |
| 🚀 Portfolio | QuantuMaster007 Portfolio |
MIT License - See LICENSE file for details