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4 AI use cases for quality control in manufacturing
Learn how the integration of AI and machine learning into manufacturing processes can help organizations meet quality control needs, such as defect detection and waste reduction.
As manufacturing processes grow more complex, organizations are increasingly adopting AI-powered systems to optimize their operations, and one aspect of manufacturing that AI can assist with is quality control.
Quality control (QC) ensures that products meet the required standards, and an AI-powered system can help identify defects and reduce waste. However, using AI for QC comes with challenges as well. Introducing AI and machine learning (ML) into a company's manufacturing processes requires substantial investment, integration and training.
Here's what companies should know before using AI for quality control in manufacturing.
Benefits of using AI for quality control in manufacturing
AI can help improve key QC processes in the following ways:
- Reduces human error. While a human employee might miss a problem because of fatigue or rushing through work, a properly trained QC AI will spot any errors, enabling human employees to take the proper corrective action. In addition, AI can potentially spot problems that humans would have trouble detecting, such as tiny product defects.
- Improves brand reputation. Customers expect high-quality goods, and product defects can lead to frustration and bad reviews. AI can identify product defects before the product is sent to the customer.
- Helps meet regulations. Many parts and products must meet stringent regulations, and AI helps companies do so by flagging any compliance issues or other problems.
- Reduces waste. ML can help prevent faulty ingredients and parts from entering downstream manufacturing. Removing these flawed inputs leads to fewer products that are rejected because of inferior parts, which cuts down on waste.
- Keeps goods viable. AI can monitor environments to ensure products are stored and transported safely. For example, AI can monitor the temperature of a food storage space to ensure the conditions remain safe.
4 use cases for AI in manufacturing quality control
Some potential use cases for AI in quality control include the following:
1. Ensures consistency
Supply chains are complex, and quality can vary widely depending on the source materials, suppliers and other factors. AI and ML can consolidate data and identify patterns that result in lower-quality input, among other uses.
2. Enables analysis
Some manufacturing issues are simply the result of one-off accidents, but other issues might indicate more widespread problems. AI can highlight patterns in manufacturing errors, which employees can use to solve the underlying problem.
3. Forecasts supply and demand
Manufacturers must accurately calculate supply and demand to ensure their company will possess enough material to produce goods and meet customer needs. AI can model lead times and potential marketplace demand, which can help prevent delays in the supply chain and ensure enough products are always available.
4. Integrates with automation
Organizations can integrate their existing QC system with automation and robotics to improve the speed of building, scanning and deciding whether to accept or reject products.
A QC system can use automation to reroute defective items for further inspection and send viable items to robots that can pack and ship finished products.
Paul Maplesden creates comprehensive guides on business, finance and technology topics, with expertise in supply chain and SaaS platforms.
