Artificial Intelligence (AI) in manufacturing surpasses traditional approaches to process optimization. It analyzes and learns faster, provides more and better information for decision-making, and reveals new possibilities for action. This makes AI a driver for improved efficiency, quality, and flexibility across all areas. However, these successes don’t come automatically just by implementing AI. The foundation lies in meaningful process data. These data enable the required complex analyses that lead to new insights and optimization approaches. But how do you obtain these data, and how can their quality be assessed? Which AI method is best suited for specific applications? Marcus Röper, AI expert and Product Owner for Embedded AI at Ascon Systems, explains how it all fits together.
When it comes to monitoring, analyzing, and optimizing complex production processes in real time, AI becomes a game-changer. Especially when combined with digital twins and automation technologies, it creates a powerful ecosystem for continuous adjustments, precise forecasting, and integrative process orchestration. For AI applications to fully realize their potential, they require a specific quality of process data. In many cases, this quality is not inherently available and must first be established.
Process Data as the Foundation for AI Applications
Process data are generated during production or manufacturing processes. They provide information about the condition, changes in condition, and performance of machines and processes. Their sources include machine and equipment sensors, IoT devices, or control software such as ERP, MES, and SCADA systems. AI applications are built upon this process data.
Five key factors are particularly important for evaluating the reliability and usability of data:
- Data Quality: The quality of process data is crucial to the success of AI applications. High data quality means that the data is accurate, reliable, and free from errors or outliers. Inaccurate or flawed data can lead to incorrect analyses and inefficient—or even harmful—decisions.
- Data Completeness: Complete data ensures that all relevant information for the process is captured and available. Missing data creates gaps in the analysis, reducing the performance of AI models.
- Data Consistency and Standardization: Consistent and standardized data formats facilitate the integration and processing of data from various sources. Uniformly structured data allows AI algorithms to operate more efficiently and simplifies the interpretation of results.
- Data Timeliness and Real-Time Availability: Up-to-date process data is critical for monitoring. It forms the basis for timely or even real-time decisions. AI applications can thus respond immediately to process changes and recommend proactive measures.
- Data Security and Privacy: Protecting process data from unauthorized access and complying with data protection regulations are essential. Security measures for external access ensure the integrity and confidentiality of the data, while internal privacy policies ensure that sensitive information is handled in accordance with legal requirements and internal compliance rules.
When these factors are considered, and the data is properly prepared, it provides a strong foundation for AI models to build upon and operate effectively. The next question is how to turn this into actionable knowledge.
Process Intelligence with AI: Learning from Process Data
The ability of AI to learn from process data means it analyzes insights derived from the data and combines them with its knowledge of optimal conditions and understanding of complex workflows. It identifies patterns, can recommend decisions, and generates forecasts and predictions. These insights help companies accurately predict future events such as demand, bottlenecks, or failures. AI also assists in classifying and quickly interpreting data, which can significantly improve the foundation for business decision-making.
Approaches like Process Mining and Data Mining combine the analysis of process workflows with the search for previously hidden correlations in large datasets. Modern Large Process Models take this a step further by fully modeling and simulating extremely complex, dynamic processes. This enables real-time optimization of process flows, as well as improvements in resource utilization and capacity planning. The choice of the right AI method is also critical for successful process optimization.
AI Methods for Process Optimization
AI methods encompass a wide range of technologies, including generative algorithms such as Large Language Models (LLMs) like GPT, as well as specialized algorithms from fields like Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL).
The choice of AI method depends on the specific use case. LLMs are excellent for text-based applications and knowledge management, while ML and DL excel in optimization and prediction when working with large, complex datasets. By leveraging an ensemble of these technologies, companies can generate comprehensive and precise insights that enable efficient and sustainable process optimization.
Each method has specific strengths and is applied in different ways. Here is an overview of relevant AI approaches and the results they can deliver:
- Large Language Models (LLMs): Process natural language for production instructions and reports, enhance communication and decision-making through efficient analysis, and extract relevant information from large text datasets.
- Outlier Detection: Identifies anomalous data points, detects issues like machine faults and quality problems early, increases process stability, and reduces downtime.
- Classifications and Regressions: Classification algorithms categorize data, while regression models quantify relationships for numerical predictions. Both methods support informed decision-making by recognizing patterns and trends.
- Reinforcement Learning Algorithms: Learn from feedback from their environment and continuously optimize their strategies, enabling dynamic adaptation and autonomous process optimization.
- Time Series Forecasting: Analyzes historical data to identify trends and patterns, enables accurate predictions for production demands and maintenance cycles, and improves planning and resource allocation.
There are different providers offering these methods, often with significant variations. Before implementing AI, companies should evaluate their specific requirements using a checklist to determine which AI model or combination of models meets their needs. These tailored systems and methods must not only be effective but also transparent and explainable to function properly in the complex manufacturing environment. The methods can be used individually or in combination. It is also important to note that a single model is never a standalone solution in the context of process automation; rather, it serves as an intelligent core within a broader framework.
Combined Measures for Data Integration and Transparency
To address the challenges of fragmented and isolated data, both technical and strategic measures are required. Data integration technologies such as ETL processes (Extract, Transform, Load), data catalogs, and data meshes play a central role in breaking down data silos and providing a holistic view of operations. Data meshes enable a decentralized data architecture, treating data as a product and assigning domain-specific teams’ responsibility for data processing and provisioning. Additionally, middleware for data integration ensures seamless interoperability between various systems.
Three Use Cases of AI in Practice
1. Quality Control and Process Agility:
A company in the automotive industry employs AI-based image processing for visual inspection of components. By using computer vision algorithms, defects and quality deviations are detected in real time. The software-defined production process integrates these AI insights directly into the production line, enabling immediate adjustments. The result is a reduction in waste and production costs, along with improved precision and efficiency.
2. Anomaly Detection and Safety:
In the chemical industry, a company leverages AI to detect anomalies in real time, preventing production disruptions and accidents. The AI continuously analyzes sensor data and identifies nonlinear patterns that may indicate potential hazards, such as leaks or pressure spikes. Software-defined production allows the immediate implementation of countermeasures proposed by the AI, enhancing operational safety and preventing environmental damage.
3. Prescriptive Maintenance and Self-Healing:
A machinery manufacturing company has implemented AI-powered predictive maintenance to minimize unplanned downtime. Sensor data from machines and equipment are analyzed to detect early signs of failure and wear. By integrating dynamic maintenance strategies, the AI accounts for specific aging and degradation behaviors. Software-defined production enables maintenance actions to be seamlessly incorporated into production schedules, initiating self-healing processes when necessary. This approach results in greater system reliability and significant cost savings.
The Future of Process Optimization with AI
Looking to the future, the next stage of process optimization with AI lies in the holistic analysis and optimization of complex systems. These systems not only include individual processes but also their interactions with other processes, resources, and external conditions. By leveraging AI, companies can simulate, analyze, and optimize their entire value chain to enhance efficiency, sustainability, and resilience. In addition to AI, other technologies such as digital twins, advanced simulation models, and data-driven decision-making mechanisms are also utilized. These enable the modeling and optimization of complex scenarios and dependencies.
This interplay of AI, data, and intelligent analysis represents a paradigm shift in process optimization. It allows companies to implement localized process improvements while also developing system-wide strategies aimed at long-term sustainability and competitiveness.
