Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on production scheduling. However, there is still a lack of comprehensive research on the applications of I4.0 enabling technologies in manufacturing life-cycle processes. This paper is thus intended to provide a systematic literature review answering the following research question: What are the applications of I4.0 enabling technologies in the business processes of manufacturing companies? The study analyses 186 articles and the results show that production scheduling and control is the process most often investigated, while there is also an increasing trend in servitization and circular supply chain management. Moreover, there is extensive combined use of IoT, Big Data Analytics and Cloud, whose applications cover a wide range of processes. On the contrary, other technology like Blockchain is not as widely discussed in the domain of I4.0. This picture calls for a future research agenda extending the scope of investigation into I4.0 in manufacturing. Furthermore, the results of this research can prove extremely useful for practitioners who wish to implement one or more technologies, providing them with solutions for applications in manufacturing.

The role of digital technologies in service business transformation is under-investigated. This paper contributes to filling\ud this gap by addressing how the Internet of things (IoT), cloud computing (CC) and predictive analytics (PA) facilitate\ud service transformation in industrial companies. Through the Data–Information–Knowledge–Wisdom (DIKW) model, we\ud discuss how the abovementioned technologies transform low-level entities such as data into information and knowledge\ud to support the service transformation of manufacturers. We propose a set of digital capabilities, based on the extant\ud literature and the findings from four case studies. Then, we discuss how these capabilities support the service transformation\ud trajectories of manufacturers. We find that IoT is foundational to any service transformation, although it is mostly\ud needed to become an availability provider. PA is essential for moving to the performance provider profile. Besides\ud providing scalability in all profiles, CC is specifically used to implement an industrialiser strategy, therefore leading to\ud standardised, repeatable and productised offering

This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on organisational decision-making. Specifically, the study addresses three key research questions: RQ1: How is (generative) AI adopted within KM processes in organisations? RQ2: What factors influence the adoption of AI in these processes, either facilitating or inhibiting it? RQ3: How does AI adoption in KM processes affect organisational decision-making? An explorative investigation has been conducted through semi-structured interviews with KM and AI experts from a worldwide sample of 52 mostly private, large and for-profit organisations. Interviews have been analysed through a mixed thematic analysis. The study provides an original framework in which the three investigated concepts are interconnected according to a dual relationship: linear and retroactive and 20 factors affecting AI adoption within KM processes. The provided model guides managers in improving their organisational decision-making through AI adoption in KM processes. Moreover, according to the rational decision-making model, the authors propose a six-step systematic procedure for managers. To the best of the authors’ knowledge, this is the first study that simultaneously addresses AI, KM and decision-making and provides an integrated framework showing the relationships between them, allowing organisations to better and practically understand how to ameliorate their decision-making through AI adoption in KM processes.

Purpose: This paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).Design/methodology/approach: In the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.Findings: Using a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.Practical implications: This study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.Originality/value: This study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.

Purpose: The purpose of this paper is to investigate how much the Italian manufacturing companies are ready to be concretely involved in the so-called “Industry 4.0” (I4.0) journey. In particular, this paper focuses on analyzing the knowledge and adoption levels of specific I4.0 enabling technologies, also considering how organizations are involved and which are the main benefits and obstacles. Design/methodology/approach: A descriptive survey has been carried out on a total of 103 respondents related to manufacturing companies of different sizes. Data collected were analyzed in order to answer five specific research questions. Findings: The findings from the survey demonstrate that Italian manufacturing companies are in different positions in their journey toward the I4.0 paradigm, mainly depending on their size and informatization level. Furthermore, not all the business functions are adequately involved in this transformation and their awareness about this new paradigm seems quite low because of the absence of specific managerial roles to guide this revolution. Finally, there are strong differences concerning both benefits and obstacles related to the adoption of I4.0 paradigm, depending on the technology adoption level. Research limitations/implications: Future research should focus on developing case studies about pilot I4.0 practitioners in order to understand the root cause of successful cases. Both managerial and practical references should be developed, helping Italian manufacturing enterprises to consolidate and strengthen their position in global competitive market. Finally, it would be interesting to carry out the same study in other countries in order to make comparisons and suitable benchmark analyses. Originality/value: Despite scholars have debated about the adoption of technologies and the benefits related to the I4.0 paradigm, to the best of authors’ knowledge, only a few empirical surveys have been carried out until now on the adoption level of I4.0 principles in the manufacturing sector of a specific country.

Purpose: This study aims to enhance the theoretical foundations of servitization research by establishing a theoretical connection with complexity management. The authors develop a conceptual framework to describe complexity management mechanisms in servitization and digital platforms' specific role in allowing synergies between complexity reduction and absorption mechanisms.Design/methodology/approach: A theory adaptation approach is used. Theory adaptation introduces new perspectives and conceptualization to the domain theory (servitization, with a focus on the role of digital platforms) by informing it with a method theory (complexity management).Findings: This study provides four key contributions to the servitization literature: (1) connecting the servitization and complexity-management terminologies, (2) identifying and classifying complexity-management mechanisms in servitization, (3) conceptualizing digital platforms' role in servitization complexity management and (4) recognizing digital platforms' complexity-management synergies.Originality/value: This study highlights that by using digital platforms in servitization and understanding the platform approach more thoroughly, companies can gain new capabilities and opportunities to manage and leverage complexity.