Retail | Supply Chain, Logistics & Transportation
Inventory Replenishment Framework
Objective
The client faced frequent stockouts and overstock situations, leading to lost sales and increased holding costs. The current inventory management system could not accurately predict demand or optimize replenishment schedules, resulting in inefficiencies and customer dissatisfaction.
Challenges
- High variability in customer demand.
- Inaccurate demand estimations.
- Suboptimal replenishment schedules.
- High holding and stockout costs.
- Limited integration of real-time data.
Solution Proposed
- Implemented AI/ML algorithms such as ARIMA / LSTM for accurate demand forecasting while identifying trends & seasonality.
- Deployed Reinforcement Learning (RL) for dynamic inventory replenishment optimization.
- Utilized Business Intelligence (BI) tools for real-time data analytics and visualization.
- Implemented engineering services to integrate & unify data from numerous data sources, including data from IoT sensors for real-time inventory tracking.
Outcome
- 30% reduction in stockouts.
- Decrease in holding costs, enhancing inventory management efficiency.
- Boost in sales due to better product availability.
- Improved customer satisfaction and loyalty.
- Enhanced decision-making through real-time data insights.
Design & Thinking Wins
- Seamless integration with existing ERP systems.
- Scalable architecture to accommodate future growth.
- User-friendly dashboards for easy monitoring and decision-making.
- Automated alerts for low stock levels and replenishment needs.
- Customizable algorithms to adapt to different product categories and seasons.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
