Northbound: A dynamic engine for real-time warehouse and transport coordination

Northbound(an MVP factory company) is a warehouse management system that tracks live truck locations that are planned to onboard/offboard goods in the warehouse to better plan resources.

• The previous warehouse management system lacked the ability to track the real-time location of trucks in transit, resulting in resource allocation inefficiencies.
• The system did not provide accurate estimated time of arrival (ETA) for trucks arriving at their destinations, which hindered effective logistics planning.
• The backend system failed to fetch truck locations and calculate ETAs at regular intervals, resulting in outdated information and logistical challenges.
• The web application anticipated varying levels of traffic, necessitating a solution that could adapt and optimize operations accordingly.
• Given the cost-sensitive nature of the logistics industry, there was a critical need to minimize infrastructure expenses while enhancing supply chain efficiency.

• We utilized the AWS Location service to enable precise ETA calculations between two geographical coordinates.
• Simform implemented a solution for consistent ETA calculations by leveraging AWS EventBridge Schedules. These schedules triggered Lambda functions, ensuring regular ETA updates.
• For the serverless REST API, Simform employed a comprehensive AWS service stack. This included using SQS for message queuing, Lambda functions for serverless compute tasks, and SES for efficient email communication. This approach enhanced API efficiency and scalability.
• Simform selected Amazon Aurora, a high-performance relational database service, to store and manage data effectively. This decision ensured reliable storage and access to logistics and ETA-related data as required.

• Achieved an ETA accuracy rate of 95% or higher, ensuring that calculated arrival times closely match actual arrivals.
• Achieve a 99% email delivery rate for notifications and updates sent through SES, ensuring effective and reliable communication with stakeholders.
• Demonstrate the ability to handle a 50% increase in the number of simultaneous API requests during peak periods without degradation in performance.

• Amazon Cognito: Our team employed AWS Cognito for user authentication and access token management.
• Amazon API Gateway: We utilized AWS API Gateway to route API calls, perform authentication checks, and direct requests to Lambda functions.
• AWS SAM: The entire serverless backend was developed, tested, debugged, and deployed using AWS SAM.
• Amazon Location Service: Amazon Location Service was responsible for calculating ETAs and routes, optimizing logistics.
• Amazon EventBridge: AWS EventBridge was used as a centralized scheduler for backend cron-tasks.
• Amazon Aurora: AWS Aurora with PostgreSQL ensure data reliability and recovery in our solution.
• Amazon CloudFront: AWS CloudFront was used as a CDN for delivering static content globally by caching it in different AZs.
• Amazon S3: S3 was used to store all static content, including photos of warehouses and various documents.
• Amazon CloudWatch: AWS CloudWatch closely monitored logs across different services and Lambda functions for debugging and tracing.
• Amazon SES: AWS SES was used to send transactional emails about scheduling, re-scheduling, and appointment cancellations.
• Amazon SQS: AWS SQS served as a message broker for asynchronous communication between services and handling unprocessed messages in a DLQ.