Manufacturing | Demand & Production Planning
Production Planning & Forecasting in the Manufacturing Industry
Objective
Manufacturing companies face significant challenges in optimizing production schedules and accurately forecasting demand. Inefficient production planning increases operational costs, inventory imbalances, and missed delivery deadlines.
Challenges
- High variability in customer demand.
- Complex supply chain dynamics.
- External variability into production processes.
- Inaccurate historical data.
- Integration of disparate data sources.
Solution Proposed
- Utilization of AI/ML algorithms such as ARIMA, LSTM, and Random Forest champion-challenger approach in demand forecasting to capture seasonality and trends.
- Implement BI tools for real-time data visualization and decision-making.
- Deploy engineering solutions for process optimization and 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.
- Incremental improvement in 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.
Back to ManufacturingDisclaimer: 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.
