From Reactive to Predictive: Using Historical Data to Reduce Scrap, Rework, and Warranty Claims

In the fast-paced world of manufacturing, quality is paramount. Yet, many companies find themselves stuck in a reactive cycle, addressing issues only after they lead to scrap, rework, or costly warranty claims. This reactive approach can significantly impact a manufacturer's bottom line, with the cost of poor quality potentially ranging from 15% to 40% of an organization's revenue, encompassing expenses related to defects, rework, and lost customers. The key to truly optimizing quality lies in shifting from merely reacting to problems to proactively predicting and preventing them by leveraging historical data.

Traditional quality control methods, such as statistical process control and root cause analysis, are essential but often applied after a failure has occurred. This retrospective view means that valuable time is spent on manual investigations to identify the root causes and trace affected products. The challenge is compounded by data silos, where critical information about designs, procurement, production, and quality remains isolated in different systems or even within individual departments. This fragmentation makes it difficult to connect the dots and gain a holistic understanding of issues across the entire product lifecycle. Furthermore, a significant amount of critical data, such as legacy drawings or handwritten notes, is often stored as unstructured data, making it inaccessible for systematic analysis.

The Power of Predictive Quality

The solution lies in embracing predictive quality control, which involves unlocking insights hidden within your historical data. This proactive approach transforms dormant information into actionable intelligence, allowing manufacturers to identify inefficiencies, risks, and opportunities that are not apparent from isolated data sources. By linking disparate data, manufacturers can gain a comprehensive understanding of their operations, from the initial design to the final product's performance in the field.

To achieve this, several key data points must be integrated and analyzed:

• Sensor data from equipment: Monitoring metrics like vibration and temperature can help predict maintenance needs and identify anomalies that could lead to quality issues.
• Process data from production runs: Analyzing deviations and patterns in manufacturing processes can flag potential problems early.
• Customer data: Insights from surveys, warranty claims, and interactions can highlight pain points and opportunities for quality or service improvement.
• Inspection and test data: Information on scrap rates and warranty claims by product configuration can pinpoint design and process issues.
• Engineering drawing data: Details like dimensions, tolerances, and material specifications are crucial for understanding design intent and manufacturability.

Benefits of a Data-Driven Quality System

Implementing a data-driven quality management system (QMS) yields substantial benefits:

• Increased yield, throughput, and asset utilization.
• Significant reductions in quality defects, scrap, rework, and warranty claims. Companies like Izumi Techno have seen a 15% improvement in defect rates, directly benefiting their bottom line.
• Faster and more effective root cause analysis, allowing teams to quickly identify other potentially problematic parts.
• Informing future design choices to prevent repeat failures and improve manufacturability.
• Proactive risk assessment across the entire product portfolio, enabling teams to address potential issues before they become widespread failures.

Despite these clear advantages, implementing a data-driven QMS presents its own challenges. These include resistance to change from employees, integration issues when connecting new systems with existing infrastructure, and persistent data quality concerns that affect accuracy and reliability. Many traditional systems struggle to incorporate unstructured data effectively, leaving valuable information trapped and unusable.

How CADDi Helps Transform Quality Control

CADDi Drawer directly addresses these challenges, acting as a System of Insight (SoI) that complements existing Systems of Record (SoR) like ERP and PLM. It revolutionizes quality control processes by efficiently managing and analyzing drawing data, streamlining the gathering, organizing, and leveraging of information to identify quality issues and drive continuous improvement.

Here’s how CADDi makes predictive quality a reality:

• Automated Data Extraction and Digitization: CADDi Drawer automatically scans and extracts all data from drawings, including dimensions, text, and shapes, even from handwritten documents. This digitizes your entire drawings archive, making even 30-year-old designs searchable and comparable, eliminating manual data entry errors and providing a complete dataset for analysis.
• Comprehensive Data Linking and Centralization: The platform connects drawing data with all relevant supply chain information, such as cost, quantity, supplier details, sales prices, and cost breakdowns. It seamlessly integrates with existing systems like ERP, CAM, CAD, and can directly link to quality defect reports and spec sheets. This creates a centralized data lake that serves as a "single source of truth", bridging departmental data silos.
• Intelligent Search Capabilities: CADDi Drawer offers powerful search functionalities that go beyond basic text searches.
  
  • Keyword Search: Users can search the entire historical drawing archive by any keyword, including material, size, designer name, part name, or notes.
  • Similarity Search: Its patented technology identifies the actual geometry and shape of parts to surface visually similar drawings, even from decades-old or handwritten sketches. This capability allows a quality manager to quickly identify all other products that use a similar component or share a critical design feature when a defect is found.
  • Image Search: Users can upload a photo of a sketch or drawing to find the closest matches.
• Enhanced Collaboration: By providing a common platform for sharing and accessing information, CADDi Drawer breaks down barriers between departments like quality assurance, engineering, procurement, and manufacturing. This fosters an environment where feedback is rapidly integrated and improvements are continuously implemented, ensuring that all stakeholders contribute to enhancing quality and efficiency.

With CADDi Drawer, manufacturers can move from manually reacting to costly quality issues to proactively identifying and mitigating risks across their entire product portfolio. This empowers teams to make data-driven decisions that not only reduce scrap, rework, and warranty claims but also drive continuous improvement and overall profitability.

Ready to see how CADDi can help you switch from reactive to proactive for better quality rates? Explore our interactive product tour or book a personalized demo.

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