CPG | Sales Planning
Lost Opportunity Analysis for Sales Planning
Objective
CPG companies often face challenges in accurately forecasting sales and demand, leading to lost opportunities due to stockouts or overstocking. There is an ardent need to provide a comprehensive solution for optimizing sales planning and minimizing lost opportunities.
Challenges
- CPG companies often face challenges in accurately forecasting sales and demand, leading to lost opportunities due to stockouts or overstocking.
- There is an ardent need to provide a comprehensive solution for optimizing sales planning and minimizing lost opportunities.
Solution Proposed
Algorithms
- Advanced time series forecasting techniques such as ARIMAX and LSTM for demand forecasting and Machine Learning ensemble models for product style-level forecasting.
- Data Lake Platform, Cloud Advisory, Assessment and Migration, ML Ops, and DevOps Infrastructure Management, and Governance.
- The solution leverages deep learning and Machine Learning models to improve forecasting accuracy from the existing 60-65% to 85-90%. Automating the framework helps reduce execution downtime, enabling quicker insight delivery and faster solution deployment.
Outcome
- Increase in forecasting accuracy from existing 60-65% to 85-90%.
- Lesser stockouts due to better inventory and supply chain management.
- Automation of the framework helped reduce execution downtime.
Design & Thinking Wins
- Consolidation of shopper, market, and syndicated datasets from multiple sources.
- Demand transference for identification and replacement of poor-performing SKUs.
- Effectiveness assessment of new assortment over existing assortment.
- SKU rationalization for assortment recommendation.
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.
