One of India’s largest retail conglomerates, operating an extensive national distribution network, relied on a high-volume central warehouse as the backbone of its logistics operations.
This warehouse — with 24 active docking stations — handled 24×7 truck arrivals for loading and unloading, servicing hundreds of retail outlets daily.
However, as operations scaled, the company struggled to efficiently manage the high volume of truck traffic. The first-in, first-out (FIFO) docking method led to longer waiting times, underutilized docks, and growing inefficiencies that directly impacted delivery timelines and operational costs.
The company partnered with TDWS Consulting Group to develop a custom-built intelligent dock management system designed to streamline warehouse operations, reduce idle time, and maximize throughput.
The Challenge
The existing warehouse operation model was reactive, not optimized. With limited data visibility and manual scheduling, operational efficiency plateaued despite continuous manpower and process improvements.
Key challenges included:
- Inefficient Dock Scheduling: Dock assignment was done purely on arrival order, ignoring cargo type, urgency, or proximity of goods.
- High Truck Idle Time: Long queues formed during peak hours, causing unnecessary waiting and demurrage charges.
- Manual Coordination: Communication between transport, warehouse, and drivers depended on phone calls and paperwork.
- No Real-Time Insight: Supervisors lacked visibility into dock status, truck progress, or estimated turnaround times.
- Poor Spatial Optimization: Forklifts and staff often traveled longer distances to load/unload goods from distant docks.
It was clear that the company needed a data-driven, algorithmic approach to dock scheduling — one that could dynamically assign trucks to optimal docks based on real-time warehouse conditions.
Solutions Delivered
TDWS Consulting Group designed and developed a custom enterprise-grade application built on the Java Technology Stack, tailored to the retailer’s warehouse operations.
The solution leveraged rule-based logic and predictive algorithms to plan, schedule, and optimize truck docking in real time.
Key solution highlights included:
- Smart Dock Scheduling Engine A proprietary algorithm assessed each incoming truck’s load profile, unloading priority, SKU location, and current dock occupancy to allocate the most efficient dock automatically.
- Real-Time Operations Dashboard Supervisors gained full visibility into dock activity — occupied bays, pending arrivals, estimated completion times, and load priorities — through an intuitive dashboard interface.
- Dynamic Reallocation System The system could automatically reschedule trucks if delays occurred or if priority shipments arrived unexpectedly, ensuring continuous optimization.
- Workforce and Equipment Sync The application coordinated forklift and staff allocation in line with truck docking schedules, balancing workload and minimizing idle time.
- Predictive Analytics Historical data on truck arrivals, unloading times, and SKU movements were analyzed to predict congestion patterns and optimize future slot planning.
- Driver Communication Integration Dock allocations and timing updates were directly communicated to drivers via mobile notifications, reducing manual coordination and delays.
Results Delivered
Within 15 months of deployment, the custom dock management system transformed the warehouse into a data-driven, high-efficiency logistics hub.
33%
increase in daily truck throughput
Over
₹100 crore
annual cost savings
42%
reduction in average docking time
Key measurable outcomes included:
- 33% increase in daily truck throughput, achieved without expanding physical dock capacity.
- Over ₹100 crore annual cost savings, driven by reduced waiting time and improved manpower utilization.
- 42% reduction in average docking time, enabling faster turnaround and continuous throughput.
- Automated scheduling and reallocation, eliminating human dependency in dock assignments.
- Real-time analytics and performance insights, empowering management with data for strategic decision-making.
- Scalable and modular system architecture, ready for integration with ERP, IoT sensors, and predictive logistics platforms.
Technology Stack
- Backend: Java Spring Boot
- Frontend: Angular Framework
- Database: PostgreSQL
- Integration Layer: REST APIs for ERP and Fleet Systems
- Analytics: Custom Predictive Scheduling Engine with Data Lake Integration
- Hosting: Deployed on Private Cloud (Kubernetes-based)
Conclusion
By leveraging the power of custom-built enterprise software, TDWS Consulting Group helped one of India’s largest retailers transform its warehouse operations — shifting from manual FIFO scheduling to an intelligent, predictive, and fully automated docking system.
The result:
- Reduced turnaround time,
- Increased truck throughput by one-third, and
- Delivered measurable annual savings exceeding ₹100 crore.
This project stands as a testament to how strategic technology intervention — powered by the Java stack and intelligent scheduling algorithms — can redefine logistics efficiency at scale.
