Solutions of interest include:

• Digital twin platforms for simulating manufacturing workflows
• Machine learning models for soft sensing and real-time process adjustment
• Simulation tools for what-if scenario testing in production environments
• Cross-site performance analytics platforms for process benchmarking

Our must-have requirements are:

• Simulates key manufacturing steps (e.g., mixing, heating, extrusion, drying)
• Simulates how changes in ingredients, equipment, or environmental conditions affect product quality
• Predicts how process parameters affect final product quality (e.g., texture/hardness, moisture/water activity, bulk density/size uniformity, color, and/or nutrition retention)

Our nice-to-have's are:

• Integrates with real-time sensors to predict and adjust process parameters (e.g., moisture, temperature, dosage) dynamically, to improve yield, throughput, and energy efficiency
• Enables cross-site performance comparison to capture best practices and improve efficiency globally
• Integrates energy, water, or resource efficiency metrics into process simulations
• End-to-end digital twin models that simulate entire production lines under different scenarios