The Application of AI in The Process of New Product Introduction at E-BI

Artificial Intelligence (AI) can play a significant role in various stages of new product manufacturing, including tooling design, process development, and quality assurance (QA) planning. With its leading Global Supply Chain Management System (GSCM), E-BI has been developing and adapting AI technologies for our clients’ success in complex system operations. Here’s how AI can assist in each of these areas:

1. Tooling Design: AI can help in designing tools and molds more efficiently by analyzing complex geometries and optimizing designs. It can generate and evaluate numerous design variations quickly, taking into account factors like material properties, manufacturing constraints, and performance requirements. AI-driven tools can assist engineers in creating designs that are both functional and cost-effective
   • Example: Generative Design – AI algorithms can generate multiple design options for a specific tool or mold, considering input parameters such as load conditions, material properties, and manufacturing constraints. Engineers can then select the most suitable design from the generated options.
2. Process Development: AI can assist in developing manufacturing processes by simulating various scenarios, predicting outcomes, and optimizing process parameters. This helps to reduce trial and error and accelerate the development cycle. AI can also identify potential bottlenecks and suggest improvements to enhance efficiency and quality.
   • Example: Process Simulation – AI can simulate the manufacturing process, taking into account factors like material behavior, temperature, pressure, and fluid flow. This simulation can help identify potential defects or inefficiencies before the actual production begins.
3. QA Plan and Quality Assurance: AI can enhance quality assurance processes by analyzing large amounts of production data in real time, identifying patterns, and predicting defects or deviations from quality standards. It can assist in automating inspection tasks, reducing human error, and ensuring consistent product quality.
   • Example: Defect Detection – AI-powered computer vision systems can analyze images or videos of products during the manufacturing process to identify defects, such as cracks, scratches, or dimensional inaccuracies. This enables immediate corrective action and prevents defective products from reaching customers.
4. Data Analysis and Decision-Making: AI can analyze vast amounts of data collected during the manufacturing process, providing insights that humans might miss. These insights can guide decision-making, process optimization, and continuous improvement efforts.
   • Example: Predictive Maintenance – AI can monitor equipment sensors and data to predict when machinery or tools might require maintenance. This helps prevent unexpected downtime and improves overall operational efficiency.
5. Continuous Improvement: AI can learn from historical manufacturing data and suggest improvements over time. By identifying trends and anomalies, AI systems can provide recommendations for optimizing processes and increasing product quality.
   • Example: Process Optimization – AI algorithms can analyze historical data to identify patterns leading to defects or inefficiencies. It can then suggest adjustments to process parameters to improve overall quality and productivity.

In summary, E-BI’s IT team, with different AI learning and training models, can significantly contribute to new product manufacturing by streamlining tooling design, facilitating process development, ensuring quality assurance, analyzing data for insights, and guiding continuous improvement efforts. By leveraging AI technologies, E-BI can reduce costs, minimize defects, accelerate product development, and enhance customer satisfaction.