The Data Daily

The Case for Machine Learning in Quality Management

The Case for Machine Learning in Quality Management

Quality management is a crucial part of running a business. If companies hope to maximize sales and build a loyal customer...

Quality management is a crucial part of running a business. If companies hope to maximize sales and build a loyal customer base, they must embrace continuous improvement of their products and processes. Machine learning can help.

As markets grow increasingly competitive, quality management must become as efficient and effective as possible. In today’s environment, that means capitalizing on the most disruptive technologies. In many cases, that’s where machine learning comes into play.

Quality management encompasses many smaller disciplines from refining manufacturing processes to handling customer complaints. Businesses must interpret relevant data in all these applications — often in large volumes — to find optimal solutions. ML brings two primary advantages to that process: accuracy and efficiency.

ML models tend to outperform humans when making connections between data points. One institute has developed a tool that cancorrectly predict the likelihoodof tumor regrowth in cancer patients. As long as they have quality data, many models display similar accuracy in other contexts.

In quality management, ML algorithms could interpret situations better than humans, leading to more effective changes. A model could weigh the many disparate factors in manufacturing quality to determine how best to alter workflows to reduce waste or improve efficiency. Since many quality-management decisions rely on understanding broad and complex data sets, ML is an ideal solution.

Machine learning can also accelerate quality management processes. When these workflows are manual, they’re often slow. Humans take time to read through relevant information and understand situations. An ML model can analyze data points far faster, providing actionable insights in less time and driving quicker improvements.

In some scenarios, workers may have to sift through thousands of reports to learn where they stand as a company. ML algorithms could process that information in hours or even minutes, helping businesses refine their processes faster and achieve quicker returns on investment.

These benefits can apply across many specific processes as well. Here’s a look at some use cases for machine learning in quality management and how this technology improves them.

In manufacturing facilities, ML models can pair with machine vision to detect product defects faster and more accurately than humans. Heineken installed such a system in a bottling plant in 2018, achieving anear 0% error ratewhile inspecting 22 bottles each second.

Over time, ML quality-control systems could also detect trends. They could recognize repeated errors and determine where these issues arise. Manufacturers could adjust their workflows as necessary to reduce future defects, lowering operating costs through continuous improvement.

Understanding risks is a critical part of quality management for financial businesses like insurers and banks. Since machine learning is typically better at predicting factors like risk from diverse data sets, it’s the ideal tool to refine these assessments. Predictive models can help understand various customers’ risks to enable more personalized and accurate pricing.

Progressive Insurance, for example, uses its telematics program’s14 billion miles of driving datato enable ML-based risk assessment. This data helps predictive analytics models learn which factors indicate risk, predicting premiums and losses for different customers’ policies. They can customize coverage to balance customer affordability and risk management for the company.

Manufacturers and other product-based businesses could also use ML to refine their aftermarket services. Analytics models could look through customer complaints and product life cycles to better understand their longevity. Companies could use these insights to improve production, maintenance service, and warranty costs.

One such example of where predictive modeling could help tailor service to specific markets is the renewable energy industry. While most solar panel installmentscome with 12-year warranties, various location-specific outdoor conditions could affect average panel life spans. Predictive models could look at differences in installments across locations to determine how long products will last in different environments. They could tailor warranties by area to provide more appropriate coverage.

Customer service is one area of ML quality management that applies across all industries. Companies can use chatbots to provide initial customer service to people asking questions or making complaints. Analytics models can look at this data to discover trends, revealing areas to improve.

As repeated issues appear, ML models will notice and can inform relevant people within the company. The business can adjust its product, services, or workflows to address the root of the issue to improve overall customer service.

ML can take quality management far beyond human capabilities. Data scientists should take note of this opportunity and learn to create and refine models to serve these markets. As more businesses realize how ML can improve these processes, possibilities for data scientists will expand.

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