Product Line Profitability & Margin Performance Analysis for Nassau Candy Distributor

Abstract

This research paper presents a descriptive analysis of product-level and division-level profitability using the Nassau Candy Distributor dataset. The study examines sales, cost, gross profit, and gross margin to identify products and divisions with different profitability characteristics. The analysis uses Python, Pandas, NumPy, Plotly, and Streamlit for data validation, aggregation, visualization, and interactive dashboard development. Data consistency checks compare recorded gross profit with the difference between sales and cost. Product-level and division-level summaries are used to examine profitability performance. The findings identify products with the highest and lowest total gross profit and gross margin, as well as differences in profitability across the Chocolate, Other, and Sugar divisions. The study is descriptive and does not claim to predict future performance or establish causal relationships. The work demonstrates how data analytics and interactive dashboards can support exploratory business profitability analysis. Results should be interpreted in the context of the dataset's coverage and limitations.