CPG | Manufacturing
Inventory Management
Objective
CPG companies aim to optimize their inventory management processes to reduce excess stock, minimize stockouts, and improve overall supply chain efficiency. They need a solution that can forecast demand accurately, streamline inventory allocation, and enhance production planning.
Challenges
- Inaccurate demand forecasting leads to overstocking or stockouts.
- Complex supply chain with multiple geographies and product lines.
- Manual and time-consuming inventory management processes.
- Difficulty in integrating various data sources for comprehensive analysis.
Solution Proposed
- The solution employs a combination of Machine Learning algorithms, including Random Forest for demand forecasting.
- Deep Q-Networks for inventory policy optimization and Gradient Boosting for lead time prediction.
- Dynamic inventory policy adjustments and what-if scenario analysis.
- The engineering module includes ETL pipelines for data integration, microservices architecture for scalability, and API endpoints for seamless integration with existing ERP systems.
Outcome
- 25% reduction in overall inventory carrying costs.
- Improvement in inventory turnover ratio.
- Significant service level achievement.
- Decrease in stockouts.
- Reduction in excess and obsolete inventory.
- Improvement in demand forecast accuracy.
- Faster response to supply chain disruptions.
Design & Thinking Wins
- Real-time inventory visibility across the entire supply chain.
- Automated reorder point and safety stock calculations.
- Dynamic inventory policy adjustments based on market conditions.
- Predictive analytics for potential stockouts and overstock situations.
- Integration with IoT sensors for real-time inventory tracking.
- Multi-echelon optimization considering interdependencies between inventory locations.
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.
