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Josephson junctions act as active elements in superconducting electronics. The behavior of this nonlinear element is characterized by the relation between current and the quantum mechanical phase‐difference. For an accurate device modeling, detailed knowledge about this relation is necessary. This paper aims to discuss these issues.

To obtain detailed information, a method for DC measurement of the current‐phase relation suitable for all kinds of superconducting circuit elements was accomplished.

The authors developed a linear transformation algorithm to calculate the current‐phase relation from the measured data.

It turns out that in future designs additional connections and special test structures are required to gain more knowledge about inductance values required for the algorithm.

Based on the inverse calculation of that algorithm, the authors found a 7 percent deviation of the current‐phase relation of a standard superconductor/insulator/superconductor Josephson junction from the predicted sine‐wave behavior. Furthermore, the paper suggests to use this method to evaluate the current‐phase relation of new Josephson elements such as a superconductor/ferromagnet/superconductor junction. Therefore, the authors will deposit the new element directly on the chip with the test setup fabricated with standard Nb‐technology.

The purpose of this paper is to identify the key performance factors that can lead toward sustainability in the Industry 4.0 supply chains of manufacturing industries.

Questionnaire is used to collect the data from manufacturing sector to prioritize the factors, which integrates both Industry 4.0 and sustainability. For this, stepwise weight assessment ratio analysis (SWARA) method is used to obtain the weights for criteria and sub-criteria to prioritize the factors.

The present study brings the findings about five key performance factors. Social factor needs much attention among all the criteria, followed by ecological, economic, information technology and dynamic capability theory. Further, change management, third-party audits and novel business models are key sub-factors to improve performance of sustainability in Industry 4.0 supply chains.

This study prioritized the performance factors of Industry 4.0 and sustainable supply chain in Indian manufacturing sector. These prioritized factors help to improve performance of organizations, which are practicing the Industry 4.0 and sustainability practices. Managers in manufacturing industries can use the SWARA for assessment of weights for the criteria and sub-criteria factors to take appropriate decisions to improve the organizations’ performance.

Managers in manufacturing industry can use these prioritized factors to improve the performance of their supply chains.

In today’s uncertain business environment, Industry 4.0 is regarded as a viable strategic plan for addressing a wide range of manufacturing-related challenges. However, it appears that its level of adoption varies across many countries. In the case of a developing economy like India, practitioners are still in the early stages of implementation. The implementation of Industry 4.0 appears to be complex, and it must be investigated holistically in order to gain a better understanding of it. Therefore, an attempt has been made to examine the Industry 4.0 implementation for the Indian manufacturing organization in a detailed way by analyzing the complexities of relevant variables.

SAP-LAP (situation-actor-process and learning-action-performance) and an efficient interpretive ranking process (e-IRP) were used to analyze the various variables influencing Industry 4.0 implementation. The variables were identified, as per SAP-LAP, through a thorough review of the literature and based on the perspectives of various experts. The e-IRP has been used to prioritize the selected elements (i.e. actors with respect to processes and actions with respect to performance) of SAP-LAP.

This study ranked five stakeholders according to their priority in Industry 4.0 implementation: government policymakers, industry associations, research and academic institutions, manufacturers and customers. In addition, the study also prioritized important actions that need to be taken by these stakeholders.

The results of this study would be useful in identifying and managing the various actors and actions related to Industry 4.0 implementation. Accordingly, their prioritized sequence would be useful to the practitioners in preparing the well-defined and comprehensive strategic roadmap for Industry 4.0.

This study has adopted qualitative and quantitative approaches for identifying and prioritizing different variables of Industry 4.0 implementation. This, in turn, helps the stakeholder to comprehend the concept of Industry 4.0 in a much simpler way.

Increasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.

First, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.

The paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.

The presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.

The paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.