In today’s industrial landscape, artificial intelligence (AI) has become an essential tool for enhancing performance and driving innovation. However, the success of any AI project hinges on one crucial element: data quality. Accurate and reliable data is indispensable for powering AI algorithms, ensuring relevant analyses and precise predictions.
Unfortunately, many companies struggle to maintain this quality, often encountering issues like missing, incorrect, or poorly structured data. Such challenges not only hinder the effectiveness of AI projects but can also result in significant costs. Here’s why data quality is critical, along with three concrete use cases.
Data Quality: A Key Factore for AI Success
To harness the full potential of AI solutions, data quality must be ensured at every stage: data collection, cleaning, and structuring.
Data collection: A Crucial Step for Quality Assurance
Data collection is the foundation of effective AI, but it can vary widely in quality if not carefully managed.
In systems like Product Lifecycle Management (PLM) and ERP, data quality depends heavily on the accuracy of information entered, whether through manual or automated inputs. Reliable data collection also includes regular feedback from subcontractors, whose data is vital for tracking product status and material quality.
Updating old data is essential to avoid obsolescence that could compromise analyses. A thorough and consistent collection process lays a solid foundation for high-performing AI models.
Data cleaning: Eliminating Errors and Inconsistencies
After collection, data cleaning is a crucial step that includes:
Error detection: Identifying obvious mistakes like outliers or poorly filled fields.
Managing inconsistencies: Standardizing data to ensure coherence and alignment with real-world conditions.
Structuring Data for Optimial Use
Data structuring is key to enabling AI models to interpret information accurately. This process involves:
Information prioritization: Organizing data by relevance.
Linking sources: Connecting data from different systems (PLM, ERP, etc.) to ensure comprehensive understanding.
Now, let’s explore three real-world AI use cases in industry.
Use Case 1: Cost Prediction for Manufacturing Processes in Stamping
In the automotive industry, a client is using AI to predict the cost of manufacturing stamped parts, optimizing profitability as early as the design phase. This approach leverages a dedicated dataset to achieve more accurate cost estimates well before production.
The main challenge lies in respecting client-specific business rules, which must be integrated to clean the data and ensure relevance. By anticipating costs, the company improves operational efficiency and maximizes profits.
For further insights, download our white paper detailing this use case.
Use Case 2: 3D Part Recognition
In the field of industrial part design, a client is exploring AI to determine whether a newly designed part already exists, aiding in duplicate management. This integration offers substantial benefits, including faster response to quote requests, streamlined parts sourcing, and a shortened development cycle.
Through 3D data collection, the project faced challenges with data conversion and labeling, but rigorous model optimization has improved the accuracy of similar-part searches. Overcoming these obstacles, AI enables more efficient 3D part recognition, enhancing competitiveness and customer satisfaction.
To learn more, download our white paper on this use case.
Use Case 3: Extraction of Technical Data from Excel Files
An industrial client is using AI to extract relevant technical data from hundreds of Excel files that detail their parts' manufacturing processes. However, specialized jargon and complex abbreviations make interpretation difficult for AI models, requiring sector-specific clarifications.
Additionally, data readability varies due to mixed text and images, merged cells, and multiple tables on a single page.
By adopting standardized formats and tailored AI models, the company achieves information retrieval in 98% of cases, compared to only 40% previously. This significant improvement demonstrates how combining AI and industry expertise transforms data extraction, facilitating informed decision-making and optimizing operational efficiency.
For a more detailed exploration, download our white paper now.
These examples highlight that deep industry knowledge and rigorous data management are essential for ensuring prediction quality. By combining AI expertise with sector-specific insights, companies can turn challenges into opportunities, optimizing operational efficiency.
To dive deeper into these critical issues and explore these use cases in detail, download our white paper The Importance of Data Quality for AI Applications in Industry. It provides in-depth insights into AI’s role in industry and concrete strategies for improving data quality in your projects.
Don't miss this opportunity to expand your knowledge and accelerate your AI initiatives!
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