CPG | Manufacturing
Production Planning & Forecasting
Objective
The CPG industry faces significant challenges in optimizing labor to meet production demands while minimizing costs. Inefficient labor allocation leads to increased operational expenses, reduced productivity, and missed deadlines.
Challenges
- High variability in customer demand.
- Complex supply chain dynamics.
- External variability in production processes.
- Inaccurate historical data.
- Integration of disparate data sources.
Solution Proposed
- Utilize AI/ML algorithms such as ARIMA, LSTM, and Random Forest with a champion-challenger approach for demand forecasting to capture seasonality and trends.
- Implement BI tools for real-time data visualization and decision-making.
- Deploy engineering solutions for process optimization, model CICD, and drift analysis.
- Innovation: Combining AI/ML with BI allows for dynamic adjustments in production schedules based on real-time data, reducing lead times and improving accuracy.
Outcome
- 20% reduction in inventory holding costs.
- Improved on-time delivery rates.
- Increase in production efficiency.
- Reduction in operational costs.
- Enhanced visibility into production processes.
Design & Thinking Wins
- Successfully integrated AI/ML models with existing ERP systems.
- Developed a custom BI dashboard for real-time monitoring.
- Implemented MLOps, CICD, and Model Maintenance protocols, reducing downtime by 30%.
- Achieved seamless data integration from multiple sources.
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.