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

Category: Supply Chain

Services: Supply Chain Automation, Demand Forecasting, Inventory Management Solutions, Workflow Optimization, Real-time Monitoring and Tracking.

  • Achieved 95% accuracy in ETA predictions.
  • Maintained 99% email delivery rate via SES.
  • Handled 50% more API requests, no performance loss.

Northbound(an MVP factory company)

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.

Problem statement

  • 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  • The backend system failed to fetch truck locations and calculate ETAs at regular intervals, resulting in outdated information and logistical challenges.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.

Proposed Solution & architecture

  • 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.

Metrics for success

  • 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.

Architecture diagram


AWS Services

  • 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.

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