This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

COVID-19 pandemic has created disruptions and risks in global supply chains. Big data analytics (BDA) has emerged in recent years as a potential solution for provisioning predictive and pre-emptive information to companies in order to preplan and mitigate the impacts of such risks. The focus of this article is to gain insights into how BDA can help companies combat a crisis like COVID-19 via a multimethodological scientific study. The advent of a crisis like COVID-19 brings with it uncertainties, and information processing theory (IPT) provides a perspective on the ways to deal with such uncertainties. We use IPT, in conjunction with the Crisis Management Theory, to lay the foundation of the article. After establishing the theoretical basis, we conduct two surveys towards supply chain managers, one before and one after the onset of the COVID-19 pandemic in India. We follow it up with qualitative interviews to gain further insights. The application of multiple methods helps ensure the triangulation of results and, hence, enhances the research rigor. Our research finds that although the current adoption of BDA in the Indian industry has not grown to a statistically significant level, there are serious future plans for the industry to adopt BDA for crisis management. The interviews also highlight the current status of adoption and the growth of BDA in the Indian industry. The article interestingly identifies that the traditional barriers to implementing new technologies (like BDA for crisis management) are no longer present in the current times. The COVID-19 pandemic has hence accelerated technology adoption and at the same time uncovered some BDA implementation challenges in practice (e.g., a lack of data scientists).

Energy Saving by Blockchaining Maintenance