How AI Can Support KPIs Of Quality Control Manager In 2024?

In the 21st-century production sector, companies should constantly improve their processes, mainly management, production organization, and quality management. Only their proper functioning can ensure high product quality at the final stage of creation. In fact, rework necessary to achieve production quality accounts for 5% of total spending in the U.S. construction sector, more than $65 billion annually (Autodesk/FMI ). This not only meets the needs of product recipients but also substantially increases profits, providing a strong incentive for quality managers to strive for excellence.

To continually improve production quality, quality managers in the manufacturing industry should pay attention to Key Performance Indicators (KPIs). These primarily include monitoring defect rates, managing customer complaint rates, optimizing overall equipment effectiveness (OEE), minimizing reprocessing costs, and optimizing the cost of quality (CoQ). In this journey, artificial intelligence (AI) will emerge as a powerful tool, lightening the load and empowering quality managers to pursue better quality in 2024. So, let's explore how AI supports KPI for quality assurance in the manufacturing industry.

KPI For Quality Assurance – Defect Rate

Defect Rate is the percentage of defective units in a production batch, indicating the effectiveness of quality control measures. The first way AI supports production quality management KPIs is by detecting errors early in production and allowing them to be fixed quickly. This helps reduce defect rates and maintains the company's financial stability and product excellence. With advanced AI technologies, manufacturers can reduce the risk of defective products reaching the market and lower operational costs such as rework and customer returns, improving their financial position and competitiveness.

A few examples:

• Machine learning and image recognition techniques, including Computer vision – these systems, are particularly useful as they examine photos of the production line in real-time, detecting even the slightest anomalies and enabling quick responses.
• Convolutional neural networks (CNN) enable accurate defect classification using massive data sets. Another way to find bugs is to use anomaly detection methods, which look for changes from normal production patterns. These include unsupervised learning algorithms like exclusion forests and autoencoders.

KPI For Quality Assurance – Customer Complaint Rate

Customer Complaint Rate reflects the number of customer complaints about product quality, which indicates customer satisfaction and improvement areas. In this field, AI supports KPI for quality assurance by managing customer complaint management processes, ultimately improving customer satisfaction and retention. For this purpose, sentiment analysis, natural language processing (NLP), and AI-powered chatbots are used:

• AI-powered sentiment analysis tools can accurately measure the sentiment behind customer complaints across various channels, including social media, emails, and surveys.
• NLP algorithms enable organizations to identify recurring problems, detect emerging trends, and discover patterns in customer complaints. By recognizing common themes and root causes, companies can proactively address them, reducing the likelihood of similar complaints.
• AI-powered chatbots can respond instantly to customer queries and issues, offering 24/7 support and easing frustration.

AI enables companies to respond more effectively to customer complaints, increasing satisfaction and loyalty and, ultimately, better business results.

KPI For Quality Assurance – Overall Equipment Effectiveness (OEE)

Overall Equipment Effectiveness (OEE) measures equipment performance, availability, and quality, providing insight into production efficiency and downtime. AI is vital in increasing OEE by enabling predictive maintenance and optimizing production scheduling. Using AI algorithms allows firms to boost operational efficiency, productivity, and equipment reliability. This way, maintenance may be planned, reducing unscheduled downtime and production disruptions.?

For example, sensors built into machinery can continuously check fluid levels, vibration, and temperature. After that, AI systems examine this data to find trends that could point to breakdowns, allowing maintenance staff to step in before problems get worse.

Furthermore, AI programs improve production schedules by considering several variables, including demand changes, resource limitations, and equipment availability. These algorithms can dynamically modify production schedules in response to shifting circumstances, guaranteeing maximum resource efficiency and reducing idle time.?

On the other hand, AI-driven production scheduling systems, for instance, can prioritize orders according to resource limitations and urgency, maximizing throughput while meeting output limits.

KPI For Quality Assurance – Reprocessing Cost

Reducing the number of rejects, which entails enormous costs, is necessary to maintain quality standards. AI in manufacturing offers a range of technologies that can effectively minimize reprocessing costs in the manufacturing sector. Advanced machine learning algorithms can analyze massive data sets generated from manufacturing processes, equipment sensors, and quality control systems. These algorithms discover patterns and anomalies associated with defects. As a result, they indicate specific areas that generate quality problems, such as equipment failures, differences in raw materials, or human errors. Moreover, AI-based analytics can provide ideas for targeted interventions. For example:

• predictive maintenance (PdM) algorithms detect early signs of equipment degradation or failure. This prompts proactive maintenance activities to prevent defects before they occur, reducing the need for reprocessing.
• AI-driven quality control systems use real-time data analysis techniques to detect anomalies and recognize patterns, identify deviations from quality standards, and trigger corrective actions. This minimizes defects in production.

KPI For Quality Assurance – Cost of Quality (CoQ)

The cost of Quality (CoQ) is the total costs incurred to ensure product quality, including prevention, evaluation, and failure costs. AI solutions also help identify cost-reduction opportunities in the manufacturing sector. This includes using predictive analytics to predict and prevent quality problems.

Here, predictive analytics supports KPI for quality assurance and identifies potential quality issues before they become apparent. It is possible to compare historical and current data based on which AI algorithms detect patterns indicating impending defects or deviations from quality standards. This enables producers to intervene early.

In addition to predictive maintenance, AI offers many other opportunities to reduce quality costs in the manufacturing industry. Some examples include:

• Convolutional neural networks (CNN) and recurrent neural networks (RNN) for image recognition and reinforcement learning (RL) algorithms for trial-and-error learning.
• NLP techniques for analyzing unstructured data from emails, reports, and other sources to assess supplier performance and identify potential quality risks.
• Generative design algorithms and artificial intelligence techniques, such as genetic algorithms and deep learning are used to optimize product designs for manufacturability and quality.

How to Efficiently Implement AI to improve KPI For Quality Assurance?

As you can see, multiple use cases show how AI supports KPI for quality assurance. Nevertheless, before implementing AI solutions into the company’s operations, quality control managers can test the ground by participating in the AI workshop , i.e., in-depth consultations with research & development specialists in artificial intelligence. During workshop sessions, areas ripe for integrating AI technologies and those that predict the highest ROI are identified. Here, step by step, an implementation plan appropriate to the specific case and needs of a given enterprise will be developed. Some AI companies also offer 1h free AI consultations , during which you can have an initial and non-binding conversation about the possibility of using it in quality management in a given company.