The advent of Industry 4.0 has changed manufacturing systems due to utilizing new digital technologies, including AI (Artificial Intelligence), IoT (Internet of Things), cloud computing, big data analysis, and automation. One of the technologies developed in this domain is Digital Twin (DT), which is an effective method that facilitates the establishment of virtual models of physical manufacturing systems in real time. Nevertheless, conventional digital twins are only geared towards monitoring and visualization and lack the ability to make autonomous decisions. The merger of AI with Digital Twin technology allows for the intelligent prediction, optimization, and flexible control of manufacturing processes.The research paper presents a Digital Twin architecture powered by AI for improving production processes in manufacturing environments with Industry 4.0 technology. The architecture integrates IoT-enabled data collection, machine learning algorithms, forecasting technologies, simulating, and intelligent decision-making levels with the aim of enhancing production efficiency, eliminating downtime, improving usage of resources, and increasing quality of products. The paper discusses various components of the selected architecture as well as its operational processes and application in industries. It also outlines challenges that can arise while implementing the suggested architecture in manufacturing environments and possible directions for future research within the area of AI-powered Digital Twin technology.
Virendra Gomase, Suhas B. Dhande, P. Natu et al.· Journal of Intelligent Decis...· 0 citations
Business Intelligence systems powered by Artificial Intelligence (AI) have demonstrated to be an innovative approach towards transforming organizational intelligence into valuable information for strategic decisions, maximum efficiency of operations, and constant innovations within organizations. Conventional Business Intelligence systems heavily utilize descriptive analytics based on historical reporting methods and dashboard-based visualizations, and as such, they feature limited predictive and prescriptive analytics capabilities. In this scenario, the current paper aims at providing the full conceptual framework of AI-based Business Intelligence, encompassing the various elements of the system, namely, data management, AI data analytics, automated insight generation, intelligent decision making, and innovation within the organization.This framework is based on machine learning, deep learning, natural language processing, predictive analytics, and anomaly detection technologies that help process structured and unstructured data from various sources including enterprise systems, customer databases, IoT devices, market information systems, and social media platforms. AI-based analytics can help in identifying trends that manifest themselves in business processes, performance problems, risks, and opportunities as well as in providing recommendations for decision making in real time.The framework promotes and boosts innovations through enhanced strategic planning, a focus on customers’ needs in product development, optimizations of operations including processes, and adaptability of business models. The findings indicate that business intelligence based on AI technology can help improve the quality of decisions in addition to fostering higher operational efficiency, agility of organizations, and securing compliance with the demands of modern competition. Nevertheless, one should keep in mind that challenges related to data quality, systems integration, data security, explainability, governance, and adequate readiness of organizations might hinder the implementation of this framework.The results highlight the need for AI to perform the role of an intelligent decision-making support tool which enhances human expertise without replacing it. Further studies should investigate the use of AI across various industries, use of Explainable Artificial Intelligence (XAI), Generative AI, AI governance frameworks and the validation of ideas proposed through machine learning experiments and organizational case studies. The suggested project gives a framework for companies in their attempts to achieve intelligent, flexible and innovation-oriented digital transformation using the AI-driven systems of Business Intelligence.
Virendra Gomase, Suhas B. Dhande, P. Natu et al.· Journal of Intelligent Decis...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
