Stellar Stock Navigator

Inspired by the meticulous resource planning required for interstellar voyages in 'Interstellar' and the price fluctuations observed in 'E-Commerce Pricing' scrapers, 'Stellar Stock Navigator' is an inventory management system designed for small businesses with fluctuating or seasonal demand. The core concept draws from the novel 'Nightfall' by hypothesizing that certain external factors (akin to the 'darkness' in the story) can significantly impact consumer behavior and thus demand for specific products. The system will function by:

1. Data Ingestion: Users input historical sales data, product costs, and lead times. The system can optionally scrape publicly available sales data from competitor websites (ethically and within terms of service) or other relevant market indicators to identify trending products or price shifts (akin to the e-commerce scraper).
2. Predictive Modeling: Using simple statistical models (e.g., time series analysis, moving averages, regression), the system will forecast demand for individual SKUs. It will also incorporate user-defined 'event triggers' – analogous to 'Nightfall's' celestial phenomena – such as upcoming holidays, local events, or even macro-economic indicators that could influence purchasing habits.
3. Dynamic Stock Allocation: Based on the demand predictions and user-defined safety stock levels, the system will recommend optimal reorder points and quantities. It will highlight potential overstock or understock situations before they become critical, much like a ship captain needing to conserve or procure resources for a long journey.
4. Cost Optimization: The system will provide insights into potential cost savings by suggesting bulk purchase opportunities when demand is predicted to be high, or by recommending clearance sales for items with predicted low demand to avoid holding costs. This mirrors the careful economic considerations of resource management in space exploration.

This project is niche as it targets businesses that struggle with demand forecasting beyond simple historical averages, particularly those susceptible to external market influences. It's easy to implement as it can start with basic Python scripting and readily available libraries, and can be deployed as a web application with minimal infrastructure. The low-cost aspect is inherent in using open-source tools and cloud hosting options. The high earning potential lies in offering this as a SaaS (Software as a Service) solution, with tiered pricing based on the number of SKUs managed and the complexity of the predictive models used, or as a consultancy service to help businesses implement and customize the system.