Factory-to-Customer Shipping Route Efficiency Analysis: An Exploratory Case Study of Nassau Candy Distributor

This paper presents an exploratory data analysis of factory-to-customer shipping records for Nassau Candy Distributor. Using Python, Pandas, Plotly, and Streamlit, the project examines sales, shipment volume, order activity, gross profit, gross margin, and recorded order-to-shipment date differences. The study describes a data analysis workflow that includes data inspection, preprocessing, key performance indicator calculation, interactive visualization, and dashboard development. The resulting dashboard provides a way to explore shipment records and business performance indicators. A notable data-quality issue is the unusually large difference between recorded order dates and ship dates. These values require validation before they can be interpreted as actual delivery lead times. The analysis is exploratory and does not claim to establish an experimentally validated optimal shipping route. Keywords: Supply Chain Analytics; Shipping Data Analysis; Logistics; Data Visualization; Python; Pandas; Plotly; Streamlit; Data Quality; Business Intelligence.