Esmail Arshad
All work

Independent Project · Supply Chain Analytics · 2026

Supply Chain Workbench

Built a supply-chain workbench that calculates reorder needs, flags freight exceptions and scores supplier performance from CSV data, with AI-generated action summaries.

Inventory Planning · Freight Analytics · Supplier Performance

Live Demo GitHub

Workflows
3 reorder planning · freight exceptions · supplier scoring
Data input
Flexible CSV automatic + manual field mapping
Processing
Local calculations run in-browser · no database
Supply Chain Workbench Inventory Planner showing summary KPIs, inventory status, reorder recommendations and an AI-generated Action Summary.
Inventory Planner — Sample data with inventory status, reorder recommendations and an AI-generated Action Summary.

What I built

Supply Chain Workbench combines three recurring analyses in one interface: inventory planning, freight exception analysis and supplier performance scoring. Each workflow calculates results from uploaded CSV data; DeepSeek generates an Action Summary from those results when requested.

Three workflows

Inventory Planner

Calculates days of cover, lead-time demand, reorder points, inventory position and MOQ-rounded order recommendations.

Freight Exceptions

Compares cost per mile with the lane average and flags shipments that are high-cost, late or both.

Supplier Scorecard

Scores supplier delivery, quality, cost and service with configurable weights. Missing required data is marked Incomplete rather than forced into a score.

How it works

Upload CSV → map fields → analyze → review results → export CSV or generate Action Summary

You do not need exact header names. Common variants are matched automatically—for example, Stock On Hand, on_hand and Quantity On Hand all resolve to the same field. If a required field cannot be matched, select it manually before analysis. Unused columns are ignored.

Build approach

  • Development. I used Claude and Codex for implementation, iteration and QA, while defining the supply-chain rules and expected outputs explicitly and validating them against known results.
  • Calculation logic. Inventory, freight and supplier calculations are defined in code rather than generated by a language model.
  • Local processing. Uploaded CSV data is parsed and analyzed in the browser. No database is required and session data is not persisted.
  • Action Summary. DeepSeek generates a short summary from already-calculated structured results when requested. Raw uploaded CSV files are not sent to the AI endpoint.

All work