Case Study
Supply Chain Performance & Inventory Risk Intelligence
Data Analyst
Built an end-to-end supply chain intelligence dashboard tracking delivery performance, inventory health and supplier risk to enable proactive decisions.
01 / Business Problem
The challenge.
Supply chain leaders lacked a unified view of delivery performance, inventory health and supplier risk — making it hard to spot stockout risks, late shipments and underperforming suppliers before they hit revenue.
02 / Dashboard Gallery
Dashboards & visualizations.
Interactive reports that turn the analysis into decisions stakeholders can act on.
03 / Data Source
Where the data came from.
Operational supply chain dataset covering shipments, warehouses, suppliers and SKU-level inventory, combined with supplier scorecards and lead-time logs.
Multi-table dataset with shipment-level records (origin, destination, carrier, lead time, on-time status), SKU inventory snapshots, supplier master data with risk attributes, and historical demand by region.
04 / Methodology
How the analysis was built.
Data Cleaning
Consolidated shipment, inventory and supplier tables, standardized location and SKU codes, removed duplicate shipment events and engineered lead-time, on-time-flag and risk-tier fields.
SQL Analysis
Wrote SQL with CTEs and window functions to compute on-time delivery %, rolling lead times, inventory turnover, days-of-supply and supplier risk scores; validated against operational benchmarks.
Dashboard Development
Built a multi-page Power BI report — executive KPI overview, warehouse and route performance, inventory health, and a supplier risk page with drill-through to SKU-level stockout risk.
05 / Key Insights
What the data revealed.
On-time delivery drops ~9 points on long-haul routes through two specific transit hubs — a clear bottleneck.
Roughly 18% of SKUs drive 80% of stockout incidents, concentrated in fast-moving categories with low safety stock.
Top 12 suppliers contribute 65% of late shipments, mostly tied to extended lead-time variability.
Regional demand spikes are predictable from prior-month patterns but inventory is not pre-positioned accordingly.
06 / Recommendations
What the business should do next.
Reroute long-haul shipments away from the two bottleneck hubs or add a buffer transit day in planning.
Raise safety stock on the top 18% stockout-prone SKUs and review reorder points monthly.
Move the 12 high-risk suppliers to a weekly performance review and qualify backup suppliers for critical SKUs.
Pre-position inventory in high-demand regions based on the rolling 3-month demand forecast.
07 / Technologies
The stack.
Explore the code
Dive into the repository.
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