Retail | Merchandising & Category Management
Product Rationalization
Objective
The client faced challenges in managing an extensive range of products (SKUs), which led to inefficiencies in inventory management, increased costs, and suboptimal product offerings. The goal was to streamline the product portfolio to enhance profitability and customer satisfaction.
Challenges
- Identifying underperforming SKUs without impacting customer satisfaction.
- Balancing inventory levels to avoid overstocking or stockouts.
- Integrating data from various sources for comprehensive analysis.
Solution Proposed
Utilizing Machine Learning (ML) algorithms like clustering and regression to identify patterns in sales data and customer preferences:
- Clustering algorithms (e.g., K-means) grouped similar SKUs based on sales performance and customer demographics.
- Regression models predict future sales trends and identify potential high-performing SKUs.
- PBI dashboard provided real-time insights for decisions.
- Engineering efforts integrated data from POS systems, inventory databases, and customer feedback platforms.
Outcome
- Reduction in SKU count by 20%, leading to a 15% decrease in inventory holding costs.
- Improved inventory turnover rate.
- Enhanced customer satisfaction scores due to better product availability.
- Increase in overall profitability.
Design & Thinking Wins
- Seamless integration of ML models with existing retail systems.
- Real-time BI dashboards for continuous monitoring and decision-making.
- Scalable solution adaptable to various retail environments.
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.
