The demand for guaranteed product quality through comprehensive traceability is rapidly spreading beyond the pharmaceutical industry and into other manufacturing sectors. This rising demand stems from both increased customer awareness and stricter regulations. To address this need, manufacturers are turning to Product Traceability, also known as Product Genealogy, solutions.
Efforts over the past 4-5 years, even by Micro, Small and Medium Enterprises (MSMEs), to embrace digitalization and align with Industry 4.0 principles have paved the way for the deployment of hybrid Product Genealogy solutions. These solutions combine digital technology with human interventions. However, the emergence of readily available and deployable Generative AI models presents a promising opportunity to further eliminate human intervention, ultimately boosting manufacturing profitability.
To illustrate this potential, let’s consider the Long Steel Products Industry. This industry encompasses a diverse range of products, from reinforcement bars (rebars) used in civil construction with less stringent requirements, to specialized steel rods employed in demanding applications like automobiles and aviation.
The diagram below gives a high-level view of the manufacturing process stages.
Beyond core process automation done under Industry 3.0, steel manufacturers have embraced digitalization through Visualization Solutions. These solutions leverage existing sensors, supplemented by new ones and IIoT (Industrial IoT) technology, to transform data collection. They gather data from the production floor, send it to cloud hosted Visualization platforms, and process it into meaningful textual and graphical insights presented through dashboards. This empowers data-driven decision-making by providing valuable management insights, significantly improving efficiency, accuracy, and decision-making speed, ultimately benefiting the bottom line.
However, human involvement remains high in decision-making, defining actions, and implementing them on the production floor. This is where Generative AI, a disruptive technology, enters the scene.
Imagine a production process equipped with a pre-existing Visualization solution, constantly collecting data from diverse sensors throughout the production cycle. Let’s explore how Generative AI adds value in such a plant, specifically focusing on long steel products where each batch run (“campaign”) typically produces rods/bars with distinct chemical compositions (e.g., 8mm with one composition, 14mm with another).
Insights and Anomalies
- Real-time data from diverse production sensors (scrap sorting, melting, rolling, cooling) feeds into a Time-Series database. This multi-modal telemetry data, like temperature, pressure, chemical composition, vibration, visual information etc., fuels a Visualization platform generating predefined dashboards and alerts. With training and continuous learning, Generative AI models analyse this data in real-time, identifying patterns and deviations not envisaged by predefined expectations. These AI-inferred insights, alongside predefined alerts, highlight potential issues like unexpected temperature spikes, unusual pressure fluctuations, or off-spec chemical composition.
- If trained on historical and ongoing ‘action taken’ data, the AI model can generate partial or complete configurations (“recipes”) for uploading to PLCs (Programmable Logic Controllers). These recipes, tailored for specific campaigns based on desired results, adjust equipment settings like temperature, cooling water flow, and conveyor speed. The PLCs then transmit these configs to equipment controllers, optimizing production for each unique campaign.
- Individual bars can be identified within a campaign using QR code stickers, engraved codes, or even software-generated IDs based on sensor data. This ID allows the AI to link process and chemical data (known as ‘Heat Chemistry’) to each specific bar. This information helps identify non-conforming products early, preventing them from reaching final stages. For example, non-conforming bars can be automatically separated at the cooling bed before reaching bundling stations.
- Customers can access detailed information about the specific processes and materials used to create their steel products, including actual chemistry and physical quality data points. This transparency builds trust in the product’s quality and origin, differentiating your brand in the market.
Enriched Data Records
- The AI model’s capabilities extend beyond mere interpretation of raw sensor data—it actively enriches it with additional information. This enrichment process encompasses:
- Derived features: AI extracts meaningful variables from sensor data, such as calculating cooling rates from temperature readings or estimating carbon content from spectral analysis.
- Contextualization: AI seamlessly links data points to specific production stages, equipment used, and even raw material batch information, providing a holistic view of the manufacturing process.
- Anomaly flagging: AI vigilantly marks data points that deviate from expected values, making critical events easily identifiable and facilitating prompt corrective actions. This also helps in continuous learning by the AI model.
- This enriched data forms a comprehensive digital history for each bar, providing invaluable insights that fuel process optimization and quality control initiatives.
While the aforementioned functionalities showcase Generative AI’s immediate impact on traceability, its potential extends far beyond. Trained and self-learning models pave the way for advancements like predictive maintenance, product simulation, waste forecasting, and even autonomous recipe management. However, these exciting future applications lie beyond the scope of this blog.
Despite its nascent stage in long steel product genealogy, Generative AI is already attracting significant attention from various companies and research initiatives. This growing interest underscores its immense potential to revolutionize the industry.
Challenges and Considerations
- Data Quality and Availability: The success of AI-powered traceability hinges on accurate and complete data throughout the production process. Integrating AI with existing infrastructure and ensuring data consistency across systems pose significant challenges.
- Privacy and Security Concerns: Sensitive data about materials, processes, and customers must be protected. Secure data storage, robust access control mechanisms, and compliance with relevant regulations are paramount.
- Scalability and Cost-Effectiveness: Implementing AI-based solutions requires investment in hardware, software, and expert skills. Careful ROI analysis and planning are crucial to avoid budget overruns. Scaling these solutions to large facilities and complex supply chains requires thoughtful cost analysis and strategic planning.
By addressing these challenges and unlocking the power of Generative AI, manufacturers can establish robust and transparent product traceability systems. This, in turn, will lead to enhanced product quality, increased customer trust, and more sustainable practices.