franckdjandja/african-logistics-performance-optimization

Supply Chain Analytics Project analyzing logistics delays, shipping cost efficiency, and operational performance across African logistics networks using R and Tableau. # African Logistics Performance Optimization ## Project Overview Efficient logistics systems are essential for economic development and trade growth across African markets. However, logistics operations often face challenges including delivery delays, infrastructure limitations, and cost inefficiencies. This project analyzes logistics performance across multiple African countries using data analytics and visualization techniques to identify operational bottlenecks and propose optimization strategies. The analysis combines **R for data analysis** and **Tableau for interactive visualization**, enabling strategic insights for logistics performance improvement. --- # Business Context Supply chain efficiency is a critical competitive advantage for logistics operators across emerging markets. African logistics networks face structural challenges including: - long transportation distances - infrastructure constraints - variable delivery reliability - high operational costs Improving logistics efficiency can significantly enhance delivery performance, customer satisfaction, and operational profitability. --- # Business Problem How can data analytics identify inefficiencies in logistics operations and support data-driven decision making to improve supply chain performance across African markets? --- # Dataset The dataset contains **5000 simulated logistics shipments across African logistics networks**. Each record represents a shipment transaction including operational and financial attributes. ### Key Variables - Country - City - Warehouse - Transport Mode - Distance (km) - Delivery Time (days) - Expected Delivery Time - Shipping Cost - Order Value - Order Date Dataset location in repository: data/logistics_dataset.csv --- # Analytical Methodology The analysis follows a structured data analytics workflow. ## 1 Data Preparation Performed using **R** Key steps: - Data cleaning - Delivery delay calculation - Feature transformation - Data formatting …