AI for Industrial Decision Making
Harnessing the power of deep learning for enhanced quality decision making in industrial processes
Challenge
Maintaining enhanced quality decision-making in industrial processes presents several challenges, particularly when incorporating machine learning (ML) and deep learning (DL) technologies.
Firstly, ensuring the continuous availability of accurate and relevant data is essential, as ML and DL models heavily rely on large and high-quality datasets. Additionally, the dynamic nature of industrial systems demands constant model monitoring and retraining to adapt to evolving conditions and prevent performance degradation. Furthermore, the interpretability of ML and DL models can be complex, hindering the understanding of decision-making processes, thereby reducing trust and hindering acceptance by stakeholders.
Lastly, incorporating these advanced technologies requires a skilled workforce capable of both maintaining the models and understanding the insights they provide. Successfully addressing these challenges is crucial for harnessing the full potential of ML and DL to drive effective decision-making in industrial settings.
RINA solution
The solution aims to enhance decision-making for quality control in industrial processes by representing a holistic workflow concept that maximizes the use of all available information. The core component exploits a combination of deep learning models with sophisticated management of training data to simplify the configuration and maintenance also from legacy decision support systems.
The solution successfully overcome the need to combine quality information across multiple production steps by modeling the entire supply chain and production workflow. This involves tracking relevant information for quality decisions, even if it is measured at an upstream process, to ensure consistent and reliable quality decisions at the appropriate plant.
As final step in the production data pipeline, the pre-processed quality data are stored in a data model that can be accessed by the decision support system. This data model is designed to accommodate different types of measurement data, such as 1D and 2D measurements, as well as event-based data, making them suitable for the deep learning process to learn and make quality decisions.
Benefits for the stakeholders
Through this solution, RINA offers a range of key benefits for customers:
- Improved Decision-Making: the use of advanced deep learning technology enables better understanding and utilization of available data, leading to informed and data-driven decisions.
- Streamlined Processes: The system simplifies the configuration and maintenance of decision support systems, reducing complexity and enhancing overall workflow efficiency. This streamlining translates to improved productivity and reduced operational costs.
- Quality Consistency: the tracking of relevant information throughout the supply chain ensures that the right quality decisions are made at the appropriate stages.
- Enhanced Data Utilization: the solution facilitates optimal utilization of all available data sources. This means everyone can leverage their data more effectively, leading to better insights and continuous improvement.
- Security Aspects: it prioritizes security and confidentiality throughout its implementation. Data handling and management are executed with robust security measures to safeguard sensitive information. The system complies with industry standards for data privacy and follows best practices for secure data storage and transmission.
The system data model accommodates various measurement data types, making it adaptable to diverse industrial applications, including but not limited to manufacturing, steel production, energy production, pharmaceuticals, and transportation. It is well-suited for industries with complex supply chains and multi-step production processes that require consistent quality decisions at each stage.
