How Machine Learning Can Transform Quality Management
Services like Netflix, Google and Facebook are known for using predictive technology to learn our preferences and provide customized suggestions that get smarter over time. Now that technology is coming to the manufacturing floor, helping companies increase production capacity by as much as 20%.
More than just higher rates of production, companies can also expect to see major improvements in quality through the use of machine learning. In this post, we’re looking at how machine learning can transform quality management, from reducing defects to improving efficiency.
How Does Machine Learning Work?
Machine learning uses algorithms or calculation sequences that allow equipment to learn from data and improve performance. Prediction-generating models are continually updated based on data outputs, allowing the system to continually refine the model itself.
The machine learner compares the model’s predictions against the actual outcome, using that data to adjust the parameters feeding the model’s predictions.
Machine Learning Applications
Machine learning is already revolutionizing the manufacturing world, with applications that include:
- Quality control: Training machines with a library of visual data can help equipment learn to spot both in-specification products and defects. Analyzing component data may also help companies predict which parts are likely to fail quality control, also helping to trace defects to their origin in the production process.
- Predictive maintenance: Instead of creating maintenance schedules based on time intervals, companies can use sensors to detect unusual conditions long before equipment fails. One automotive plant used this type of technology to increase overall equipment efficiency (OEE) by 20%, helping improve reliability and product quality.
- Production optimization: Smart manufacturing systems can help companies boost product yields. More reliable equipment, optimized production planning and equipment sensors that monitor and adjust outputs in real time all make this possible.
- Reducing costs: According to Forbes, machine learning can reduce material consumption by 4%, also cutting rework by a full 20%. More predictable production capacity means companies can also cut volumes of finished goods waiting to be shipped by nearly one-third.
- Supply chain integration: Machine learning will allow manufacturers and suppliers to more tightly integrate planning and ordering, helping speed delivery while reducing material shortages.
All in all, these applications have the potential to vastly improve the efficiency of operations while reducing defects, helping to cut quality costs and improve customer satisfaction.
Big Data and the Role of the QMS
Big data means companies can now amass larger datasets than ever before. But as many organizations are finding out, this data is essentially meaningless if you don’t have a way to process it and generate insights from it. That’s where machine learning comes in.
Machine learning is both a driver of and the answer to big data. To get the kinds of quality benefits discussed here, companies need huge amounts of quality-related data. Just as important, you need that data integrated with the rest of your production process, and even the company’s performance as a whole. That means having a Quality Management System (QMS) that links information from areas that include:
- Equipment calibration and maintenance.
- Nonconforming materials.
- Corrective action.
- Quality records.
- Customer complaints.
Beyond just tying this quality-related data together, the QMS must also integrate with other areas of the organization such as risk, finance and logistics. Integration of quality data with the entire organization is what allows you to spot trends on a larger scale. Ultimately, this is what allows you to ask big questions around the internal processes and factors that affect quality the most.