Facing the Fourth Industrial Revolution: empowering (human) design agency and capabilities through experimental learning

This article identifies and describes the transformation of designer skills within the Great Transformation (Brynjolfsson and McAfee, 2014) as defined by many economists and sociologists. The so-called Fourth Industrial Revolution (Schwab, 2014) is a paradigm shift enabled by the convergence of technological changes - biotech, nanotech, 3D printing, robotics, big data and AI - that significantly influence the nature of work, the design and materialization of products and services, as well as their market, their structure, and their relations with human agents. This systemic process also changes the design field, its cultural and socio-economic structures, its traditional domains, and its consolidated practices. We witness both new opportunities for, but threats to, the conventional system of human imaginative and operational capacities that are changing how they can be learned. The re-discussion of the design(er) role affects the structure and meaning of the discipline, as well as the processes, places, and capacities that can generate learning. Design education is a core component of this change. It is so for those who will be shortly become designers and for retrofitting the knowledge and skills of practitioners and educators. This article reviews the principal studies and theories on the transformation of the production system and the market. Its focus is on the structural factors which enable identification of the leading transformational drivers of the experimental-experiential learning which will become the basis upon which changes in design education and design/designer skills will be defined considering the growth of open and distributed socio-technical systems in our contemporary society

Facing the Fourth Industrial Revolution: empowering (human) design agency and capabilities through experimental learning

This article identifies and describes the transformation of designer skills within the Great Transformation (Brynjolfsson and McAfee, 2014) as defined by many economists and sociologists. The so-called Fourth Industrial Revolution (Schwab, 2014) is a paradigm shift enabled by the convergence of technological changes - biotech, nanotech, 3D printing, robotics, big data and AI - that significantly influence the nature of work, the design and materialization of products and services, as well as their market, their structure, and their relations with human agents. This systemic process also changes the design field, its cultural and socio-economic structures, its traditional domains, and its consolidated practices. We witness both new opportunities for, but threats to, the conventional system of human imaginative and operational capacities that are changing how they can be learned. The re-discussion of the design(er) role affects the structure and meaning of the discipline, as well as the processes, places, and capacities that can generate learning. Design education is a core component of this change. It is so for those who will be shortly become designers and for retrofitting the knowledge and skills of practitioners and educators. This article reviews the principal studies and theories on the transformation of the production system and the market. Its focus is on the structural factors which enable identification of the leading transformational drivers of the experimental-experiential learning which will become the basis upon which changes in design education and design/designer skills will be defined considering the growth of open and distributed socio-technical systems in our contemporary society

AI-Driven Decision Support Systems in Management: Enhancing Strategic Planning and Execution

Artificial intelligence (AI) is transforming strategic decision-making processes across various industries. Organizations increasingly rely on AI-driven decision support systems that leverage massive amounts of data and real-time analytics to enable more informed planning and predictive capabilities. However, less focused research has explored the integration and impact of such tools specifically within managerial strategy and execution contexts. This study conducts qualitative and quantitative analysis on the deployment of machine learning-based recommendation systems aimed at enhancing the strategic capabilities of management teams. Results indicate that AI decision tools led to improved analytic capacities, competitive response times, and reimagined vision planning, yet also posed transparency and trust challenges around advanced automation techniques. Findings provide novel implications into AI’s emerging role in augmenting and extending higher-level organizational strategy design and enactment by key decision-makers and leaders. Future directions are discussed related to addressing responsible development issues as adoption continues accelerating

National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)

As we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp

Business Process Simulation: A Systematic Literature Review

Business process simulation marks an essential Business Process Management technique for analysing business processes and for reasoning about process improvement. Despite its importance, literature is lacking a comprehensive, updated overview of research contributions to the field of business process simulation. In this systematic literature review, we assess the present state of research on business process simulation including prior work between 1990 and 2016. Results reported in the present study assist in advancing the discussion on future research on business process simulation by compiling and analysing prior work. The present literature review focuses on prior research involving conceptual business process models, e.g., BPMN models, with a graphical model representation as a starting point for business process simulation and excludes other foundations to build simulation models

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin