Predictive Quality – improving product quality with AI

Imagine being able to detect production errors before they even occur. What if your production line was not only monitored by artificial intelligence (AI), but actively optimised to ensure better product quality? This vision is no longer a dream of the future, but already a reality for many companies that rely on predictive quality in their production processes.

Quality management in industrial companies today faces enormous challenges:

• Complex production processes,
• data volumes and diversity,
• rising customer expectations
• and the pressure to work ever more efficiently and cost-effectively

make it difficult to detect errors in good time and correct them.

This is due, among other things, to increasingly complex products, larger production volumes and increased requirements for complete documentation and traceability. Traditional methods such as manual inspections and random checks quickly reach their limits here. This is exactly where Predictive Quality comes in.

This method uses AI, among other things, to solve existing problems in production more quickly and efficiently and to proactively master future challenges.

Predictive quality is a concept from the field of quality management. It focuses on predicting and ensuring product quality. To this end, advanced data analysis methods and machine learning are used to preventively improve the quality of products and processes through the use of data-driven predictive models in manufacturing.

The aim is to identify and prevent quality problems before they occur, which minimises scrap and rework and increases production efficiency. The use of predictive models enables companies to implement proactive quality control that goes beyond simply reacting to errors.

Predictive Quality is based on the continuous collection and analysis of production and process data. Different sensors record various process parameters in real time, such as:

• Temperatures
• Pressure
• Humidity and other relevant parameters

This data is then combined with historical process parameters and evaluated using machine learning to identify patterns and correlations. Based on these patterns, predictions can be made about when and where quality problems could occur in the production process. This allows preventive measures to be taken before expensive production errors occur.

Predictive quality and predictive maintenance are specific applications of predictive analytics. This term generally refers to all methods used to predict future events based on historical and current data.

Predictive quality focuses on optimising quality in production processes. Predictive maintenance, on the other hand, aims to monitor the condition of machines and predict maintenance requirements.

Predictive quality therefore focuses on ensuring product quality. This method analyses production-related data such as:

• Material properties
• Process parameters and
• Environmental conditions.

This provides a holistic view of quality control that goes beyond pure machine monitoring. The aim is to track the product along the entire production line and prevent time-consuming quality checks or rework.

Smart cameras and the increasing use of artificial intelligence in the form of machine learning make it easier to implement the increased quality requirements more effectively. Compared to traditional methods, these technologies offer numerous advantages. We will take a closer look at these in the following.

Compared to traditional quality control methods, which are usually reactive to problems as they arise, predictive quality offers a proactive approach. This makes it possible to identify potential problems before they lead to production downtime or quality defects.

This enables companies not only to improve the quality of their products, but also to save costs by reducing waste and rework. In addition, Predictive Quality helps to improve the efficiency and reliability of production processes.

Every error in production leads to rejects, and rejects in turn require rework to correct the errors that have occurred. This cycle has a negative impact on profitability, as it consumes both time and resources that could be used to produce error-free products.

Quality problems are often caused by mechanical or technical issues, material problems or the fact that complex and interlinked processes can only be checked at the end.

Many manufacturing companies still rely on traditional quality assurance methods, such as visual inspections or random checks. Although these approaches are well established, they are increasingly reaching their limits.

In complex production environments in particular, manual processes can no longer keep pace with the demands for speed, precision and data volume.

Nevertheless, manual inspection of products at the end of the production line is still commonplace. While this process was sufficient in the past to ensure acceptable quality, it is now often too slow and inaccurate to meet modern production standards such as Lean Production, Six Sigma or ISO 9001.

Various technologies are used to implement predictive quality, including AI in the form of machine learning or generative AI, as well as IIOT platforms.

These technologies enable the processing and analysis of large amounts of data in real time. In addition, a robust IT infrastructure is necessary to ensure efficient data processing and storage.

Machine learning (ML): is a branch of artificial intelligence that develops algorithms and statistical models that enable computers to learn from data and make decisions without being explicitly programmed. Machine learning can be used in various applications, including predictive analytics.

Predictive analytics: refers to techniques that use historical data to predict future events or trends. Predictive analytics uses statistical algorithms and models to identify patterns in data and apply these patterns to predict future outcomes.

Generative AI (or GenAI): Unlike predictive analytics, it can generate new and unique results based on recognised patterns. In manufacturing, for example, it is used to simulate production processes and develop new solutions.

IIoT platform (Industrial Internet of Things): serves as a basis for uniformly collecting and structuring data, for example via a unified namespace (UNS). By networking machines via an IIoT platform, data from different sources can be collected and centrally consolidated. This is crucial for making relevant data available across the board and using it as a basis for predicting quality problems.

Production companies require different types of data as a basis for implementing predictive quality in the production process. These include:

• Production data
• Process data
• Quality requirements
• Data on environmental conditions

This data should be continuously recorded and analysed in real time. This includes, for example, process parameters, material properties and data from the final inspection.

Holistic recording is necessary in order to obtain a complete picture of the production processes and to be able to make accurate predictions.

High data quality is a key factor in the success of predictive quality. Those responsible for data collection should therefore ensure that their data is consistent, complete and accurate.

This can be achieved through regular data checks, the implementation of data validation rules and training for employees in data maintenance.

In addition, those responsible should ensure that data sources are well documented and standardised to enable smooth data integration and analysis.

The challenges involved in data collection, processing and integration are manifold:

That is why companies need to invest in powerful data management systems and establish clear processes for data collection and preparation.

Modern approaches such as data lakes and data meshes have been developed to meet these challenges, especially with regard to production data.

A data lake is a centralised repository that stores large amounts of raw data in its original format. This is particularly relevant for production data, as it often comes in different formats (e.g. sensor data, log files, machine parameters, videos of production processes).

Advantages of data lakes for production data:

• Scalability: A data lake can store large amounts of unstructured and structured data. Production data generated by sensors and IIoT devices (e.g. SCADA systems) can be stored here without restrictions.
• Flexible data acquisition: Production data can be acquired from various sources (e.g. machines, production control systems, ERP systems) without having to be converted into a specific schema. This enables faster data integration.
• Advanced analytics: Production data stored in a data lake can be used for various types of analytics, such as predictive maintenance, process optimisation or quality improvement. It is also easy to combine production data with data from other areas of the company (e.g. sales, logistics).
• Accessibility: Different departments (e.g., production management, quality assurance, IT) can access the data to perform analyses and make data-driven decisions.

The Data Mesh approach is a modern concept that aims to decentralise data architectures. In contrast to the centralised approach of a data lake, Data Mesh pursues the idea of managing and using data in a domain-specific manner. Production data is viewed as independent data products that are managed by the respective teams or domains.

Advantages of Data Mesh for production data:

• Decentralised responsibility: Each production line, location or even machine can be viewed as its own domain that manages its own data products. This means that the teams working closest to the production data have full control over its management, quality and use.
• Leverage domain-specific knowledge: Because responsibility for the data lies with the teams that best understand the production processes, they can make faster and more effective decisions about how to use the data. This leads to higher data quality and better analytics.
• Faster data delivery: Teams that manage their own data products can access production data more quickly and use it for analytics and decision-making without relying on central IT departments.
• Scalability and flexibility: Because each domain can operate independently, the data mesh grows with the business without creating central bottlenecks. This is particularly beneficial in large production operations with multiple locations or production lines.

To measure the success of AI in quality control, clear KPIs (key performance indicators) should be defined. These can include the following points:

• Reduction of scrap
• Shortening of production times
• Improvement of product quality

For example, a large manufacturer in the injection moulding industry set KPIs such as ‘20% reduction in error rate’ and ‘15% reduction in production time’ as targets for its AI implementation. These two KPIs were regularly reviewed after implementation of the AI technology to evaluate the success of the measures.

