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
Manual Forecasting
The reliance on manual data entry for demand forecasting created significant inefficiencies and inconsistencies across regional warehouses. The system couldn't effectively scale to support the expanding product line and growing operational complexity.
Inventory Management
Inefficient stock management led to frequent overstocking and significant waste of perishable goods. The lack of accurate forecasting tools resulted in elevated inventory costs and resource wastage.
Vendor Management
Relationship-based vendor selection processes resulted in unreliable delivery schedules and increased stockouts of high-demand items. The absence of data-driven vendor evaluation impacted supply chain reliability.
Vendor Management
Relationship-based vendor selection processes resulted in unreliable delivery schedules and increased stockouts of high-demand items. The absence of data-driven vendor evaluation impacted supply chain reliability.
Dynamic Demand Adaptation
The system failed to account for demand fluctuations during festivals, sports events, and seasonal changes. This limitation resulted in significant inventory misalignment during peak periods.
Real-Time Analytics
Warehouse managers lacked critical insights into stock consumption patterns and product trends. This visibility gap led to suboptimal shelf space utilization and delayed response to changing consumer preferences.
We designed an AI-powered unified dashboard to automate demand forecasting, streamline vendor selection, and optimize inventory management. This end-to-end solution leveraged machine learning algorithms, real-time data analysis, and predictive modeling, providing warehouse managers with an intuitive interface to manage inventory, make re-order decisions, and predict demand effectively.
Intelligent Demand Forecasting
We developed and deployed an advanced forecasting engine using a combination of ARIMA, Prophet, and LSTM neural networks. The system processes historical sales data, seasonality patterns, and external events to generate accurate demand predictions, eliminating manual forecasting efforts.
Automated Vendor Management
We implemented a sophisticated vendor scoring system that evaluates suppliers based on key performance metrics. The solution automatically ranks vendors based on pricing, lead times, and reliability scores, enabling data-driven supplier selection and optimization.
Real-Time Analytics Engine
Our team built a comprehensive analytics platform that monitors stock burn rates and inventory movements in real-time. By implementing predictive analytics algorithms, the system automatically identifies declining demand patterns and suggests proactive inventory adjustments.
Smart Event Integration
We integrated external data feeds to create an event-aware forecasting system. The platform automatically adjusts inventory predictions based on upcoming festivals, weather forecasts, and sporting events, ensuring optimal stock levels during peak periods.
Centralized Management Dashboard
To tie everything together, we developed a unified control center that provides warehouse managers with instant access to all critical metrics. This centralized platform streamlines decision-making processes and simplifies inventory management workflows.
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 stockouts
Reduction in manual operations
Improvement in vendor reliability
Increase in peak period sales
Faster inventory turnover
This project highlights the power of AI and machine learning in transforming inventory and vendor management for retail chains. By combining real-time data, predictive modeling, and an intuitive dashboard, we delivered a solution that empowers the client to efficiently manage inventory and meet demand in a dynamic market environment.
Learn how we worked with clients to overcome their challenges and created the best solutions and experiences in these case studies.
