Inspiration "Our project, Supermarket Savior, is driven by a dual challenge: significant food waste in supermarkets and widespread hunger. Each year, supermarkets discard vast amounts of perfectly edible food due to various reasons such as overstocking or aesthetic standards. Simultaneously, food insecurity affects millions globally. We were motivated to find a solution that could tackle both issues simultaneously by redirecting surplus food to those in need."
What It Does "Supermarket Savior uses data analytics to identify which food items are likely to be wasted. Our system analyzes sales trends, expiry dates, and inventory levels to predict surplus foods. We then facilitate the distribution of this surplus to food banks and charities through an automated matching platform, ensuring that less food goes to waste and more reaches those who are hungry."
How We Built It "For Supermarket Savior, we leveraged Tableau for its advanced data visualization tools, which allowed us to process and display complex data sets effectively. We complemented Tableau with Python scripts for data cleaning and analysis, automating the workflow to predict food surplus accurately. The platform backend is built with Python and Flask to manage data inputs and outputs seamlessly."
Challenges We Ran Into "Collecting and accessing timely data from supermarkets was our biggest challenge. Many supermarkets were initially hesitant to share data due to privacy concerns. To overcome this, we assured them of data anonymization and demonstrated the societal impact of our project. We also faced technical challenges in integrating various data sources, which we addressed through rigorous testing and iterative improvements."
Accomplishments That We're Proud Of "We are immensely proud of creating a prototype that not only functions efficiently but has already facilitated the redistribution of over 500 kilograms of food in our pilot tests with local supermarkets. The feedback from participating stores and food banks has been overwhelmingly positive, encouraging us to expand our efforts."
What We Learned "This project has been a profound learning experience in applying data science to solve real-world problems. We've enhanced our abilities in data analysis, especially in predictive modeling and real-time data processing. We've also gained invaluable experience in collaborating with diverse stakeholders, from supermarket managers to nonprofit organizations."
What's Next for Supermarket Savior "As for the future, we plan to scale Supermarket Savior by onboarding more supermarkets and expanding our geographic reach. We're exploring the integration of AI to refine our predictive algorithms further and considering developing an app to make our system more accessible to supermarkets and charities alike. Additionally, we're seeking partnerships with national retail chains to amplify the impact of our project."
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