The problem Dukaan.AI solves

Problem Statement:

In retail, optimizing shelf space directly impacts product visibility, customer experience, and sales.
Develop a tool that optimizes product allocation to limited shelf space considering factors like popularity, seasonality, and profit margins.

Product Popularity Algorithm:
Assess product popularity based on historical sales, customer reviews, and real-time trends.
Utilize machine learning and sentiment analysis for accurate predictions.

Seasonal Variation Analysis:
Identify and adapt to seasonal changes in demand using predictive models.

Profit Margin Optimization:
Prioritize products with higher profit margins for prime shelf space.
Create an intuitive dashboard displaying key metrics and recommendations.
Allow retailers to experiment with shelf layouts using drag-and-drop functionality.

Augmented Reality Shelf Simulation:
Visualize different shelf layouts in-store using augmented reality technology.
Predict customer traffic and visibility impact for each layout.

Predictive Stock Replenishment:
Detect out-of-stock products in real-time and generate replenishment orders.
AI Retail Assistant, helps you as a data analyst and one stop solution for all our business development
queries eg : what product i should place on my first shelf to maximize profitability

Smart Retail AI Customer Engagement Analytics:
Integrate customer engagement metrics for targeted product marketing.

Challenges I ran into

Low-Light Product Detection: Overcoming the challenge of accurately detecting products on shelves in low light conditions to ensure real-time stock updates are reliable and timely.

Apriori Algorithm for Product Recommendations: Implementing the Apriori algorithm for efficient and relevant product recommendations, balancing computational efficiency with the complexity of real-world retail data.

Dynamic Stock Management in Real-Time: Developing a robust system for dynamic stock management that can handle sudden changes in inventory, predict stock depletion, and automate replenishment orders, all in real-time.