Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.

Smart manufacturing is considered as a new paradigm that makes work smarter and more connected, bringing speed and flexibility through the introduction of digital innovation. Today, digital innovation is closely linked to the “sustainability” of companies. Digital innovation and sustainability are two inseparable principles that are based on the concept of circular economy. Digital innovation enables a circular economy model, promoting the use of solutions like digital platforms, smart devices, and artificial intelligence that help to optimize resources. Thus, the purpose of the research is to present a systematic literature review on what enabling technologies can promote new circular business models. A total of 31 articles were included in the study. Our results showed that realization of the circular economy involved two main changes: (i) managerial changes and (ii) legislative changes. Furthermore, the creation of the circular economy can certainly be facilitated by innovation, especially through the introduction of new technologies and through the introduction of digital innovations.

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Today, companies’ success is increasingly linked to their ability to manage agreements, alliance and networks. This article focuses on performance measurement systems in business networks, and develops a literature review in order to understand the state of the art of this topic. The scope of the research is to understand how PMSs have to be designed and used in different types of networks. A framework on the relationship between networks features and PMSs characteristics has been designed and three features of business networks relevant for performance measurement design have been identified: nature of partners, nature of relationships, nature of shared resources. The study shows that in the last decades ‘business networks’, on one hand, and PMS, on the other hand, have been widely studied but little attention has been given to the issue of performance measurement in a network context

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.