Industry 5.0: A survey on enabling technologies and potential applications

In the modern business environment, characterized by rapid technological advancements and globalization, abetted by IoT and Industry 5.0 phenomenon, innovation is indispensable for competitive advantage and economic growth. However, many organizations are facing problems in its true implementation due to the absence of a practical innovation management framework, which has made the implementation of the concept elusive instead of persuasive. The present study has proposed a new innovation management framework labeled as “Absolute Innovation Management (AIM)” to make innovation more understandable, implementable, and part of the organization’s everyday routine by synergizing the innovation ecosystem, design thinking, and corporate strategy to achieve competitive advantage and economic growth. The current study used an integrative literature review methodology to develop the “Absolute Innovation Management” framework. The absolute innovation management framework links the innovation ecosystem with the corporate strategy of the firm by adopting innovation management as a strategy through design thinking. Thus, making innovation more user/human-centered that is desirable by the customer, viable for business and technically feasible, creating both entrepreneurial and customer value, and boosting corporate venturing and corporate entrepreneurship to achieve competitive advantage and economic growth while addressing the needs of IoT and Industry 5.0 era. In sum, it synergizes innovation, design thinking, and strategy to make businesses future-ready for IoT and industry 5.0 revolution. The present study is significant, as it not only make considerable contributions to the existing literature on innovation management by developing a new framework but also makes the concept more practical, implementable and part of an organization’s everyday routine.

https://www.mdpi.com/2078-2489/11/2/124/pdf

Innovation in the Era of IoT and Industry 5.0: Absolute Innovation Management (AIM) Framework

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL) Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT Finally, we present several future research directions to inspire and motivate further research in this area

Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

The purpose of the article is to determine the possibilities and limitations of managing the global financial system on the basis of artificial intelligence. For determining the reasons of the recent global financial crisis and for determining the opportunities and limitations of overcoming the crisis with the help of modernization of this system’s management with the help of artificial intelligence, the authors use the method of SWOT analysis. For describing the essence of the process of managing the global financial system on the basis of artificial intelligence, the method of modeling of economic systems and the method of formalization are used. As a result, the authors come to the conclusion that the working hypothesis is correct – artificial intelligence does possess large potential in the sphere of stimulation of anti-crisis development of the global financial system. Allowing overcoming the influence of “human factor” on development of this system, which was one of the main reasons of its recent crisis, artificial intelligence can ensure stable functioning, high effectiveness, and sustainable growth of the global financial system. Despite the wide possibilities for optimization of management of the global financial system on the basis of artificial intellect, including rationalization of decision making, programming of social responsibility of economic subjects, expansion of opportunities in the sphere of analysis and forecasting of long-term consequences of their actions and market events, and elimination of dependence of financial markets on the influence of social tendencies, this process is peculiar for certain limitations. In order to overcome them, the authors offer a conceptual model of managing the global financial system on the basis of artificial intelligence.

Managing the Global Financial System on the Basis of Artificial Intelligence: Possibilities and Limitations

There are several areas in which organisations can adopt technologies that will support decision-making: artificial intelligence is one of the most innovative technologies that is widely used to assist organisations in business strategies, organisational aspects and people management. In recent years, attention has increasingly been paid to human resources (HR), since worker quality and skills represent a growth factor and a real competitive advantage for companies. After having been introduced to sales and marketing departments, artificial intelligence is also starting to guide employee-related decisions within HR management. The purpose is to support decisions that are based not on subjective aspects but on objective data analysis. The goal of this work is to analyse how objective factors influence employee attrition, in order to identify the main causes that contribute to a worker’s decision to leave a company, and to be able to predict whether a particular employee will leave the company. After the training, the obtained model for the prediction of employees’ attrition is tested on a real dataset provided by IBM analytics, which includes 35 features and about 1500 samples. Results are expressed in terms of classical metrics and the algorithm that produced the best results for the available dataset is the Gaussian Naive Bayes classifier. It reveals the best recall rate (0.54), since it measures the ability of a classifier to find all the positive instances and achieves an overall false negative rate equal to 4.5% of the total observations.

Predicting Employee Attrition Using Machine Learning Techniques

The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to do if framework conditions changed unexpectedly? The purpose of the paper is to analyse how the digital transformation will impact the Business Process Management (BPM) while using methods like machine learning or artificial intelligence. Therefore, the core components will be explained, compared and set up in relation. To identify application areas interviews and analysis will be held up with digital companies. The finding of the paper will be recommendation for action in the field of BPM and process optimization through machine learning and artificial intelligence. The Approach of optimizing and management processes via machine learning and artificial intelligence will support companies to decide which tool will be the best for automated BPM.

/pdf/automated-business-process-management-in-times-of-digital-ukj7y0scm2.pdf

Automated business process management – in times of digital transformation using machine learning or artificial intelligence

While the most companies are still struggling with the digitalization of their business through the integration of artificial intelligence (AI), Internet of Things (IoT), cloud technologies and further developed technology the next step of Industrial Revolution appears in the nearest future. Industry 5.0 will step into the future daily business due to the velocity of further technological development and changing human process integration. In this context, the paper analyses and evaluate the business impact of the next Industrial Revolution, so call Industry 5.0. Therefore, the actual Industry 4.0 business transformation record of accomplishment as well as weakness and threats are analysed by interviews with experts and business representatives. The first result is the business situation analysis to identify existing gabs with the derivation of opportunities and threats as well as suggestions for the business how to transform best in times of the next Industrial Revolution. Furthermore, the approach of integrate human workers back into the supply chain next to automated processes is debated. This paper underlines the assumption that companies did not recognize, yet the next Industrial Revolution because of the lack of entrepreneurship and transformation capacity related to Industry 4.0.

Industry 5.0 – The Expected Impact Of Next Industrial Revolution

Knowledge Management – The Foundation for a Successful Business Process Management

Corporate Development with Agile Business Process Modeling as a Key Success Factor

This chapter presents an exhaustive reference analysis and synthesis about the Industry 5.0 development, challenges, and implications. First, a state of the art on Industry 4.0 and digitalization will be presented considering the Industrial Revolution stages. Currently, manufacturing and business processes are increasingly using Cloud and mobile technologies, Internet of Things (IoT), Artificial Intelligence (AI), and Data Analytics, which heralds the fifth Industrial Revolution (Industry 5.0). Consequently, in the second part of the chapter, there will be presented synthesis research drawing the state-of-the-art overview on Industry 5.0, as a core idea of this chapter. Furthermore, a detailed characterization will be done to identify the differences and similarities from Industry 4.0 to Industry 5.0 and Society 5.0 by gaining benefits, advantages, and disadvantages as well as expected business value and ethical implications. 

Daniel Paschek

Papers

Automated business process management – in times of digital transformation using machine learning or artificial intelligence

Industry 5.0 – The Expected Impact Of Next Industrial Revolution

Knowledge Management – The Foundation for a Successful Business Process Management

Corporate Development with Agile Business Process Modeling as a Key Success Factor

Industry 5.0 Challenges and Perspectives for Manufacturing Systems in the Society 5.0