Optimising Delivery Operations with AI-Powered Auto Trip Creation System
INTECH partnered with a retail company to revolutionize their delivery operations through an AI-powered automated trip planning system.
Our AI-powered solution turned a slow and inefficient delivery process into an efficient system that not only saved delivery time but also maximizes vehicle capacity utilization.
The client is a prominent supermarket and retail chain known for its wide range of home utility products and competitive pricing.
The business model relies heavily on logistics to meet the growing demands of an extensive network of fulfillment centers and delivery points.
Delivery time plays a crucial role in their commitment to provide high quality experience to customers.
The client faced significant operational challenges in their delivery logistics.
They wanted to ensure products were delivered on time from fulfillment centers to various delivery points.
But in absence of a robust fulfillment system, they faced the following challenges:
1. Manual Planning Inefficiencies:
Time-consuming manual trip planning led to suboptimal route creation and resource allocation.
2. Capacity Underutilization:
Lack of automated grouping and optimization resulted in inefficient use of delivery vehicle capacity.
3. Delivery Delays:
Manual intervention in trip planning caused delays and inconsistencies in delivery schedules.
4. Resource Management:
Complex coordination between fulfillment centers and delivery points created operational bottlenecks.
5. Scalability Limitations:
The manual system couldn’t effectively handle growing delivery volumes or expand its service areas.
INTECH designed and implemented an AI-powered Auto Trip Creation System.
The solution automated their previously manual and time-intensive trip planning process while ensuring seamless optimization of delivery routes and resource utilization.
Using advanced machine learning algorithms and data point clustering the system intelligently grouped shipments, calculated optimal routes, and maximized vehicle capacity.
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Smart Clustering: AI algorithms grouped shipments based on proximity, delivery time slots, and fulfillment mode, ensuring logical and efficient delivery clusters.
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Route Optimization: Machine learning algorithms calculated optimal delivery paths, minimizing transit times and fuel consumption.
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Dual Delivery Modes: The system efficiently handled both home delivery and pickup point fulfillment, adapting seamlessly to client’s requirements.
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Capacity Optimization: Vehicles were intelligently loaded to achieve trip ranges of 25–30 deliveries, maximizing resource utilization.
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Real-Time Processing: Advanced algorithms with O(n²) time complexity ensured rapid trip planning and updates, even during peak operational hours.
Step 1: Core Optimization Goals
The machine learning approach focused on achieving three primary optimization goals:
- Minimizing radial distance between shipments in a single trip
- Reducing the radius of each trip
- Maximizing trip capacity while minimizing the total number of trips
Step 2: Agglomerative Hierarchical Clustering Methods
To achieve these goals, several clustering methods were implemented:
- Single Linkage Method: Defines the distance between clusters as the minimum distance between any two points within the clusters. It merges clusters with the smallest single linkage distance at each stage, with a time complexity of O(n²).
- Complete Linkage Method: Uses the maximum distance between points in clusters as the distance metric and combines clusters based on the smallest complete linkage distance, with a time complexity of O(n²logn).
- Average Linkage Method: Calculates the average distance between all data points in two clusters and merges them based on the smallest average linkage distance, with a time complexity of O(n²logn).
- Centroid Method: Measures the distance between the cluster mean vectors and merges clusters with the smallest centroid distance. This method focuses on overall cluster positioning rather than individual points.
Step 3: Technical Implementation
The system was designed for real-time operation using an unsupervised learning approach, ensuring minimal processing time and computational complexity.
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Replacing manual processes with AI-driven decision-making.
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Increasing operational efficiencies, resulting in significant cost savings.
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Enabling the client to manage higher delivery volumes and support growth.
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Optimizing routing and capacity for faster, more accurate deliveries.
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Improving delivery timelines and reliability, enhancing customer satisfaction and brand loyalty.
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Python: For core development machine learning, data analytics, data visualization, and programing applications.
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Apache Kafka: For real-time data processing and event handling for seamless system operations.
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REST API: To provide smooth integration with client’s existing systems, facilitating efficient communication across platforms.
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Flask: Used as a lightweight, scalable deployment framework to deliver the application.
