Diagnostic Analytics in DFM: Uncovering “Why It Happened?”

Building upon the insights gleaned from descriptive analytics, diagnostic analytics delves deeper into the data to understand the underlying causes of observed DFM outcomes. It seeks to answer the crucial question: “Why did this happen?” By identifying root causes, correlations, and contributing factors, diagnostic analytics empowers engineers to move beyond mere symptom recognition to truly address design flaws and process inefficiencies at their source. This layer of analysis is critical for informed problem-solving and preventing recurring issues.

Key Components of Diagnostic Analytics in DFM

1. Root Cause Analysis (RCA):

   RCA is at the heart of diagnostic analytics. In DFM, it involves systematically investigating frequent or critical rule violations to trace them back to their origin—whether a specific design decision, a flawed process step, a material characteristic, or a lack of designer training. Common RCA techniques adapted for DFM include:

   • 5 Whys: Repeatedly asking “why?” to peel back layers of causation. For example, “Why did this part fail the minimum wall thickness check?” “Because the designer didn’t account for material shrinkage.” “Why didn’t they account for shrinkage?” “Because the design guidelines weren’t clear on shrinkage compensation for this material.” “Why weren’t they clear?”
   • Fishbone (Ishikawa) Diagrams: Categorizing potential causes (e.g., Man, Machine, Material, Method, Measurement, Environment) leading to a specific DFM problem (e.g., high tooling costs). Its importance lies in addressing the underlying causes rather than just treating symptoms.
   • Pareto Analysis: Identifying the “vital few” causes that contribute to the “trivial many” DFM issues (e.g., 80% of DFM violations come from 20% of design features or 20% of designers).

   DFM tools provide the granular data necessary for RCA, allowing analysts to drill down from a summary report of ‘top 10 DFM violations’ to the specific instances of those violations, examining associated designs and timestamps.

2. Correlation Analysis:

   This component involves identifying statistical relationships between different design parameters, manufacturing variables, and DFM outcomes. Correlation analysis helps answer questions like: “Is there a relationship between increasing geometric complexity and an increase in manufacturing defects?” or “Does the use of certain exotic materials correlate with a higher incidence of welding issues?” Examples in DFM include:

   • Positive Correlation: As the number of complex internal features increases, the likelihood of ‘tool accessibility’ violations also increases.
   • Negative Correlation: As draft angles increase, the frequency of ‘demolding’ issues decreases.
   • No Correlation: The color of the plastic part has no correlation with its moldability.

   Techniques like scatter plots, correlation matrices, and regression analysis are employed. By identifying these correlations, engineers can develop hypotheses about causal relationships and target areas for further investigation or design guideline refinement. For instance, if a strong correlation is found between a specific type of fillet and cracking during injection molding, diagnostic analysis can lead to a re-evaluation of fillet design standards.

3. Drill-down Analysis:

   Often integrated into DFM dashboards and reporting tools, drill-down analysis enables users to navigate from high-level summaries to increasingly detailed views of the data. For example, a user might see a dashboard showing an uptick in ‘thread relief’ issues. They can then click on that metric to see which parts are affected, then view the results and design tree on a specific CAD model in the associated CAD Software to see which features are problematic, and finally, view the exact DFM rule violation and perhaps even the CAD model section where the issue lies. This granular exploration is crucial for pinpointing the exact problematic features, design choices, or manufacturing process steps that contribute to DFM non-compliance. A sample dashboard is shown below where design engineers, reviewers or other stakeholders can pinpoint issues and track them across design variations.

4. Exception Reporting/Outlier Detection:

   Outlier detection involves identification of designs or manufacturing processes that deviate significantly from established norms or expected performance. Outliers might indicate a novel design approach that works exceptionally well (and should be studied for best practices) or, more commonly, a significant problem that warrants immediate attention. For example, a design with an unusually high number of DFM violations compared to similar designs, or a manufacturing batch with an unexpected defect rate, would be flagged for diagnostic investigation. Statistical methods like Z-scores or clustering algorithms can be used for outlier detection. Recurring patterns among outliers can be used to train machine learning models so that such outliers are flagged during design or not flagged in the future if deemed unimportant.

Benefits of Diagnostic Analytics in DFM

• Enables Targeted Design Improvements: By understanding the ‘why’ behind DFM issues, engineering teams can implement precise, effective design changes rather than resorting to trial-and-error. This leads to more efficient iteration cycles and higher-quality designs.
• Reduces Recurring Issues Through Informed Decision-Making: Addressing root causes means solving problems permanently, preventing the same issues from reappearing in future designs or product generations. This fosters a learning organization and continuous improvement.
• Supports Continuous Improvement Initiatives: Diagnostic insights directly feed into refining DFM guidelines, updating design best practices, improving designer training programs, and optimizing manufacturing processes. It transforms failures into learning opportunities.

Challenges of Diagnostic Analytics in DFM

• Requires High-Quality, Granular Data: To perform meaningful root cause and correlation analysis, the data must be not only clean but also highly detailed. This includes comprehensive design parameters, material specifications, manufacturing process data, and detailed DFM rule violation logs. Insufficient granularity can mask the true causes.
• May Involve Complex Statistical Analysis: Identifying correlations and establishing causality often requires expertise in statistical methods, data modeling, and potentially machine learning algorithms. Organizations may need to invest in data science capabilities that abstract away this complexity.
• Dependent on Domain Expertise for Interpretation: While statistical models can identify correlations, interpreting these correlations in the context of specific engineering principles and manufacturing realities requires deep domain expertise. A correlation between two variables doesn’t necessarily imply causation; engineers must validate the findings with their understanding of physics, materials science, and manufacturing processes.
• Confounding Variables: In complex manufacturing environments, many factors can influence DFM outcomes simultaneously. It can be challenging to isolate the impact of a single variable or establish a direct causal link when other “confounding” variables are also at play. Advanced statistical techniques and controlled experiments may be needed to mitigate this challenge.

These challenges can be addressed by focusing on only the critical issues revealed by descriptive analytics. Apply the pareto principle to determine which 20% of issues will provide 80% of the benefits in time, cost, etc. Deep dive on those issues using statistical tools used in the organization. Future DFM tools may have built-in capabilities for this purpose.