Project Overview

The Supply Chain Metrics project is a comprehensive exploration of a Kaggle dataset, showcasing my SQL skills and ability to derive valuable insights. The project is divided into key sections, addressing distinct aspects of supply chain management:

Cost Analysis

I examined manufacturing costs, the relationship between costs and selling prices, and overall product profitability, providing crucial financial insights.

Supply Chain Analysis

I analyzed average lead times for different products, how lead times affect stock levels and availability, and explored correlations between defect rates and inspection results, shedding light on quality control.

Logistics Analysis

My investigation into transportation modes, their impact on lead times and costs, and commonly used routes offers valuable insights for logistics optimization.

Quality Analysis

I determined average defect rates for different product types and assessed correlations between defect rates, inspection results, and manufacturing costs.

Production Analysis

I examined how production volumes relate to stock levels and sales quantities, contributing to inventory management strategies.

Optimization Questions

I explored ways to reduce lead times, examining the transportation modes and routes with the shortest lead times, providing actionable recommendations.

This project showcases my growth as a data analyst, delving deep into supply chain data and extracting meaningful insights using SQL queries. My ability to ask relevant questions, apply SQL functions like aggregation and joins, and offer optimization suggestions demonstrates my analytical and problem-solving skills. This project is a testament to my progress and capability in data analysis.