AI-Driven Yield Optimization in a Corn Processing Plant

The Business & The Challenge

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A corn processing facility faced challenges in yield predictability and overall production efficiency, specifically across its core outputs: gluten, oil, and starch. The facility struggled with:

• Low and unpredictable yield performance
• Limited visibility into key process variables affecting output
• Lack of an analytical framework to pinpoint improvement opportunities

The key objective:
Improve site yield and predictability by identifying critical input-output relationships using AI and prescriptive analytics.

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Approach & Modeling Steps

Falconi implemented an AI-powered optimization approach, involving four core phases:

1. Problem & Data Discovery

• Process mapping and variable prioritization
• Data acquisition and database structuring

2. Model Development

• Exploration of historical variables (e.g., oil content, corn quality)
• Developed predictive and prescriptive models using a hybrid AI ensemble:
   
  • Support Vector Machine (SVM)
   
  • Artificial Neural Network (ANN)

3. Model Operations &Delivery

• Identified optimal operation points per production scenario
• Delivered site-specific playbooks and rollout plans for scale
• Defined governance for ongoing model usage and oversight

Results Achieved

Metric                                                                      Result

Explained Variance (Oil  Yield)                    79%

Mean Error in Weekly Oil  Yield                   0.17 percentage points

Estimated Financial Gain  (Annually)       Up to $1.74M

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✅ Identified high-impact variables to optimize yield across starch, oil, and gluten
✅ Enabled operational tuning with scenario-specific yield projections
✅ Supported predictive control of plant outputs for strategic resource allocation

📈 Example Output – Yield Projection Model

Why It Worked

• Combined robust data acquisition with cutting-edge machine learning     models
• Focused on variable prioritization and simulation, not just retrospective     analytics
• Delivered playbooks and operational guidance that empowered staff to act

📘 Conclusion

This project demonstrates how Falconi’s analytics expertise helped a corn processing plant optimize yields across multiple production lines. By leveraging AI to simulate optimal operating conditions, Falconi delivered measurable gains in predictability, efficiency, and bottom-line financial returns.

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