CPG | Merchandising
Assortment Optimization
Objective
CPG companies often face challenges in maximizing revenue and profitability through data-driven SKU rationalization and portfolio management. Organizations are looking to optimize product mix across different channels and markets to drive efficient inventory management and enhance customer satisfaction.
Challenges
- Complex demand transference patterns.
- Local market variations, shelf space constraints, and seasonal demand fluctuations.
- Understanding Cannibalization effects.
- Supply chain limitations.
- Category role conflicts.
- Store format variations.
- Inventory holding costs.
- Product lifecycle management.
Solution Proposed
- Implement Mixed Integer Linear Programming (MILP), Association Rule Mining, and Deep Learning models for demand forecasting and optimization.
- The solution aims to develop an SKU rationalization engine, demand forecasting module, space optimization system, cannibalization analyzer, and store clustering framework.
- The innovation lies in combining traditional optimization techniques with Machine Learning to create a dynamic assortment planning system that adapts to changing market conditions and considers demand transference.
Outcome
- 40% improvement in CLV prediction accuracy.
- Increase in customer retention rates.
- Growth in the high-value customer segment.
- Reduction in customer churn.
- Better allocation of marketing resources.
- Increase in average customer spend.
Design & Thinking Wins
- Real-time optimization engine.
- Performance tracking dashboard.
- Automated planogram generator.
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.
