Project: Loka.ai/Members: Quan Nguyen, Duc Nguyen, Long Bui

I. Motivations

While small businesses represent 44% of the U.S. GDP, 99% of all firms, and have generated 63% of new jobs over the last decade, they face challenges in leveraging data-driven decision-making to enhance profitability comparing larger enterprises. This may be due to the lack of resources and experience necessary to implement data-driven technologies and invest in research and development.

A platform that could help small firms to optimize their financial resosurces and understand of market dynamics has become more urgent.

II. Solution Overview

Given our limited resources and time, we aim to focus on a specific area that best showcases the potential of this approach:

An innovative approach to identify the ideal locations for small-scale food and beverage enterprises.

Most optimized location. What it demonstrates ?

• Customer demographics
• Potential Markets
• Rental & operational costs
• Competitor presence

Focusing first on F&B industry. Why F&B ?

• 99.9% of businesses in F&B industry are small firms.
• 56% of F&B business owners struggle with managing operational costs and profitability
• 52% cite economic uncertainty as a major hurdleeconomic uncertainty as a significant hurdle.
• The U.S. packaged food market is projected to reach US$1.6 trillion by 2030

III. Milestones & Challenges

• Pull APIs from different data sources (GoogleMapAPI, Census Bureau, NY Open Data)
• Integrate pulled data into MongoDB
• Utilize React to construct web interface
• Optimize Flask to build server
• Develop a machine learning model that captures essential parameters using Cerebas API to rank most optimized locations
• Leverage PropelAuthority to authorize user management
• Design, reorganize, and visualize metrics into meaningful and understandable insights

IV. Takeaways

Pros

• Operational and Functional: The app is fully operational, allowing users to access its features seamlessly, enabling immediate utilization for business enhancement.

• Showcases Essential Capabilities: It effectively demonstrates the necessary skills and functionalities, providing valuable insights for optimizing processes and decision-making.

• Room for Improvements: The project shows solid foundation that could potentially develop new features and continous improvability in the future.

Cons

• Potential Model Biases: Some biases have been identified in the model, which may affect accuracy. Further testing and fine-tuning are needed to improve reliability.

• Capacity Limitations: The app currently has restrictions on handling larger datasets and accommodating more users, necessitating enhancements for scalability.

• Data Dependencies: Fetching data from different sources and does not have inernal data may lead to disrupt the system if sources collasped.

V. Future Enhancements

Model Improvements:

1. Market Equilibrium Adjustment: Implement dynamic Supply & Demand balancing algorithms to reflect real-time market conditions.
2. Logistics Analysis Implementation: Incorporate advanced logistics modeling to optimize supply chain operations and reduce costs.
3. Data Backup Sources: Utilize redundant cloud storage, regular snapshots, and blockchain technology for secure and reliable data backups.
4. Features Added: Introduce machine learning price predictions, real-time sentiment analysis, customizable alerts, and interactive visualizations.

Platform Integrations:

1. Market Data Providers: Establish connections with providers like Bloomberg for comprehensive market data feeds.
2. Integration with CRM Systems: Connect with platforms like Microsoft Business Central (launched in 2021 to serve small business's data solutions) to retreive more accurate financial data.