Java Projects - Retail Domain

Intelligent Inventory Management System

The Intelligent Inventory Management System was designed to solve critical challenges in retail inventory management, primarily addressing stockouts, overstock situations, and inefficient replenishment processes across retail stores and warehouses.

The system serves multiple user groups: store managers who need real-time visibility into stock levels, warehouse operators managing fulfillment, procurement teams handling vendor relationships, and finance teams monitoring inventory investment.

The core architecture employs event-driven design where every inventory movement (sales, returns, transfers, receipts) triggers events through Kafka, enabling real-time inventory position updates.

Store-level transactions from POS systems flow through REST APIs, while bulk warehouse movements are processed through batch interfaces using Spring Batch.

The system implements smart allocation logic that considers factors like store performance, historical sales patterns, and local events to optimize stock distribution.

Machine learning models continuously analyze sales patterns, seasonal trends, and external factors (weather, local events, promotions) to generate automated replenishment recommendations.

A React-based dashboard provides customized views for different user roles - store managers see store-specific inventory metrics and alerts, warehouse managers view cross-location transfer recommendations, and procurement teams access vendor performance metrics and reorder suggestions.

The system integrates with vendor portals through EDI interfaces for automated purchase order generation and tracking. Real-time alerts notify relevant stakeholders about potential stockouts, overstock situations, or unusual inventory movements.

The platform includes a returns management module that tracks return reasons and automatically adjusts future procurement recommendations.

All inventory movements are tracked with full audit trails in PostgreSQL, while MongoDB stores real-time inventory snapshots and analytical data.

The system's impact includes reduced manual intervention in replenishment processes, improved stock availability, reduced working capital through optimized inventory levels, and enhanced vendor collaboration through automated procurement workflows.


Omnichannel Customer Experience Platform

The Omnichannel Customer Experience Platform was developed to bridge the gap between online and offline retail experiences, addressing the fundamental challenge of providing seamless customer journeys across multiple shopping channels.

The platform serves various stakeholders: customers shopping across web, mobile, and physical stores; store associates accessing customer profiles and order histories; customer service representatives handling cross-channel inquiries; and marketing teams managing personalized campaigns.

The system's core functionality revolves around a unified customer profile that aggregates interactions across all touchpoints - when a customer starts their journey on the mobile app, continues on the website, and completes the purchase in-store, all interactions are captured and linked. The order management system maintains a single source of truth for all orders, enabling features like buy-online-pickup-in-store (BOPIS), ship-from-store, and cross-channel returns.

Real-time inventory visibility across channels ensures customers and store associates can accurately view product availability, with store-level inventory updates streaming through Kafka to maintain consistency.

The platform implements a sophisticated cart persistence mechanism allowing customers to maintain their shopping cart across devices and channels, with offline capabilities in the mobile app for continued shopping without internet connectivity.

A unified promotion engine ensures consistent application of discounts and offers across channels, while the recommendation system uses cross-channel purchase history to provide personalized product suggestions.

Store associates access a tablet-based application that provides customer insights, purchase history, and preferences, enabling personalized in-store service.

The customer service module gives representatives a complete view of customer interactions across all channels, streamlining issue resolution. Marketing teams can create and monitor omnichannel campaigns through a unified dashboard, with real-time analytics showing campaign performance across different touchpoints.

The system integrates with physical store systems like POS and digital displays, creating a cohesive experience where online browsing history influences in-store product recommendations.

A loyalty program spans all channels, allowing points earning and redemption regardless of purchase channel, while maintaining a single customer view that drives personalization across the entire shopping journey.

Smart POS and Payment Integration System

The Smart POS and Payment Integration System was designed to modernize traditional point-of-sale operations while incorporating advanced payment processing capabilities and real-time analytics.

The system handles complex payment workflows across multiple retail locations, operating with high availability and fault tolerance.

Built on Spring Boot microservices architecture, separate services manage payment processing, inventory updates, customer loyalty, and analytics.

Each transaction triggers events through Kafka, enabling real-time synchronization across all connected systems.

The payment processing module supports diverse payment methods including contactless payments, mobile wallets, cryptocurrency, and traditional card payments, with transaction data securely encrypted using AWS KMS.

A crucial feature is the offline processing capability, where transactions are queued locally using IndexedDB when internet connectivity is lost, then automatically synchronized once connection is restored.

The frontend, built with React and TypeScript, provides an intuitive interface for cashiers while offering advanced features like split payments, partial refunds, and dynamic currency conversion.

Real-time fraud detection is implemented using machine learning models that analyze transaction patterns and flag suspicious activities instantly.

The system integrates with multiple payment gateways through a custom abstraction layer, allowing seamless failover between providers to ensure maximum payment success rates.

Customer data is stored in MongoDB for flexible schema evolution, while transactional data uses PostgreSQL for ACID compliance.

The loyalty program integration allows real-time point calculation and redemption, with personalized offers generated based on purchase history.

Performance monitoring using Grafana and Prometheus provides instant visibility into system health, while Splunk handles log aggregation for troubleshooting.

The system achieved significant improvements in transaction processing time and increased payment success rates through intelligent routing and retry mechanisms.

