Project Overview: The foundation of this project is a custom-architected dataset simulating the complete business operations of Valentis Motors, a mock EV manufacturer. To provide a realistic analytical environment, the dataset encompasses corporate profiles, departmental structures, plant and supplier information, and comprehensive financial statements. This structured data serves as the single source of truth for the entire analytical suite and is available for download on this page.

The Business Problem: Strategic finance often operates in silos. Budgeting can be subjective, forecasting disconnected from external market forces, and competitor analysis a time-consuming manual task. This suite was designed to solve these issues by creating a single, cohesive analytics engine.

My Solution: I developed a multi-faceted tool that combines three powerful modules that work together.

First, a Predictive Cash Flow Forecasting Model, which generates a predictive forecast system using Python (Prophet, Pandas) that integrates internal financial data with external economic indicators such as Economic Activity Index, Consumer Confidence Index, Interest Rate Proxy, EV Component Cost Index. This model also allows scenario modelling and can incorporate current economic factors such as "Tariffs" to generate robust cash flow forecasts and run "what-if" scenarios.

Second, a Intelligent Budget Optimizer uses Python (PuLP) to recommend allocations that maximize ROI. The model is powered by historic departmental performance data(company asesses ROI on previous years investments) from a custom SQL database and utilizes forecast estimates from "Predictive Cash Flow Forecasting" as future budget(for the 2025 year in this project) and constraints.

Finally, an AI-Enhanced Competitor Benchmarking Tool automates data collection (BeautifulSoup) and generates qualitative insights using an LLM API. This component is a work in progress, will post any updates as soon as I can! Key outputs are visualized below.