Artificial Intelligence as an Emerging Technology in Global Trade

The paper proposes conceptual model of a digital platform for cyber-physical management of modern enterprises in the upcoming era of Industry 5.0. Unlike Industry 4.0, which focuses on automation of physical processes, Industry 5.0 is oriented on digitization of knowledge and automation of reasoning processes for creating artificial intelligence that is able to manage enterprises. This still emerging era will be characterized by the vision of any business, including industrial production or logistics, as a complex adaptive system built on fundamental principles of self-organization and evolution, as well as interaction of artificial intelligence systems and humans. The paper shows that implementation of such production and logistics management systems will require development of new models and decision-making methods based on knowledge and semantic information processing, integration of computational and communication components, accumulation of big data and its processing for predictive analytics, blockchain technologies for fixing mutual obligations of systems components in the for m of smar t contracts, as well as human-machine and software inter faces. Existing approaches to creation of digital platforms within the digital economy of Industry 4.0 and their limitations are analyzed. The concept of digital ecosystem is developed as an open, distributed, self-organized "system of systems" of smart services capable of coming up with solutions and automatically resolving conflicts through negotiations and concessions. The concept of the digital platform within Industry 5.0 is described, which will be able to support functioning of the digital ecosystem of "smart services" of cyberphysical management of both individual objects and enterprises of humans and robots, and in the future, industries of such enterprises — implemented using self-organizing autonomous agents at all levels.

This chapter explains the development and significance of China’s mega supply chains and their position in the global division of labour. It explains the importance of modularity, standardization, and complementarity in supply chains. It analyses how efficiencies and resilience are achieved and balanced in supply chains and the importance of platforms, both geographical clusters, such as industrial bases in Shenzhen, Chengdu, and Suzhou, and digital platforms, such as Alibaba and Pinduoduo. The chapter also argues that China’s mega supply chains have become regional hubs supplying intermediate products to manufacturing facilities in countries with lower labour costs. It discusses the extent to which China is progressing towards Industry 4.0, with smart supply chains, and how the country is responding to the challenges from growing global trade tensions.

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.

The term Industry 4.0 has become increasingly pervasive in the context of industrial manufacturing and it has been considered the fourth industrial revolution (Henning [1]) [...]

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.