Logistics Network Efficiency
The Problem
A food delivery network in Bangladesh was facing high rider turnover, leading to missed SLAs, delayed deliveries, and constant operational instability. Riders frequently dropped out due to long travel distances, inconsistent earnings, and inefficient allocation — creating a cycle of churn and poor service quality.
Overview
The network relied on a centralized dispatch model that did not account for rider convenience or locality.
Instead of optimizing for system efficiency alone, the challenge was to redesign operations in a way that aligned rider incentives with delivery performance — without introducing heavy technology.
Client Context
- Client: Regional food delivery network
- Region: Bangladesh
- Focus: Last-mile logistics, rider operations, dispatch optimization
The Challenge
- High rider churn: Frequent drop-offs due to poor earning consistency
- Long-distance assignments: Riders traveling inefficient routes
- Missed SLAs: Delays caused by poor allocation logic
- Operational overhead: Constant need to onboard and manage new riders
The system needed to improve retention and delivery performance without relying on complex technology or automation.
The Solution
A low-tech, system-first operational redesign was implemented:
1. Decentralized Hub Model
- Divided delivery zones into smaller, localized hubs
- Assigned riders to specific zones to reduce travel distance
2. Localized Rider Allocation
- Prioritized assigning orders within a rider’s nearest operational zone
- Increased delivery success rates through familiarity and proximity
3. Rider-Centric Workflow Design
- Structured operations to ensure shorter trips and more consistent earnings
- Improved predictability of work and reduced fatigue
4. Zero-Tech Execution Layer
- Implemented using simple operational rules and coordination systems
- Avoided reliance on complex dispatch algorithms or new infrastructure
Results
- 15% reduction in fuel consumption by shifting last-mile delivery from motorbikes to bicycles within localized zones
- 22% improvement in ETA accuracy driven by assigning riders familiar with their delivery areas
- Improved SLA adherence during peak hours through decentralized hub operations
- Higher rider retention due to shorter trips and more predictable earnings
Key Takeaways
- Operational design can outperform technology when incentives are aligned
- Reducing complexity often leads to better scalability
- Rider experience is directly linked to system performance
- Local optimization can significantly improve last-mile efficiency
