Defining and realizing MBD success

Evan Kessick, a Model-Based Enterprise Principal Engineer at Belcan, discussed the critical components of establishing a strong foundation for model-based definition (MBD) and model-based enterprise (MBE) to enhance digital transformation in engineering and manufacturing. His talk emphasized the benefits of transitioning from traditional 2D drawings to MBD, including efficiency gains, error reduction, and faster product development cycles. Kessick highlighted the importance of industry standards, machine readability, and collaborative tools in achieving these benefits.

Key Insights

1. MBD Reduces Errors and Increases Efficiency
   • MBD consolidates all product definition data into a single source, reducing the risk of discrepancies between 2D drawings and 3D models. This can lead to up to a 60% reduction in time spent on first article inspection and up to 90% reduction in quality escapes.
2. Focus on Machine Readability for Better ROI
   • To fully utilize MBD, prioritizing machine-readable data is essential. This allows for automatic consumption and reuse of digital information across software systems, enabling automated processes like tolerance stack-up analysis, should-cost analysis, and model-based inspections, thereby reducing manual errors.
3. Importance of a Collaborative Digital Environment
   • Using collaborative tools like CoLab ensures that all stakeholders—engineering, manufacturing, supply chain, and others—can participate in real-time or asynchronous design reviews. This reduces the need for rework and speeds up the product development process.
4. Align with Industry Standards for Consistency
   • Adopting industry standards such as ASME Y14.47 for MBD ensures that product definitions are consistent and interpretable by both humans and machines, which is critical for maintaining data integrity and interoperability throughout the enterprise.
5. Early Design Collaboration Improves Outcomes
   • Shifting design collaboration earlier in the development process allows teams to identify and address issues before they escalate. This proactive approach improves data quality and accuracy, reduces time to market, and ensures alignment across all functional teams.

Tactical Tips

1. Transition to MBD for Product Definitions
   • Start moving from traditional 2D drawings to MBD. Learn about relevant industry standards and best practices to ensure that product data is clear, consistent, and machine-readable.
2. Prioritize Machine-Readable Attributes
   • When creating MBDs, ensure that attributes and annotations are both human and machine-readable. This enables automation in downstream processes like quality inspections and digital simulations.
3. Leverage Digital Collaboration Platforms
   • Use platforms like CoLab to facilitate real-time and asynchronous feedback from all stakeholders. Ensure that everyone, including external partners, has access to digital models and annotations for comprehensive reviews.
4. Incorporate Industry Standards into Processes
   • Understand and implement industry standards (e.g., ASME Y14.47) in your MBD practices. This will enhance data consistency and reduce miscommunication across the organization.
5. Conduct Early and Inclusive Design Reviews
   • Schedule design reviews early and include all relevant stakeholders—engineering, manufacturing, and supply chain. This helps catch issues early, reducing rework and ensuring cross-departmental alignment.