This service enables manufacturing SMEs to explore how AI-driven automation can optimize post-processing workflows in high-mix, low-volume production environments. It demonstrates how technologies such as part identification, sorting, and material handling can be seamlessly integrated into a fully automated and data-driven workflow.
An overarching software platform coordinates all process steps, enabling end-to-end tracking and tracing of parts while providing real-time dashboards for operators and factory managers. This ensures full operational control, improved quality, and data-driven decision-making.
By combining AI technologies with workflow automation, the service reduces manual bottlenecks, minimizes non-value-added activities, and improves efficiency in handling complex and highly variable parts.
The result is faster production, lower operational costs, and improved control over manufacturing processes characterized by high variability in geometry and materials
he outcomes of the experiment are documented in a comprehensive evaluation report, providing insights into:
End-to-end workflow efficiency and process optimization potential
Integration performance across identification, sorting, and handling modules
Improvements in traceability, quality control, and operational visibility
Reduction of manual intervention and non-value-added activities
Recommendations for scaling toward a fully automated, data-driven post-processing environment
Evaluation of ability to leverage existing technology for high variability of parts
Demonstration Exploration with clients Testing with clients parts Experimentation (with adjustments to current set-up) based on clients new and highly potential use cases
User – Manufacturing companies operating in high-mix, low-volume and high-complexity environments that need reliable part post-processing automation for high-variation manufacturing