Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Nursing Research—Principles and Methods

In the era of industrial digitalization, companies are increasingly investing in tools and solutions that allow their processes, machines, employees, and even the products themselves, to be integrated into a single integrated network for data collection, data analysis, the evaluation of company development, and performance improvement. To study the impact of Industry 4.0 on the company we used Porter’s (1985) value chain model, which is particularly useful when paying particular attention to corporate areas which have a primary role in customer value creation. Since the primary impact of Industry 4.0 is perceived in value-creating processes, and has so far had the greatest transformative effect in this area, the model can be considered to be appropriate. The objective of our research is to discover how companies operating in Hungary interpret the phenomenon of Industry 4.0, what Internet of Things (IoT) tools they use to support their processes, and what critical issues they face during adaptation. We applied a dual methodology in our investigation: We sent an online questionnaire to manufacturing and logistical service companies to investigate the IoT tools they use, and the problems they face, and received 43 answers we could evaluate. We also conducted four expert interviews with manufacturing firms to get deeper insights into the application, critical issues and development phases of IoT tools. During our research, we found that the spread of real-time data across companies—given the availability of appropriate analytical tools and methods—can have a significant impact on the entire company. In the case of CPS (Cyber Physical System), CPPS and Big Data Technologies, companies using them have been evaluated as having a higher level of logistic service, more efficient processes with their partners, improved cooperation between certain logistic functions, and higher market and financial performance and competitiveness. Applying more efficient production processes, and achieving better productivity and economies of scale, might also result in increased economic sustainability. Furthermore, we have found that companies have started on the path to digital evolution, and investments of this type have already begun.

The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary

Recent developments in production processes and their automation have led to the definition of the Fourth Industrial Revolution, commonly known as “Industry 4.0”. Industry 4.0 is a very broad domain that includes: production processes, efficiency, data management, relationship with consumers, competitiveness, and much more. At the same time, obviously, Industry 4.0 has become a new theme for management scholars and business economics disciplines and a number of contributions covering various issues and aspects have been published. However, a systematic formulation of all these contributions is still lacking in management literature. Therefore, the aim of the paper is to analyze and classify the main contributions published on the topic of Industry 4.0 in management literature, seeking to give it a unique definition, discover the gaps still remaining in literature and outline future avenues of research in this domain. A systematic review of the literature of the major academic and research databases has been used as methodology to achieve the aim of the paper. This work contributes theoretically to the development of literature on Industry 4.0 and from a managerial perspective it could support entrepreneurs in better understanding the implications and fields of application of the Fourth Industrial Revolution as well as the interplay among them.

Industry 4.0 in Management Studies: A Systematic Literature Review

Behind the definition of Industry 4.0: Analysis and open questions

Advanced manufacturing solutions, augmented reality, and cloud and big data are technologies pertaining to Industry 4.0. These technologies improve working conditions, create new business models, and increase both productivity and firm quality production. However, they can also improve life and society as a whole. This new perspective, oriented toward social and global well-being, is called Society 5.0. As has happened for all past industrial revolutions, Industry 4.0 will support the transition to a different society, i.e., Society 5.0. In this transition, open innovation and value co-creation can play an important role. The aim of the study was twofold: to examine how Industry 4.0 features and enabling technologies can support the transition to Society 5.0 and to investigate the roles of both open innovation and value co-creation within this transition. A conceptual framework was developed to jointly consider for the first time Industry 4.0, Society 5.0, open innovation, and value co-creation, which are all challenging issues that firms must cope with nowadays. Managers could profit from these insights to design ad hoc strategies in order to benefit from the opportunities emerging from this transition and overcome the main related challenges.

The Role of Open Innovation and Value Co-creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework

In 2020, the whole world had to face a pandemic with inevitable profound changes in all aspects of life, from the social to the economic sphere. The profound economic crisis that followed the rise of the pandemic has pushed firms and researchers to question the necessary changes and new challenges for the survival of businesses. In this scenario, the aim of the paper is to analyze and classify the main contributions published on the topic of COVID-19 in managerial literature, seeking to discover the perspective and the gaps and outline future avenues of research. A systematic review of the literature has been performed. The results highlight the orientation of studies in this field and the various links between different aspects that emerged. Limitations and implications complete the research.

https://www.mdpi.com/2071-1050/13/7/3791/pdf

COVID-19 in Management Studies: A Systematic Literature Review

Start-ups, among other enterprises, play a major role in the development and/or commercialization of new technologies and the development of national economies, given that firms are the innovation locus for an entire society. In Italy, a recent regulatory intervention has focused on start-ups creating a framework where innovative start-ups are defined and regulated. Among innovative start-ups, those with a social vocation are of particular interest, since they are understudied in the literature. Indeed, the aim of this paper is twofold: to analyze the relationship between social innovation and sustainability in the latter businesses, and try to understand how sustainability could be fostered through them. Italian cases of innovative start-ups will be studied through content analysis applied to the Social Impact Assessment Document provided by firms. Results show that the Social Impact Assessment Document provided by innovative start-ups explicitly pays attention to social innovation and sustainability in different ways. However, the document does not show the link between social innovation and sustainability. Nonetheless, going through these documents, the link between social innovation and the three aspects of sustainability (economic, social and environmental) clearly emerge and therefore could be better managed.

Michela Piccarozzi

Papers

Industry 4.0 in Management Studies: A Systematic Literature Review

The Role of Open Innovation and Value Co-creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework

COVID-19 in Management Studies: A Systematic Literature Review

Does Social Innovation Contribute to Sustainability? The Case of Italian Innovative Start-Ups

Is this a new story of the ‘Two Giants’? A systematic literature review of the relationship between industry 4.0, sustainability and its pillars