BlogUse CasesCortexManufacturingManufacturing Use Cases 3: Product Quality Improvement

Manufacturing Use Cases 3: Product Quality Improvement

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Throughout our series, we’ve demonstrated Cortex’s potential impact in manufacturing environment, from addressing machine downtimes to managing supply chain disruptions. In this final post of manufacturing case series, we explore how Cortex could help reduce customer returns due to product quality issues.

Pain Point – Product Quality

The company is experiencing an increase in customer returns and complaints, primarily linked to quality issues in specific product batches. While defect patterns are recognized, linking them to particular production stages or conditions remains challenging. The company has to act swiftly to find the root cause and eliminate it before product quality perception of customers becomes a major threat to their brand value.

Product Quality Manufacturing Use Cases with Cortex

Integration Method

Cortex integrates operational data from the production managemement system with customer feedback data from the CRM system.

Operational Data

  • Quality Control Metrics: Error rates from inspection results (e.g., last 6 months average error rate of 2%, while 5% in the certain batch X).
  • Production Batch Details: Details such as production dates and the specific machines used for each batch (e.g., Machine A, B, and C).

Customer Feedback Data

  • Return Records: Number of returned products, including batch numbers (e.g., Batch X with a 10% return rate compared to the last 6 months average of 3%).
  • Complaints: Specific customer complaints categorized by product type and batch number.

Data Processing By Cortex

  • Cortex analyzes quality control metrics and production batch details. It identifies patterns like higher error rates or specific defects in certain batches.
  • It examines customer return and complaint data to identify any correlations between defects and customer-reported issues in the batches with elevated defect rates.

Example Analysis

  • For Batch X, produced on 03/15/YYYY with Machine B, Cortex finds an 10% return rate due to malfunctioning displays, significantly higher than the normal 3%. Customer complaints specifically mention display issues.
  • Machine B’s data on 03/15/YYYY shows a 30% increase in error messages related to display alignment, a fact that was not adequately addressed at the time.
Cortex Product Quality Alerts

Informative Alerts

  • Cortex sends alerts to the Quality Assurance and Production Management teams, highlighting specific batches with elevated defect rates and potential correlations with customer returns.
  • Example Alert: “Batch X, produced on 03/15/YYYY, shows a higher defect rate in automated testing and correlates with increased customer returns for display issues.”

Suggested Focus Areas

Alerts specify areas for further investigation, such as “Machine B showing higher error rates on 03/15/YYYY” or “Increased manual inspection findings for display alignment in Batch X.”

Potential Benefits

  • Enhanced Product Quality: Targeted improvements based on Cortex’s insights can lead to better product quality.
  • Reduced Customer Returns: More effective quality control can decrease customer returns and complaints.
  • Improved Brand Reputation: Addressing quality issues effectively helps enhance the company’s reputation for product reliability.
  • Increased Production Efficiency: Identifying and addressing defect causes can streamline production processes, reduce waste, and improve overall efficiency.

Conclusion

Cortex’s potential role in identifying and rectifying the root causes of defects could be instrumental in reducing customer returns and complaints leading to better operational excellence. In our blog series, we aimed to demonstrate how Cortex could bring cumulative benefits to various aspects of manufacturing operations. From improving machine efficiency to smoothing out supply chain kinks and elevating product quality, each scenario highlights a facet of Cortex’s capabilities in manufacturing.

The best part of the story is, all these innovation opportunities we are mentioning in our case studies can be built and deployed within the same day, no need for months of development projects.

We invite you to try Cortex for free and witness Cortex’s capabilities firsthand. For your product quality related or any question in your mind, book a free discovery session and let’s find a solution to your specific business challenges with Cortex.

Aykut Teker is the co-founder of Selfuel, redefining innovation in data operations. Building on his extensive experience in enterprise and global R&D leadership, combined with a Ph.D. in theoretical and computational physics; he spearheads research and plays a pivotal role in shaping Selfuel’s groundbreaking, accessible, and scalable data processing platform.


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