Security compliance is maintained through regular penetration testing and automated security scans in the CI/CD pipeline using Jenkins, with deployments managed through Kubernetes for scalability and reliability.


Supply Chain Optimization Platform

I worked on the development of a Supply Chain Optimization Platform that revolutionized inventory management and distribution networks.

The system was built using a microservices architecture with Spring Boot as the foundation, incorporating event-driven patterns through Kafka for real-time inventory updates and order processing.

The backend services were developed in Java and Python, with Python specifically handling machine learning components for demand forecasting and route optimization.

We implemented GraphQL APIs to provide flexible data querying capabilities, allowing clients to request specific data combinations while minimizing network overhead.

The frontend was developed using Angular, featuring interactive dashboards and real-time visualization of supply chain metrics. Data persistence was handled through a hybrid approach - PostgreSQL for transactional data and MongoDB for storing unstructured supplier and logistics data.

The system runs on AWS, utilizing EKS for container orchestration, with automatic scaling based on demand patterns. We implemented blue-green deployments through Jenkins pipelines, ensuring zero-downtime updates.

Real-time monitoring was set up using Grafana and Prometheus, with custom alerts for inventory thresholds and delivery delays.

A key feature was the implementation of smart contracts using blockchain technology for supplier agreements and payment automation.

The system includes sophisticated algorithms for inventory optimization, considering factors like seasonal demands, supplier lead times, and transportation costs.

We integrated with external weather APIs and economic indicators to enhance forecast accuracy.

The platform features real-time tracking of shipments through IoT device integration, providing accurate ETAs and condition monitoring of sensitive goods.

Security was implemented through multiple layers, including OAuth2 for authentication, role-based access control, and encryption of sensitive data both in transit and at rest.

The system demonstrated significant improvements in inventory turnover rates and reduction in stockouts while optimizing transportation costs through intelligent route planning and load consolidation.

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Retail Analytics and Business Intelligence Platform

I led the development of a comprehensive Retail Analytics and Business Intelligence Platform at Target, which transformed raw retail data into actionable business insights.

The platform processes transactions across stores nationwide, utilizing a microservices architecture built with Spring Boot.

We implemented real-time data ingestion using Kafka streams, processing point-of-sale data, inventory movements, and customer behavior patterns.

The backend services were developed using Java for core business logic and Python for advanced analytics and machine learning models that predict customer churn and product demand.

We created a flexible GraphQL API layer that allowed business users to query complex data relationships efficiently, reducing the need for multiple API calls.

The frontend was built using React with TypeScript, featuring interactive dashboards with drill-down capabilities and real-time visualizations using D3.js.

For data storage, we implemented a polyglot persistence strategy - using PostgreSQL for transactional data, MongoDB for customer behavior analysis, and Redis for caching frequently accessed metrics.

The entire platform runs on a hybrid cloud infrastructure spanning AWS and Azure, utilizing Kubernetes for container orchestration and automatic scaling.

We implemented a sophisticated CI/CD pipeline using Jenkins, incorporating automated testing with JUnit and Mockito.

Real-time monitoring was established using Dynatrace for application performance monitoring and Splunk for log aggregation and analysis.

A key innovation was our implementation of real-time personalization engine that processes customer interaction data to generate targeted promotions and recommendations.

The platform includes advanced features like anomaly detection in sales patterns, inventory optimization algorithms, and predictive analytics for store performance.

Security was a top priority, implementing end-to-end encryption, robust authentication using OAuth2, and comprehensive audit logging.

The system demonstrated significant business impact, reducing reporting time and increasing promotional campaign effectiveness through data-driven decision making.


Dynamic Pricing and Promotion Engine

Currently at Amazon, I'm leading the development of a Dynamic Pricing and Promotion Engine that adjusts product prices and promotions in real-time based on market conditions, competitor pricing, and customer behavior.

The system is built using a microservices architecture with Spring Boot handling the core business logic, while Node.js manages real-time price updates and notifications.

The backend implements complex pricing algorithms written in Python, which analyze market trends and customer purchase patterns.

We use Kafka for event streaming, processing millions of price updates and market signals.

The data is stored across PostgreSQL for transactional data and MongoDB for storing customer behavior and market analytics.

The frontend is developed using React with TypeScript, featuring interactive dashboards for business analysts to monitor pricing strategies and promotion effectiveness.

We implemented GraphQL APIs to efficiently handle complex pricing queries and updates, significantly reducing the backend calls needed for price calculations.

The entire system runs on AWS, using EKS for container orchestration, with automatic scaling handled by Kubernetes. Our CI/CD pipeline uses Jenkins with automated testing through JUnit and Mockito, ensuring robust code quality.

We use Dynatrace and Grafana for real-time monitoring of system performance and pricing accuracy. A particularly challenging aspect was implementing real-time competitor price monitoring and automated price adjustments while ensuring system stability.

The system includes sophisticated features like dynamic bundle pricing, personalized promotions, and markdown optimization. Security is maintained through strict access controls, encryption, and comprehensive audit logging of all price changes.

Currently, I'm working on enhancing the system with machine learning capabilities to better predict optimal price points and promotion timing.


