Yield Prediction

Accurate Predictions for Consistent Output.

Yield prediction involves using AI to forecast product yield based on real-time and historical data, such as raw material quality, environmental conditions, and equipment performance. AI models identify factors influencing yield variability and suggest proactive measures to maximize output. This ensures consistency in production, reduces material waste, and helps avoid costly disruptions.

How to Do It?

1. Collect historical production data, including raw material quality and process conditions.
2. Use AI algorithms to correlate input variables with product yield outcomes.
3. Implement predictive models to provide real-time yield forecasts.
4. Adjust process parameters based on AI predictions to optimize production.

Benefits:

• Reduces yield variability across production batches.
• Identifies potential yield bottlenecks early, enabling proactive adjustments.
• Improves resource planning by predicting output volumes accurately.
• Reduces waste and optimizes raw material usage.

Risks and Pitfalls:

• Yield predictions rely heavily on data quality; inaccurate input data may produce flawed forecasts.
• Predictive models may require frequent updates to reflect changing conditions.
• Over-reliance on predictions without human oversight can introduce risks if unforeseen variables occur.

Example:
Dow Chemical’s Predictive Analytics for Yield Optimization
Dow Chemical employs AI to predict and optimize product yields across multiple manufacturing sites. The company implemented a predictive analytics platform to monitor process conditions and forecast yield for key products, such as polyethylene. By analyzing historical data and real-time inputs, Dow’s AI system identifies trends impacting yield, such as variations in raw material quality or temperature fluctuations. The system enables operators to make proactive adjustments, minimizing variability between production runs. This approach resulted in a 10% increase in average yield and a noticeable reduction in production costs.

Remember:
AI-driven yield prediction empowers chemical manufacturers to maintain consistency, maximize output, and reduce waste, ultimately leading to better profitability and operational efficiency.

Note: For more Use Cases in Chemical Manufacturing, please visit https://www.kognition.info/industry_sector_use_cases/ai-use-cases-in-chemical-manufacturing/

For AI Use Cases spanning functional areas and sectors visit https://www.kognition.info/functional-use-cases-for-enterprises/