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SKU Analysis Tool

Offline warehouse analytics that turns almost any ERP export into actionable inventory intelligence.

MAKEPythonPyQt5Matplotlib

Business Problem

Warehouse analysis is often locked inside ERP exports and analyst-built spreadsheets, leaving managers waiting hours or days for movement, ageing, supplier, fulfilment, and loss insights.

Decision Explored

Can a warehouse manager upload an ERP export and get useful inventory intelligence without an analyst, cloud service, or internet connection?

Approach

Business Logic

The tool prioritises self-service operations analysis: classify movement, surface ageing and dead stock, rank customers and suppliers, expose fulfilment gaps, and quantify inbound losses so teams can act on the current export rather than last month's report.

Technical Implementation

A standalone Python desktop application uses PyQt5, pandas, Matplotlib, and an intelligent synonym/parser layer covering 200+ ERP column variants. Six independent modules provide dashboards, charts, searchable tables, and Excel export, entirely offline.

Prototype

Offline warehouse overview with movement distribution and risk indicators.
Charts for sell-through, category sales, net movement, and coverage.
Loading state while the desktop tool parses and analyses an ERP export.
Order fulfilment analysis with full-rate and customer-level results.
Built-in getting started guide for running an analysis.
Built-in file format guide covering common ERP exports and column synonyms.

Trade-offs

This is intentionally a Windows desktop executable rather than a web app, because warehouse, inventory, and pricing files should stay on the user's machine. The downloadable .exe runs offline and keeps those files local instead of sending them to a cloud service.

Future Work

  • Add purchase-order versus GRN variance analysis
  • Add reorder-point and moving-average demand modules
  • Add multi-file merging and PDF dashboard export