Logistics Provider Reduces Fleet Costs by Over 50% with AI-Driven Optimization
Artificial Intelligence
The Challenge.
Efficient load building and route optimization are critical for any logistics provider’s growth and scalability. For a rapidly growing SaaS logistics and last-mile provider launched in 2019, manual processes were severely hindering efficiency and customer satisfaction.
Key Challenges:
- Manual Processes and Data Silos:
- Fragmented operations lacked integration, leading to inefficiencies in order management, warehousing, SLA adherence, and reporting.
- The absence of data insights restricted strategic decision-making.
- Inefficient Load and Route Management:
- Trailer space was underutilized, and poorly planned routes resulted in missed delivery deadlines and driver overtime.
- Lack of Real-Time Tracking:
- Customers had no visibility into the delivery process, while retail clients lacked the tracking data they needed.
- Suboptimal Last-Mile Operations:
- Inefficient last-mile delivery processes left packages vulnerable to delays, loss, and theft.
The company realized it needed to modernize its operations with automation and data-driven insights, turning to Pegasus One for a transformative solution.
APPROACH.
A Comprehensive AI-Powered Solution
Pegasus One conducted a deep-dive analysis of the client’s operations, leveraging its logistics optimization expertise to craft a custom API-based system. Built on the LAMP stack (Linux, Apache, MySQL, PHP), the solution employed machine learning algorithms and Google OR-Tools for advanced optimization.
Steps Taken:
- Legacy Data Integration:
- Trained the system using historical data to develop a robust model for optimizing logistics workflows.
- AI-Driven Route Optimization:
- Developed algorithms to calculate optimal delivery routes, considering constraints like weight limits, deadlines, and traffic conditions.
- Automated Packing Solutions:
- Implemented a packing model to maximize trailer utilization by analyzing object dimensions and bin capacities.
- Dynamic Scheduling:
- Enabled automated scheduling to balance complex task requirements with available resources, factoring in breaks and driver downtime.
- Enhanced User Interfaces:
- Provided a role-based, app-based tracking system for managers, drivers, and customers, delivering real-time insights and complete transparency.
Solution Highlights:
- Real-time tracking and ETA prediction.
- Flexible delivery scheduling with automated allocation.
- Dynamic, real-time route optimization powered by ML.
BENEFITS.
The solution revolutionized the client’s operations, delivering significant improvements across key performance metrics:
- Operational Efficiencies:
- Automated workflows reduced manual effort and increased productivity.
- Real-time data visibility empowered informed decision-making around resources and inventory.
- Route Optimization:
- Reduced time on the road with smarter routing, cutting fuel consumption and fleet maintenance costs by over 50%.
- Shrunk delivery turnaround times, driving fleet productivity up by 40%.
- Driver Morale and Safety:
- Stress and fatigue were mitigated through optimized schedules incorporating rest periods.
- Safer driving conditions and shorter routes boosted morale and job satisfaction.
- Enhanced Customer Experience:
- Real-time tracking improved transparency and trust.
- Increased first-time delivery success rates by 30%, reducing theft and loss incidents.
RESULTS.
Key Achievements:
- Reduced fleet maintenance costs: Over 50% savings.
- Increased delivery capacity: Boosted deliveries by more than 40%.
- Improved delivery efficiency: Achieved a 40% improvement in route and load management.
- Enhanced customer experience: Elevated customer satisfaction with accurate ETAs and reliable tracking.
IMPACT.
By partnering with Pegasus One, the client was able to:
- Transition from manual processes to a fully optimized, automated system.
- Achieve scalable growth while reducing operational overhead.
- Drive customer loyalty and competitive differentiation through better service delivery.
