In today’s rapidly evolving technological landscape, cloud computing has emerged as a pivotal solution for businesses across various industries. However, the decision to adopt cloud technologies in the industrial sector should not be taken lightly. This article emphasizes the importance of firsthand experience with cloud solutions before committing to their implementation. By engaging in pilot projects or proof-of-concept (PoC), trials, and hands-on evaluations, organizations can better understand the practical benefits and potential challenges associ-ated with cloud adoption. This approach allows businesses to assess the suitability of different cloud architectures and service providers, evaluate security measures, and ensure compatibility with existing data models and infra-structure. Additionally, gaining experience helps in identifying the necessary skills and expertise required for a successful transition to the cloud. Ultimately, experiencing cloud technologies firsthand enables informed deci-sion-making, reduces risks and maximizes the potential for achieving strategic business objectives. This article explores the steps involved in developing and executing a successful cloud pilot project, specifically from the perspective of smart manufacturing implementation.

The rolling mill has a complex scheduling process, with many logistic and operational constraints while attempting to produce plates on the correct due date of the final client. Between these constraints are the availability of the slab to be rolled and the parameters that two subsequent slabs must share or have close values, such as type of steel, temperature, thickness and width. A digital twin system is developed to assemble the next coffins that follow all the operational and logistic restrictions, while attempting to minimize the delay in demands. Coffins created using the digital twin are 12% larger and included 1% more delayed demands on average than manually created ones.

The metal and steel industry faces persistent challenges in enhancing operational safety, efficiency and complex decision-making processes. This article explores the transformative potential of generative artificial intelligence (AI), with a focus on multimodal large language models (LLMs), to address these challenges. Unlike traditional predictive and prescriptive AI, generative AI enables new possibilities by integrating and synthesizing unstruc-tured data (text, images, video) with domain-specific knowledge. Two case studies highlight practical applica-tions: (1) A Vision AI system leveraging integrated LLMs to monitor electric arc furnace operations, identifying key operational events and potential safety hazards; (2) A generative AI approach for project scheduling optimization, achieving near-optimal performance for small- to medium-sized projects. The study emphasizes generative AI’s ability to enhance decision automation, reduce reliance on manual oversight, and drive innovation in safety and efficiency. Challenges regarding accuracy, scalability and consistency are discussed, alongside recommenda-tions for future advancements in agent-based refinement techniques.

Protecting the power system for electric arc furnace steel production presents unique challenges. Dynamic sec-ondary load currents, ultrafine dust, intermittent loading and frequent breaker operations contribute to higher fail-ure risks. Overcurrent-based protection alone is insufficient to address these issues. A modern, multilayer protec-tion scheme was developed using ruggedized arc flash sensors, communications-based differential protection and voltage-based breaker failure protection. This approach effectively overcomes these challenges to signifi-cantly improve safety and reliability while protecting the most critical asset in the steelmaking operation.