AI-Integrated Production Planning for Batch-Based Manufacturing

Desired outcome

EMO FRITE d.o.o. aims to explore the development of an AI-integrated production planning system to optimize our batch-based manufacturing process. Our production involves a wide variety of products with different compositions, properties, and colors, requiring frequent equipment cleaning and complex scheduling to align with delivery deadlines and workforce availability. The goal of this project is to analyze the feasibility of AI-driven planning, identify key data requirements, and evaluate how such a system could enhance efficiency, reduce downtime, and improve overall production flow.

Initial Problem Description

Our production process consists of multiple stages:
1. Raw Material Delivery & Preparation – Receiving and weighing raw materials according to product recipes.
2. Mixing & Smelting – Melting materials in furnaces at up to 1550°C.
3. Cooling & Drying – Rapid cooling in water, followed by drying in a centrifuge to form frit.
4. Milling & Packaging – Weighing frit and additional materials, milling in ball mills, and packaging the final product.

Context

With such a diverse product range, transitioning between batches requires frequent machinery cleaning, particularly for mills and furnaces. Some transitions require dedicated cleaning batches to prevent contamination. Planning the optimal sequence of production to minimize downtime while meeting delivery deadlines is highly complex. Although we have archived data on process times and quality outcomes from various batch transitions, we need a structured approach to leverage this data for intelligent scheduling.

Connection to cross-cutting areas

1. Industry 4.0 & Smart Manufacturing
AI improves scheduling, automation, and real-time decision-making, making production more efficient.
2. Sustainability & Resource Efficiency
Optimized planning reduces waste, energy use, and unnecessary cleaning batches, lowering environmental impact.
3. Supply Chain & Workforce Management
AI helps balance raw material supply, machine usage, and worker availability, improving overall efficiency.
4. Cost Reduction & Process Optimization
Better planning reduces downtime and unexpected delays, cutting costs and increasing productivity.
5. Flexibility & Scalability
AI can adapt to different product recipes and grow with production demands, ensuring long-term efficiency.

Input

We seek international student teams with expertise in AI, data analytics, industrial engineering, and production management. This project will involve:
• Reviewing existing AI applications in manufacturing planning.
• Analyzing our historical production data to identify patterns and optimization opportunities.
• Defining the key data points required for AI integration.
• Outlining a roadmap for developing and implementing an AI-powered planning system.

Expectations

1. Assess AI Feasibility: Investigate how AI can be applied to production planning, including machine learning models for scheduling optimization.
2. Data Collection Strategy: Identify key data points needed for AI-driven planning, such as process times, cleaning requirements, batch transitions, and worker availability.
3. Production Sequence Optimization: Analyze strategies to group similar products together to minimize cleaning time and resource waste.
4. Integration with Existing Systems: Explore how an AI model could integrate with our current manufacturing and inventory management processes.
5. Performance Benchmarking: Define key metrics (e.g., production efficiency, downtime reduction, order fulfillment rates) to evaluate the impact of AI-based planning.

Desired Team Profile

Ideal participants should have backgrounds in:
• Artificial Intelligence & Data Science: Understanding how machine learning can be applied to scheduling and process optimization.
• Manufacturing & Process Engineering: Familiarity with batch-based production and industrial process optimization.
• Business & Operations Management: Evaluating cost-benefit analysis and implementation strategies for AI solutions.

Additional Information

This project provides an opportunity to explore how AI can revolutionize traditional manufacturing planning. The insights gained will help EMO FRITE assess the feasibility of implementing an intelligent scheduling system, leading to more efficient production cycles, reduced downtime, and improved delivery performance.

Related Keywords

• Electronics, IT and Telecomms
• Industrial manufacturing, Material and Transport Technologies
• Digitalization
• Industrial Technologies
• Computer related