CPG | Marketing Analytics
Demand Forecasting for Sales Planning
Objective
CPG companies face significant challenges in accurately forecasting demand, which can lead to stockouts or overstock situations. This affects sales planning, inventory management, and overall profitability. A solution is required to enhance demand forecasting accuracy.
Challenges
- High variability in consumer demand.
- Seasonal trends and promotional impacts.
- Data silos and integration issues.
- Limited visibility into real-time sales data.
Solution Proposed
- Utilize Machine Learning algorithms such as ARIMA, LSTM, and Prophet for time-series forecasting to capture complex patterns and seasonality.
- Implement BI tools for data visualization and real-time analytics to provide actionable insights.
- Develop data engineering pipelines to integrate and preprocess data from various sources, ensuring data quality and consistency.
- Innovative use of ensemble learning techniques to combine multiple models for improved accuracy.
Outcome
- Improved forecast accuracy by up to 20%.
- Reduction in stockouts.
- Decrease in excess inventory.
- Enhanced sales planning efficiency and decision-making capabilities.
Design & Thinking Wins
- Seamless integration with existing ERP and CRM systems.
- Scalable architecture to handle large volumes of data.
- User-friendly dashboards for stakeholders.
- Continuous model retraining for adaptive learning.
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.
