Field Service Route Optimization: How NextBillion.ai Helped Health-Tech Firm Improve Sample Collection Schedules and Achieve 25% Cost Savings

About the Company

Our customer, a health-tech logistics disruptor, accelerates high-speed sample collection and logistics services for diagnostics providers. They focus on efficient sample management processes to improve the accessibility and affordability of high-quality diagnostics.

Explore their journey with NextBillion.ai’s route optimization solution for field service operations.

The Problem

The company faced complex logistical challenges in managing sample collections from various locations with specified time windows.

The challenges unfolded as follows:

• Multi-stop route planning: Samples collected during visits were consolidated and delivered to a single lab thrice daily. Managing 1600+ stops for multi-stop route planning posed a challenge due to the complex geographical spread of their operations.
• Time flexibility and aggregation: Scheduled visits had the flexibility of +/- 15 minutes, and samples collected by their field service agents needed aggregation at common meeting points during specific time ranges.
• Optimization objectives: They aimed to minimize the number of riders, reduce travel distance and increase the number of visits per rider, all within a rider’s work day of around 9 hours.

Our Solution

NextBillion.ai developed a customizable route optimization solution tailored to the customer’s field service operation needs. The solution comprised:

Clustering API

• Strategically clustered endpoints, creating localized meeting points at the conclusion of each shift.
• Facilitated a streamlined handover process by intelligently grouping samples at common meeting points, enhancing operational efficiency.

Route Optimization API

• Identified the optimal multi-stop routes and number of vehicles needed to fulfill orders efficiently.
• Created optimized routes based on time windows, minimizing riders and travel distance.
• Maximized the number of visits per rider.
• Efficiently handled the hard constraint of service time per location using the API’s capabilities.
• Additionally, we incorporated +/- 15-minute flexibility for scheduled visit/arrival times, leveraging the soft constraint capabilities.

The Outcome

NextBillion.ai’s tailored route optimization solution for field service operations successfully addressed the company’s challenges, providing a cost-effective, efficient and flexible approach to field service management and sample collection route planning.

The solution’s adaptability to specific constraints showcased its ability to meet diverse requirements in the dynamic healthcare logistics landscape.
The health-tech company was able to:

• Achieve 25% cost savings by minimizing the number of riders and travel distances.
• Intelligently group sample collection tasks for faster pick-ups and deliveries, reducing turnaround times.
• Maximize fleet utilization by allowing for 35% more visits per rider.
• Lower conveyance expenses by 25%.
• Adapt to dynamic routing constraints, including service time per location and varied arrival times.