How Machine Learning is Changing Quality Management?

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Quality management is a process used to improve product quality through continuous improvement. This involves identifying problems and implementing solutions.

Machine learning is a subfield of artificial intelligence that focuses on algorithms that teach computers to perform tasks without being explicitly programmed.

What are the similarities between these two fields?

Both Quality Management and Machine Learning involve an iterative, problem-solving approach to improving products or services. Both also require data analysis.

How do they differ from each other?

Quality management is concerned with how things work and what makes them better. It's about understanding why something works well and then figuring out ways to make it better.

Machine learning is more focused on predicting future behaviour based on past experience.

Examples of Machine Learning in Quality Control

The following examples show how machine learning can be applied to quality control:

A manufacturing company uses sensors to monitor its production line for defects. The sensor data is fed into a computer model, which predicts whether there will be any defects in the next batch of widgets. If so, the model sends alerts to workers who can fix the defect before it causes damage to the device.

An insurance company has been using a machine-learning algorithm to predict claims. When a claim occurs, the insurer feeds details of the incident into the algorithm, which compares this information against similar incidents recorded by the system. Based on this comparison, the algorithm calculates the likelihood of a claim occurring in the near future.

A hospital uses a machine-learning algorithm that learns from patient records to determine when a patient needs urgent treatment. The algorithm monitors patients' vital signs and determines if their condition requires immediate attention.

A customer service department uses a machine-learning model to help customers resolve issues quickly. After receiving a complaint, the team runs a series of tests to identify the root cause of the issue. They then use the results of these tests to train the model, which suggests possible resolutions to the problem.

The following are some other examples of machine learning applications in the quality control area:

1. Automatic detection of defects in images

2. Automated defect classification

3. Detection of anomalies in data streams

4. Real-time anomaly detection for safety-critical systems

5. Recognition of defective parts

6. Quality monitoring

7. Monitoring of production processes

8. Inspection of products

9. Classification of defects

10. Detection of foreign objects in foodstuffs

11. Identification of defective components

12. Detection of counterfeit goods

13. Analysis of customer complaints

14. Prediction of future performance

15. Fraud detection

16. Customer segmentation

17. Customer behaviour modelling

18. Predictive maintenance

19. Statistical process control

20. Failure prediction


In conclusion, both quality management and machine learning are highly relevant topics today. Both are used extensively across all industries. However, while quality management focuses on ensuring that products meet specific standards, machine learning looks at patterns within large amounts of data and finds correlations between different variables.

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