@all Machine Learning (ML) is increasingly being employed in Quality Control (QC) within the Supply Chain to identify potential quality issues, streamline inspections, and enhance product quality. Here are some ways predictive models are used in this domain:

Real-world applications of ML in Quality Control within the Supply Chain can be found across industries:

• Manufacturing: ML models are used to detect defects in products, optimize inspection processes, and improve overall product quality.
• Automotive: ML is employed for identifying defects in parts, predicting failure rates, and optimizing quality control procedures during production.
• Electronics: ML models assist in detecting faulty components, predicting failure rates, and optimizing testing and inspection processes.
• Food and Beverage: ML is utilized for quality inspection of food products, including visual inspections, sensory analysis, and freshness assessments.

Technical aspects of ML models in QC may involve techniques such as convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for time series data analysis, decision trees, support vector machines (SVMs), or ensemble methods for classification tasks. Feature engineering, model training, and validation are critical steps in developing effective ML models for quality control applications.

It's important to note that successful implementation requires high-quality and representative training data, close collaboration between domain experts and data scientists, and continuous model monitoring and adaptation to evolving quality requirements.

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