ACME Case Study

This project is a sales forecasting and optimization platform for Acme. It leverages a synthetic dataset that models Acme’s hierarchical structure—Portfolio, Geography, Category, Brand, and Segment—with realistic market constraints at each level.

Key Features

• Sales Forecasting & Optimization: The core of the platform is its ability to predict future sales and optimize for various business goals.
• Hierarchical Data Modeling: It uses a synthetic dataset that accurately reflects Acme’s complex structure, including Portfolio, Geography, Category, Brand, and Segment.
• Multi-Scenario Analysis: The algorithm can run multiple scenarios, such as maximizing sales, maximizing margins, or achieving specific targets, allowing for flexible strategic planning.
• Long-Term Projections: The platform projects outcomes over a five-year period, providing a long-term view for decision-making.
• Actionable Insights: The structured output provides clear, actionable insights to help leadership set realistic growth expectations and strategic priorities.

Technical Implementation

The project is built using Python and a suite of data science libraries.

• Pandas and NumPy are used for data manipulation and numerical calculations.
• SciPy and Pulp are used for the optimization algorithms.
• Matplotlib is used for generating visualizations of the forecasts and scenarios.