Our client, a budding groceries delivery company in India, relies on a fleet of trucks and small vehicles for procuring goods from suppliers and transporting items between warehouses and stores. However, the client faced recurring issues related to fleet breakdowns, inefficient route planning, and dock congestion. These problems impacted their ability to maintain smooth supply chain operations, increased operational costs, and led to inconsistent delivery times, especially for temperature-sensitive items.
Client Name
A Leading Supermarket Chain in India
Industry
Retail & Logistics
Country
India
The client’s original sportsbook platform faced several key issues
Fleet Maintenance
Frequent vehicle breakdowns and reactive maintenance approaches severely disrupted the supply chain operations and delivery schedules. The lack of predictive maintenance systems led to escalating repair costs and significant logistics delays.
Route Efficiency
Manual route planning resulted in excessive fuel consumption and extended delivery times, particularly impacting perishable goods transportation. The absence of real-time optimization tools compromised delivery efficiency and operational costs.
Dock Management
Uncoordinated dock scheduling created severe congestion during peak hours at warehouse facilities. This bottleneck caused substantial delays in loading and unloading operations, affecting the entire supply chain performance.
Our team implemented a unified, AI-powered logistics dashboard to address each of these pain points using advanced data analytics and machine learning techniques. The solution included
Predictive Maintenance Model
Using historical breakdown and servicing data, we developed a predictive maintenance model trained on various truck health metrics (e.g., engine temperature, fuel efficiency, mileage). This model leverages machine learning techniques to forecast potential breakdowns based on real-time data from sensors installed in the trucks.
AI-Driven Route Optimization
We used reinforcement learning to optimize routes based on real-time traffic, road conditions, and product requirements (e.g., cold storage for perishable goods). The system considers multiple constraints, including distance, travel time, fuel consumption, and the need for time-sensitive delivery.
Intelligent Dock Scheduling Model
We implemented a scheduling model based on queueing theory and priority-based algorithms that manages dock usage at warehouses. The model considers historical data on truck arrivals/departures, loading/unloading times, and dock availability to predict peak usage periods and allocate docks efficiently.
Unified Real-Time Dashboard
We developed a unified, intuitive dashboard that consolidates data from predictive maintenance, route optimization, and dock scheduling modules. The dashboard provides live tracking of fleet health, route status, and dock availability in a single view.
Our team achieved success which reflected growth, engagement, and satisfaction. Our team led to exceptional outcomes focusing on growing revenue and client base, elevating user satisfaction, and much more.
Reduction in Truck Breakdowns
Reduction in Overall Logistics Costs
Increase in Dock Utilization Efficiency
Improvement in Delivery Times for Perishable Goods
Improved Operational Control and Decision-Making
This case study demonstrates our ability to leverage AI and machine learning to solve complex logistics challenges in high-stakes, high-volume environments. By addressing critical pain points through predictive maintenance, route optimization, and dock scheduling, we delivered a robust, scalable logistics management platform that provides measurable value. This solution is highly applicable to industries such as retail, FMCG, and distribution, where fleet efficiency and real-time tracking are paramount to operational success
Learn how we worked with clients to overcome their challenges and created the best solutions and experiences in these case studies.
