Industrial AI Use Cases: Addressing Challenges with Advanced Solutions
BY STEFAN MIKSCH

Following the ARC European Industry Forum this year, analysts from ARC Europe engaged with clients and attendees to discuss insights on Industrial AI applications and strategies. This summary outlines key responses from both end users and AI solution providers.

Applications of Industrial AI

End users identified key areas for Industrial AI deployment, notably in predictive maintenance, quality control, and process optimization. For instance, one company utilizes an AI-driven predictive maintenance platform to actively monitor critical equipment through vibration, temperature, and acoustic signals. By employing an enhanced anomaly detection model, they can identify potential failures weeks in advance. After updating their system with new sensors and retraining the model with six months of acquired data, they’ve achieved a 40% reduction in false positives and extended the lead time for maintenance from three days to 10–14 days.

In quality control, a different end user implements an AI vision system for inspecting cast components. This high-resolution camera technology automates the identification of surface defects, replacing a previously manual inspection process, which significantly improves efficiency and accuracy.

Additionally, in the chemical sector, an enterprise leverages AI to optimize multi-variable process parameters—such as temperature and feed rates—to enhance yield while minimizing energy use and waste. Future enhancements aim to integrate seasonal temperature variations into their model, potentially leading to even greater savings.

On the supplier side, a new AI-powered warehouse optimization platform has been developed. It predicts demand for stock keeping units (SKUs), optimizes storage layouts, and streamlines retrieval and packing processes, leading to reduced picking times and improved operational efficiency.

These applications illustrate how Industrial AI is transforming operations, delivering critical benefits such as enhanced maintenance, improved product quality, and optimized resource usage.

iTradeNetwork has partnered with Google Cloud to enhance its Cerena Solution Suite by integrating Gemini Enterprise and AI agents, aiming to streamline food and beverage supply chains. This collaboration leverages Google Cloud's advanced Data Cloud and Vertex AI platform, facilitating automation of manual workflows and improving data handling within the industry.

CEO Amer Akhtar emphasizes the partnership as a significant step in making supply chain intelligence more actionable. By using Google Cloud's infrastructure, iTradeNetwork aims to bring clarity and adaptability throughout supply chain operations, affecting procurement, finance, and logistics without complicating user experiences.

The Cerena Solution Suite, which delivers tailored solutions for each segment of the supply chain, benefits from this integration by offering enhanced data structure and categorization. This advancement leads to actionable insights rather than relying on fragmented, manually managed data. For instance, the newly developed Order Agent takes emails or PDFs of purchase orders and transforms them into validated, OMS-ready transactions, effectively reducing manual rekeying and ensuring seamless data integration.

Furthermore, intelligent agents are introduced into traditional workflows, automating routine processes and assisting staff with decision-making by identifying anomalies and providing insights. This initiative leads to greater operational efficiency and enhances capabilities for payment workflows and supplier onboarding.

In essence, iTradeNetwork's collaboration with Google Cloud not only optimizes data management but also equips customers with early visibility into risks and opportunities. This integration enables businesses to operate with increased predictability, confidence, and ultimately, competitiveness in a rapidly evolving market. To learn more, visit itradenetwork.com.

AI has significantly enhanced workflows and data analysis in materials science, with recent machine learning advancements accelerating its influence. Rather than replacing traditional methodologies, AI acts as a collaborative partner, streamlining candidate selection, guiding measurements, and optimizing research processes throughout R&D.

The complexity of materials research, which historically relied on intuition-driven exploration, often results in lengthy timelines due to countless variables in chemistry and processing. AI transforms these challenges by predicting new materials' properties, enabling inverse design for desired characteristics, and creating active-learning loops that optimize measurement selections.

One practical application is Microsoft’s MatterGen and MatterSim, which generate thousands of material candidates and evaluate them using physics-based simulations, effectively minimizing experimental needs and expediting progress in fields like energy storage.

Moreover, research from Lawrence Livermore National Laboratory illustrates AI’s ability to enhance characterization. By utilizing machine learning with X-ray absorption spectroscopy, researchers can swiftly interpret complex data, yielding faster results during experiments and creating adaptable tools for analyzing various disordered materials.

In manufacturing, AI-driven analytics uncover essential patterns within process data, leading to significant improvements. For instance, Siemens used AI to optimize printed circuit board inspections, reducing tests by 30% without sacrificing quality. Additionally, AI systems in smart manufacturing have identified production bottlenecks and streamlined workflows, ultimately increasing throughput.

The integration of AI into materials R&D not only enhances discovery but also evolves manufacturing into a responsive, data-informed process. As labs adopt these AI-driven, iterative techniques, they transition toward a unified system where discovery, characterization, and manufacturing are interlinked, paving the way for significant advancements in key sectors such as clean energy and advanced manufacturing.

Researchers are increasingly harnessing artificial intelligence (AI) to advance carbon capture technologies, essential for meeting climate goals. These innovations include generating and testing new materials and optimizing deployment strategies, which are crucial for policymakers aiming to scale carbon capture operations effectively.

Carbon capture utilization and storage (CCUS) plays a vital role in decarbonization, especially for sectors that are difficult to mitigate. The International Energy Agency (IEA) estimates that by 2050, capturing 1 gigaton of CO2 annually will be necessary, yet currently, only about 45 facilities exist, capturing merely 50 megatons. Major challenges include high energy needs and costs, underscoring the need for innovative solutions.

AI is pivotal in this endeavor, especially in identifying new materials for carbon capture. Traditional methods like post-combustion and direct air capture have numerous potential materials, with millions of variations. Researchers at the University of Illinois employed AI to automate the generation of 120,000 metal-organic frameworks (MOFs), narrowing them down to six high-performing candidates ready for physical testing after extensive simulations.

Moreover, AI facilitates the efficient implementation of CCUS systems. It can optimize how much carbon to capture and the best use of that captured carbon, whether for industrial applications or geological storage. For instance, a model created by UAE researchers enabled effective supply chain optimization, demonstrating that while storage is economically beneficial, utilization offers more sustainability. AI also helps manage energy demands: a deep reinforcement learning agent successfully scheduled carbon capture within a multi-energy system, outperforming traditional methods by over 23%.

Policymakers must leverage these AI-driven insights for effective decision-making regarding carbon capture infrastructure, subsidies, and energy policy, thereby accelerating the transition to a net-zero future.