This repository showcases an enterprise-grade Inventory Control & Quality Assurance (ICQA) analytics program designed to simulate real-world warehouse and fulfillment center inventory accuracy workflows.
The program demonstrates how inventory metrics are defined, validated, hardened, governed, and presented across multiple analytical layers—mirroring how large-scale retail and e-commerce organizations operationalize trusted inventory data.
The focus is not on dashboards alone, but on metric integrity, grain control, data quality enforcement, and decision-ready reporting.
Accurate inventory is foundational to:
- Fulfillment reliability
- Demand planning accuracy
- Loss prevention
- Operational trust in reporting
ICQA teams require metrics that are:
- Reproducible across systems
- Transparent in calculation logic
- Resistant to data quality defects
- Actionable at both executive and operational levels
This portfolio simulates those requirements using the AdventureWorks dataset as a proxy for enterprise inventory data.
Operational teams face recurring challenges when:
- Inventory accuracy metrics differ between tools
- KPI logic changes silently due to aggregation errors
- Exceptions are hidden within averaged results
- Dashboards emphasize visuals over correctness
These issues create downstream risk in planning, fulfillment, and executive decision-making.
This program delivers a phased, governed analytics solution, progressing from raw data to decision-ready dashboards:
- Excel for early KPI validation and reconciliation
- SQL Server for hardened, reusable KPI logic
- Data Quality checks to enforce metric correctness
- Power BI for executive and operations reporting, built on a controlled semantic model
All deliverables are produced incrementally using Agile-style sprints, with explicit release notes and test evidence.
This project represents the capstone of the program and integrates all prior phases into a production-style analytics solution.
- SKU × Location grain enforced across all calculations
- KPI definitions remain consistent across Excel, SQL, and Power BI
- Data quality exceptions are explicitly surfaced, not hidden
- Separate Executive and Operations views aligned to real decision needs
- Semantic model isolates business logic from visuals
- Weighted Inventory Accuracy %
- High-Risk Exposure %
- Data Quality Exception Rate
- Location-level inventory accuracy
- SKU × Location exception detail for root-cause analysis
Executive Overview
Operations Exception View
📄 PDF Export:
module_4_powerbi_dashboard/Sprint_4_Test_Evidence/ICQA_Inventory_Dashboard_v2.pdf
The program is delivered through structured sprints, each producing reviewable artifacts:
- Environment setup
- Naming conventions
- Repository structure
- Inventory reconciliation
- KPI prototyping
- Early variance analysis
- SKU × Location grain enforcement
- KPI calculation logic
- Aggregation hardening
- Exception detection
- Variance sanity checks
- High-risk flagging logic
- Executive overview dashboard
- Operations exception dashboard
- Governed semantic model
- Dedicated measures table
- Finalized executive and operations dashboards
- Governed semantic model validation
- PDF exports for leadership distribution
- Test evidence and release documentation
- Analytics artifacts packaged for enterprise review
- Microsoft SQL Server (AdventureWorks OLTP & DW)
- Microsoft Excel (KPI validation & reconciliation)
- Power BI Desktop (Semantic model & dashboards)
- GitHub (Version control, sprint artifacts, documentation)
This portfolio is designed for:
- ICQA Data Analysts
- Business Intelligence Developers
- Data Analysts supporting Operations or Supply Chain
- Hiring managers seeking evidence of analytics rigor, not just visuals
Each module folder contains:
- Sprint artifacts
- SQL scripts
- Documentation
- Test evidence
- Release notes
Start with:
module_2_sql_icqa_core_modelmodule_3_data_quality_checksmodule_4_powerbi_dashboard