Problem
Inventory teams often have stock data, but not a clear view of dead stock, reorder risk, and which items deserve attention first.
Enterprise Operations Case
Inventory Analytics Dashboard
A portfolio case built for enterprise-scale inventory reporting: stock availability, demand signals, dead stock, reorder risk, and weekly actions for food, retail, and distribution operations.
200
SKU records modeled
12 Months
Usage and demand history
6 KPIs
Built for weekly decisions
Approach
Build a reporting layer around demand history, stock on hand, lead time, and business rules so exceptions are visible immediately.
Outcome
A dashboard and weekly review flow that helps teams prioritize reorders, slow-moving stock, and at-risk SKUs.
Dataset & KPI Design
- Scenario-modeled SKU master with category, supplier lead time, safety stock, and unit cost.
- Monthly usage / demand history to compare current stock against expected consumption.
- KPIs designed for action: stock cover days, turnover rate, dead stock %, reorder risk, ABC mix, and slow-moving value.
- Output framed for weekly operations review instead of only descriptive charts.
What the Dashboard Answers
Which items have low stock coverage and need reorder attention this week?
Which SKUs are consuming warehouse value without moving fast enough?
Which categories have healthy turnover and which need clean-up or policy review?
What should management see in one slide without reading raw spreadsheets?
Dashboard Preview
The reporting layer is intentionally business-oriented: a quick KPI row, demand-vs-stock trend, and stock mix review that points the team toward action rather than passive monitoring.
Suggested stack: Power BI or Google Sheets dashboard + SQL-style KPI logic
Why This Matters for Inventory Roles
- Shows that I can translate raw stock data into management-ready reporting.
- Connects analytics to business decisions like reorder prioritization and slow-moving review.
- Keeps the operational mindset: adoption, accuracy, and clarity matter as much as the visuals.
- Builds on my real background in lab operations, QA discipline, and workflow automation.