This project seeks to optimize the distribution of fast-moving consumer goods (FMCGs) to informal retailers by improving delivery efficiency. The goal is to implement a dynamic routing strategy that minimizes travel distances and maximizes vehicle utilization. By leveraging data-driven clustering techniques, this project aims to enhance logistics operations, reduce costs, and streamline deliveries.
Efficient distribution is crucial for FMCG supply chains, where timely delivery directly impacts profitability and customer satisfaction. The current challenge is ensuring that delivery trucks are optimally utilized while minimizing transportation costs. The dataset contains geographical coordinates of retail shops and fulfillment centers, allowing for spatial clustering.
Company X aims to optimize FMCG deliveries by clustering retail shops around fulfillment centers to create efficient delivery routes. The objective is to develop a data-driven clustering model that minimizes travel distances, improves vehicle utilization, and reduces logistics costs while ensuring timely order fulfillment.
The is the main deliverable. It contains the details of the approach taken and the methodology. It contains data cleaning, exploratory data analysis, data preparation for modelling and building the classification models.