[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122756-en":3,"doc-seo-122756-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122756,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","AI and Machine Learning in Industrial Asset Management - Insights from CIAM Meetings","This research paper examines how Artificial Intelligence (AI) and Machine Learning (ML) influence Industrial Asset Management (IAM) through insights drawn from discussions in Cluster for Industrial Asset Management (CIAM) meetings. Using an interpretive case study approach, it highlights the transformative potential of these technologies, clarifies practical challenges faced during implementation, and outlines future expectations for AI and ML deployment in industrial IAM. The study links real-world experiences to broader technological promises.","SunText Review of Economics & Business  Open Access  \nISSN: 2766-4775 Research Article  \nVolume 4:3  \nAI and Machine Learning in Industrial Asset Management: Insights from CIAM Meetings  \nFrick J*  \nFaculty of Science and Technology, University of Stavanger, Norway  \n*Corresponding author: Frick J, Faculty of Science and Technology, University of Stavanger,  \nNorway; E-mail: [janfrick@icloud.com](janfrick@icloud.com)  \nAbstract  \nReceived date: 25 July 2023; Accepted date: 28 July 2023; Published date: 02 August 2023  \nCitation: Frick J (2023) AI and Machine Learning in Industrial Asset Management: Insights from CIAM  \nMeetings. SunText Rev Econ Bus 4(3): 189.  \nDOI: [https://doi.org/10.51737/](https://doi.org/10.51737/2766-4775.2023.089)[2766](https://doi.org/10.51737/2766-4775.2023.089)[-4775.2023.089](https://doi.org/10.51737/2766-4775.2023.089)  \n[Copyright:](Copyright:) © 2023 Frick J. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nThis paper investigates the influence of Artificial Intelligence (AI) and Machine Learning (ML) in Industrial Asset Management as reflected in the discussions from various Cluster for Industrial Asset Management (CIAM) meetings. (CIAM 2023) Utilizing an interpretive case study approach, it sheds light on the transformative potential of these technologies, identifies challenges encountered during implementation, and presents future predictions for AI and ML deployment in the field.  \nKeywords: Industrial asset management; AI; Machine learning; Digitalization  \nIntroduction  \nIndustrial Asset Management (IAM) has witnessed significant transformation over the years, with AI and ML emerging as key drivers of this change. Cluster for Industrial Asset Management (CIAM) is a network of companies and University in Norway. It was established in 1998 to exchange and develop knowledge between the companies and between the companies and the University of Stavanger [1] . Industrial Asset Management (IAM) refers to the strategic management of industrial assets (like machinery, equipment, and facilities) using advanced digital technologies. This typically involves the collection, analysis, and utilization of real-time data to optimize asset performance, extend asset life cycles, reduce operational costs, and improve overall productivity [2,3] . Insights drawn from several CIAM meetings highlight these technologies' potential to revolutionize IAM by enhancing decision-making processes, increasing efficiency, and minimizing human error. These case studies underscore the technologies' transformative potential and their ability to redefine conventional IAM approaches. Previous studies underscore the promise of AI and ML in industrial settings, specifically their potential to revolutionize IAM. This paper extends the existing literature by grounding the study in practical, real-world experiences drawn from CIAM meetings.  \nAI and Machine Learning: A Technical Overview  \nArtificial Intelligence (AI) and Machine Learning (ML) are two interconnected branches of computer science that have begun toredefine many aspects of modern life, including industrial asset management (IAM) . This section provides a brief technical overview of these two critical technologies.  \nArtificial intelligence (AI)  \nAI refers to the simulation of human intelligence in machines that are programmed to learn and mimic human actions. These machines can be taught to carry out tasks that would normally require human intelligence, such as understanding natural language, recognizing patterns, solving problems, and making decisions [4,5] .  \nAI can be classified into two types:  \n􀁸 Narrow AI: These are systems designed to carry out a specific task, such as voice recognition. They operate under a limited set of constraints and are only \"intelligent\" within their","cbCaijho9EJeZjjj","https://ap.wps.com/l/cbCaijho9EJeZjjj","pdf",248193,1,4,"English","en",105,"# Introduction\n# AI and Machine Learning: A Technical Overview\n## Artificial intelligence (AI)\n## Machine Learning (ML)\n# Analysis and Discussion","[{\"question\":\"What is the focus of the paper on AI and ML in industrial asset management?\",\"answer\":\"The paper investigates how AI and ML shape Industrial Asset Management (IAM) by reflecting insights from CIAM meeting discussions and related real-world experiences.\"},{\"question\":\"What technical foundations are provided for AI and ML?\",\"answer\":\"It defines AI as machine-simulated human intelligence and explains ML as learning from data, then categorizes both into types such as narrow vs. general AI and supervised, unsupervised, and reinforcement learning for ML.\"},{\"question\":\"How do CIAM insights connect AI/ML to IAM outcomes?\",\"answer\":\"The paper links AI/ML capabilities to IAM goals such as predictive maintenance, improved decision-making, reduced human error, and enhanced efficiency through analysis of real-time or large volumes of data.\"}]","AI and Machine Learning in Industrial Asset Management - 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