Logistics Operator – Java Integration, Routing & SLA Control
Background
The partner is a logistics operator with an internal platform for managing deliveries, warehouses, and routes. The team is distributed and follows a Scrum methodology. Before our engagement, development was handled entirely in-house. As the business scaled, the need arose to expand into new regions, integrate SLA controls into the existing planning module, and reduce the number of erroneous requests from business users.
Solution
We provided a Java engineer proficient in Spring Boot, PostgreSQL, and Apache Kafka. They integrated seamlessly into the team: quickly got up to speed on the technical context, aligned priorities with the Product Owner, and started delivering changes with no prolonged onboarding delays.
Deliverables
• Routing: The routing model was redesigned to account for delivery time windows, weight, dimensions, and warehouse constraints. Business rules were moved to a dedicated layer, routes are now calculated deterministically, and extension points have been added to support new regions and trip types.
• SLA Control:The planner now explicitly respects target time windows and priorities. If a route violates SLA constraints, the planner suggests alternatives. Deviations are captured both at the service level and in the UI.
• Validation & Business Rules: Server-side validation was added on the backend for addresses, time slots, statuses, and conflicts. The UI now receives clear, actionable error messages. Invalid requests are blocked before trip execution.
• Event Processing & Cargo Tracking: Kafka is now used for buffering and guaranteed processing, with order-based keys and partitioning, retries and backoff, and dedicated DLQ topics. Consumer services were rewritten to be idempotent.
• Core Module & API: Heavy operations were moved to background jobs. Reference and geodata are now cached in Redis. PostgreSQL performance was improved with proper indexes and query optimization. Quotas and rate limiting were added to public API endpoints.
• Engineering Hygiene: Static analysis and parallel checks were added to the CI pipeline. Short ADRs (Architecture Decision Records) were introduced to keep architectural context transparent for the entire team.
Outcome & Impact
The planner now respects SLA by default. Routes are generated predictably, and invalid requests are rejected at the entry point. Deployments are smoother, and the engineering team spends more time moving the product forward rather than fixing the consequences of ad‑hoc changes. Following this engagement, the partner expanded the collaboration and requested an additional Java specialist from ML Web Solutions.
The partner is a logistics operator with an internal platform for managing deliveries, warehouses, and routes. The team is distributed and follows a Scrum methodology. Before our engagement, development was handled entirely in-house. As the business scaled, the need arose to expand into new regions, integrate SLA controls into the existing planning module, and reduce the number of erroneous requests from business users.
Solution
We provided a Java engineer proficient in Spring Boot, PostgreSQL, and Apache Kafka. They integrated seamlessly into the team: quickly got up to speed on the technical context, aligned priorities with the Product Owner, and started delivering changes with no prolonged onboarding delays.
Deliverables
• Routing: The routing model was redesigned to account for delivery time windows, weight, dimensions, and warehouse constraints. Business rules were moved to a dedicated layer, routes are now calculated deterministically, and extension points have been added to support new regions and trip types.
• SLA Control:The planner now explicitly respects target time windows and priorities. If a route violates SLA constraints, the planner suggests alternatives. Deviations are captured both at the service level and in the UI.
• Validation & Business Rules: Server-side validation was added on the backend for addresses, time slots, statuses, and conflicts. The UI now receives clear, actionable error messages. Invalid requests are blocked before trip execution.
• Event Processing & Cargo Tracking: Kafka is now used for buffering and guaranteed processing, with order-based keys and partitioning, retries and backoff, and dedicated DLQ topics. Consumer services were rewritten to be idempotent.
• Core Module & API: Heavy operations were moved to background jobs. Reference and geodata are now cached in Redis. PostgreSQL performance was improved with proper indexes and query optimization. Quotas and rate limiting were added to public API endpoints.
• Engineering Hygiene: Static analysis and parallel checks were added to the CI pipeline. Short ADRs (Architecture Decision Records) were introduced to keep architectural context transparent for the entire team.
Outcome & Impact
The planner now respects SLA by default. Routes are generated predictably, and invalid requests are rejected at the entry point. Deployments are smoother, and the engineering team spends more time moving the product forward rather than fixing the consequences of ad‑hoc changes. Following this engagement, the partner expanded the collaboration and requested an additional Java specialist from ML Web Solutions.