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Development of a Semiautomated Load Planning System for Logistics Operations

yalantis

Logistics

Current Challenges

123 Sourcing faced challenges with manual load planning, leading to increased lead times, frequent errors in delivery planning and container optimization, overstocking, and higher shipping and storage costs. Urgent changes from manufacturers and suppliers further exacerbated these issues.

About the Client

Fourth-party logistics company providing transportation, consolidation center storage, picking & packing, inventory forecasting, order fulfillment, and freight forwarding services to manufacturing plants.

Project Goals

Reduce manual effort in load planning.
Minimize human errors in delivery planning and container allocation.
Decrease shipping and storage costs.
Improve operational efficiency and reduce lead times.
Enhance customer satisfaction and retention.
Enable proactive adjustments to delivery plans based on real-time data.

Functional Requirements

Load planning calculator with feasibility checks.
Container optimization based on weight and volume.
Integration with production plan data (bill of materials, lead times).
Automated recalculation of container requirements upon data changes.
Data import from JSON files.
Alerting for potential delivery issues or container shortages.
Reporting on shipping and storage costs.

Preferred Technologies

Google ORTools (for container optimization)

Multitenant architecture

JSON data handling

Interoperable architectural components

Required Integrations

Existing ERP or Production Planning Systems (via JSON data import).
Potentially Shipping Carrier APIs (for real-time shipping rates and tracking – future phase).

Key Non-Functional Requirements

Scalability to handle increasing data volumes and user load.
Performance – fast calculation times (seconds vs. hours).
Security – data protection and access control.
Reliability – accurate and consistent results.

Expected Business Impact

The implementation of this load planning system is expected to significantly reduce operational costs, improve efficiency, minimize errors, and enhance customer satisfaction. Specifically, it is projected to reduce recalculation times from hours to seconds, eliminate manual feasibility checks, minimize overstocking, and optimize container utilization leading to lower shipping and storage costs. The system will improve delivery planning accuracy, reduce lead times, and strengthen customer relationships.