The concept of “Industry 4.0” is expected to bring a multitude of benefits for industrial value creation. However, the associated risks hamper its implementation and lack a comprehensive overview. In response, the paper proposes a framework of risks in the context of Industry 4.0 that is related to the Triple Bottom Line of sustainability. The framework is developed from a literature review, as well as from 14 in-depth expert interviews. With respect to economic risks, the risks that are associated with high or false investments are outlined, as well as the threatened business models and increased competition from new market entrants. From an ecological perspective, the increased waste and energy consumption, as well as possible ecological risks related to the concept “lot size one”, are described. From a social perspective, the job losses, risks associated with organizational transformation, and employee requalification, as well as internal resistance, are among the aspects that are considered. Additionally, risks can be associated with technical risks, e.g., technical integration, information technology (IT)-related risks such as data security, and legal and political risks, such as for instance unsolved legal clarity in terms of data possession. Conclusively, the paper discusses the framework with the extant literature, proposes managerial and theoretical implications, and suggests avenues for future research.

Strategic organizational decision making in today’s complex world is a dynamic process characterized by uncertainty. Therefore, diverse groups of responsible employees deal with the large amount and variety of information, which must be acquired and interpreted correctly to deduce adequate alternatives. The technological potential of artificial intelligence (AI) is expected to offer further support, although research in this regard is still developing. However, as the technology is designed to have capabilities beyond those of traditional machines, the effects on the division of tasks and the definition of roles established in the current human–machine relationship are discussed with increasing awareness. Based on a systematic literature review, combined with content analysis, this article provides an overview of the possibilities that current research identifies for integrating AI into organizational decision making under uncertainty. The findings are summarized in a conceptual model that first explains how humans can use AI for decision making under uncertainty and then identifies the challenges, pre-conditions, and consequences that must be considered. While research on organizational structures, the choice of AI application, and the possibilities of knowledge management is extensive, a clear recommendation for ethical frameworks, despite being defined as a crucial foundation, is missing. In addition, AI,other than traditional machines, can amplify problems inherent in the decision-making process rather than help to reduce them. As a result, the human responsibility increases, while the capabilities needed to use the technology differ from other machines, thus making education necessary. These findings make the study valuable for both researchers and practitioners.

Close and novel forms of collaboration among supply chain stakeholders within and beyond industry boundaries are essential for implementing a circular economy. Industry 4.0 technologies, as recognised enablers for circular systems, can support such collaboration. This study explores the intersection of the circular economy, supply chain collaboration, and Industry 4.0. First, we derive an analytical framework that contextualises circular supply chain collaboration. Second, based on this framework, we conduct a systematic literature review that explores how Industry 4.0 technologies can enable collaboration mechanisms. The review shows that information sharing and joint planning and decision-making are the most common collaboration mechanisms studied. Simultaneously, the Internet of Things, Blockchain, and Cloud Systems are the most discussed technologies to enable those mechanisms. The most prominent review sample patterns are synthesised into three archetypes for Industry 4.0-enabled circular supply chain collaboration. Furthermore, the study derives nine promising questions for future research along six main dimensions: Digital Twins and Artificial Intelligence, joint knowledge creation and innovation, collaboration in product design and planning, systemic collaboration across industry boundaries, contextualisation with barriers for implementing circular supply chains, and transferability of applications across industries.

Purpose: The purpose of this paper is to offer a comprehensive overview of challenges and risks of the Internet of Things (IoT) in supply chain management (SCM) and provide a structured framework for classifying and analyzing the relevant literature to deduce insights for research and practice.Design/methodology/approach: A systematic literature review (SLR) of 102 peer-reviewed journal articles on the topic of IoT in SCM was conducted.Findings: This review identifies, categorizes and describes the relevant literature regarding the dimensions time and specificity. The resulting framework contains a holistic overview including focus areas and relations of macro-environmental, network-related and organizational challenges and risks. Furthermore, this review represents a conceptual framework for future research, considering the multidisciplinary body of the subject and provides an analysis of the timeline of literature, journals and used methodologies.Practical implications: This study offers valuable insights into IoT and its impact on applications, processes and the structure of organizations and supply chains (SCs). The identification of challenges and risks helps to prepare for changes in the future because of the disruptive nature of this technology.Originality/value: The originality of this paper lies in the synthesis of the fragmented body of literature of IoT challenges and risks for SCM. The derived framework and identification of relations and research gaps provide the foundation for further research to evaluate the relations between challenges and risks and offer solutions.