After introducing AI technology, regular measurements of KPIs were carried out. This took the form of periodic reviews. AI itself played a central role in continuously monitoring the error rate and production time. Sensors on the machines and real-time data analyses provided continuous updates on the performance of the systems. This meant that values were recorded and analysed automatically without manual intervention.

The implementation of AI models is not a one-time process. Rather, it is important to regularly review and optimise them in order to adapt them to new challenges and data.

This is particularly important in industries that deal with constantly changing process parameters. For example, a medium-sized food producer adjusts its AI models every six months to account for seasonal fluctuations in raw material quality and ensure consistently high product quality.

In order to successfully implement predictive quality in your company, structured and creative solutions are necessary, especially when dealing with complex requirements. To effectively overcome technical and organisational difficulties, it is crucial to establish a solid foundation right from the initial phase of the project.

MaibornWolff offers you comprehensive consulting services to help you do just that. We start with a detailed analysis of your specific needs and accompany you through all phases of the project. Our approach to finding a comprehensive solution includes the following steps:

The customer: Before the vehicles of a leading car manufacturer are ready for series production, numerous problems must be identified and rectified. The data from prototyping, auditing and warranty processes is collected separately. A holistic view of this data offers the potential to identify problems before production begins.

The task: Link data sources with connected engineering.

To reduce the use of prototypes, vehicle parts and machine tools, we use data analysis, preparation and presentation to ensure virtual, function-oriented vehicle development and quality assurance. Linking different data sources, assigning issues to data points using semantic analysis and clustering topics helps to avoid problems in production.

The special features: After the customer had formulated their vision, our project team developed the ML models and designed the infrastructure, backend and frontend from scratch.

The result: A rule- and AI-based machine learning (ML) pipeline clusters topics from the newly linked data sources. Production engineers can see the key figures relevant to them on a dashboard and use the clusters to identify problem areas.

Such clues help them run through scenarios to make data-based decisions: for example, whether the use of certain seals or the design of the door frame enables the window to close wind- and watertight and influences the quality indicator.

The forecasting and control dashboard helps to structure information and identify relevant insights. This contributes directly to reducing costs.

Developments in the field of artificial intelligence are advancing rapidly. New algorithms and technologies are constantly being developed that have the potential to further revolutionise quality control.

From aerodynamic improvements and optimised fault prediction to fully autonomous production lines, the opportunities are endless.

To be prepared for future challenges, companies should remain flexible and adaptable. This means constantly evaluating new technologies and integrating them into corporate strategy.

Predictive quality is not just a buzzword, but a real opportunity to fundamentally transform quality assurance in your company.

In this guide, we have shown you how artificial intelligence (AI) can help you master the challenges of modern production while raising product quality to a new level.

The starting point: Traditional methods of ensuring quality often reach their limits today. Manual inspections and random checks are time-consuming and prone to errors. Given the increasing complexity of production and growing customer expectations, it is becoming increasingly difficult to guarantee consistently high quality. This is where predictive quality comes in.

The role of AI: With machine learning and generative AI, you can not only detect and fix existing errors faster, but also proactively avoid future problems. AI enables you to analyse large amounts of data in real time, recognise patterns and make automated decisions. This allows you to continuously monitor and optimise the quality of your products.

Practical applications: Numerous real-world examples show how companies are already successfully using predictive quality. Whether in the automotive industry, where scrap rates are being drastically reduced, or in food production, where generative AI is helping to optimise processes, the possibilities are diverse and cross-industry. These examples illustrate that implementing AI is not just a theoretical concept, but brings measurable success.

The implementation process: Introducing AI into your production processes requires careful planning and implementation. It is important to choose the right technologies, master their integration into existing processes and train your employees accordingly.

It is also important to continuously monitor the success of your measures and regularly optimise the AI models in order to achieve the greatest possible benefit.

Your next step: Use the insights from this guide to rethink your quality management and tap into the potential of AI. Start by analysing your current processes, identifying the areas where AI can offer the greatest added value, and developing a detailed implementation plan.