Store Operations Management System

At Target, I led the development of a comprehensive Store Operations Management System that streamlined operations across their retail locations nationwide.

The system was architected using microservices with Spring Boot as the core framework, handling inventory management, staff scheduling, and store performance metrics.

We implemented real-time inventory tracking using Kafka streams to process events from point-of-sale systems, warehouse movements, and online orders.

The backend services were developed in Java with specific data processing modules in Python for predictive analytics.

We created a unified GraphQL API layer that consolidated data from multiple microservices, improving overall system performance and reducing API complexity.

The frontend was built using Angular with TypeScript, featuring responsive dashboards for store managers and regional directors to monitor operations in real-time.

For data persistence, we used PostgreSQL for transactional data and MongoDB for storing operational analytics and real-time metrics.

The platform runs on a hybrid cloud infrastructure using AWS for core services and Azure for backup and disaster recovery.

We implemented container orchestration using Kubernetes, which improved system scalability and deployment efficiency.

The CI/CD pipeline was built using Jenkins, with automated testing using JUnit and Mockito. Real-time monitoring was implemented using Splunk for log analysis and Dynatrace for performance monitoring.

A key innovation was our implementation of an AI-powered staff scheduling system that optimizes workforce allocation based on historical store traffic patterns and sales data.

The system includes advanced features like automated inventory reordering, real-time store performance comparisons, and predictive maintenance alerts for store equipment. We also implemented a sophisticated role-based access control system using OAuth2 and JWT tokens.

This system has significantly improved operational efficiency, particularly in areas of inventory management and staff scheduling optimization.


Customer Loyalty and Personalization Platform

Currently at Amazon, I'm leading the development of a Customer Loyalty and Personalization Platform that enhances customer engagement across their retail ecosystem.

The system is built using a microservices architecture with Spring Boot backend services handling customer profiles, loyalty points, rewards management, and personalized recommendations.

We use Kafka for event streaming to process customer interactions, purchase history, and browsing behavior in real-time.

The data processing pipeline combines Java microservices for core business logic with Python services for machine learning-based recommendation engines.

We implemented a hybrid API approach using both RESTful services and GraphQL, allowing flexible data querying for different client needs.

The frontend is developed in React with TypeScript, providing a seamless user experience across web and mobile platforms. Data storage utilizes PostgreSQL for transactional data like customer profiles and points, while MongoDB stores behavioral data and recommendation models.

The entire platform is deployed on AWS, using services like ECS, Lambda, and S3, with Kubernetes handling container orchestration.

We maintain high availability through multi-region deployment and implement circuit breakers using Spring Cloud.

The CI/CD pipeline uses Jenkins with automated testing through JUnit and Mockito, while monitoring is handled through a combination of Splunk for logging, Dynatrace for performance monitoring, and Grafana for metrics visualization.

A notable feature is our real-time personalization engine that adapts to customer behavior patterns and updates recommendations dynamically.

We implemented sophisticated caching strategies using Redis to optimize performance and reduce database load.

Security is managed through OAuth2 with JWT tokens, implementing role-based access control and ensuring GDPR compliance.

The system integrates with multiple external services through APIs, handling promotion engines, payment processors, and email marketing systems.

This platform has transformed how Amazon engages with customers, delivering personalized experiences while maintaining system performance and scalability.


Digital Shelf and Product Information Management

In my current role at Sam’s Club, I'm leading the development of a Digital Shelf and Product Information Management (PIM) system that handles millions of product listings across multiple marketplaces.

The platform is built using a sophisticated microservices architecture powered by Spring Boot, with specialized services handling product data enrichment, digital asset management, and catalog synchronization.

We implemented an event-driven architecture using Kafka to manage real-time product updates and inventory synchronization across different channels.

The system processes complex product hierarchies and relationships using a combination of PostgreSQL for structured product data and MongoDB for flexible attribute management and rich content.

Our API layer utilizes GraphQL for complex product queries and RESTful services for simpler CRUD operations, both built with Spring WebFlux for reactive processing.

The frontend is developed using React with TypeScript, featuring a dynamic product editor, bulk update capabilities, and real-time validation.

We've implemented an intelligent content enrichment pipeline using Python and Natural Language Processing to automatically enhance product descriptions and attributes.

The entire system runs on AWS, utilizing services like ECS, S3 for digital asset storage, and ElasticSearch for advanced product search capabilities.

We use Kubernetes for container orchestration and service scaling, particularly crucial during high-traffic periods like Prime Day.

Monitoring is handled through a comprehensive stack of Splunk for log analysis, Dynatrace for performance monitoring, and Grafana with Prometheus for metrics visualization.

One of our key innovations was implementing a machine learning-based quality scoring system that automatically evaluates product content completeness and suggests improvements.

The CI/CD pipeline uses Jenkins with extensive automated testing through JUnit and Mockito, ensuring code quality and system reliability.

We've achieved significant performance improvements through implementation of caching strategies and database optimization, reducing product update latency from minutes to seconds.

The system handles over 100 million SKUs while maintaining sub-second response times for product queries, thanks to our optimized indexing and caching strategies.