Amusanolabisi/Agricultural-Supply-Chain-Performance-Analysis

An end-to-end data analysis project exploring revenue, post-harvest losses, transport costs, and profitability across crops, states, and seasons in Nigeria's agricultural supply chain — built with SQL, Python, Excel, and Power BI. 🌾 Agricultural Supply Chain Performance Analysis An end-to-end data analysis project exploring revenue, post-harvest losses, transport costs, and profitability across crops, states, and seasons in Nigeria's agricultural supply chain — built with SQL, Python, Excel, and Power BI. 📌 Project Overview This project analyzes agricultural supply chain data covering ₦3.15 trillion in total revenue across multiple Nigerian states, crops, and seasons. The goal was to identify where value is being lost in the supply chain — from farm to market — and present findings in a way that supports smarter decisions for agribusinesses and cooperatives. 🔍 Key Insights 💰 Total revenue reached ₦3.15T but post-harvest losses averaged 18.50% — a massive value leak in the chain 🚛 Transport costs hit ₦2bn, pointing to serious logistical inefficiencies across states 📍 Benue and Niger lead all states in revenue generation 🌱 Groundnut records the highest post-harvest losses by crop 🌦️ Performance varies significantly across Dry, Off, and Wet seasons — key intelligence for storage and distribution planning ✨ Special Feature — Custom Tooltip The Power BI dashboard includes a custom tooltip that displays farmer-level detail on hover — including Farmer ID, crop, quantity harvested, and net profit — without leaving the main dashboard view. This was intentionally designed to improve the user experience and make the dashboard more intuitive for stakeholders. 🛠️ Tools Used SQL- Data extraction and querying Python- Data cleaning and exploratory analysis Excel- Data validation and modelling Power BI- Dashboard, visualization, and custom tooltip 📁 Repository Structure agric-supply-chain-analysis/ ├── agric_supply_chain_queries.sql # SQL queries for data extraction ├── agric_supply_chain_analysis.ipynb # Python notebook for EDA and cleaning ├── agric_supply_chain_dashboard.png # Power BI dashboard screenshot └── README.md # Project documentation …