The fourth Industrial Revolution is driving the creation of fully connected ecosystem. Organizations are now reshaping their strategies to become fully transparent, including their supply chain management. The area of supply chain digitalisation is starting to attract growing attention; however, its research status remains unclear. We set out this study to understand what constitutes the underlying structure of its research, what topics have been investigated, what areas need further attention, how the existing literature can be classified, and how the discipline can move forward. We applied a mixed-method approach using both quantitative and qualitative techniques to achieve this. A bibliometric analysis of 331 articles with 12709 references was first conducted to discover the underlying knowledge foundation and evolution of supply chain digitalisation, current attention, and grouping of research into distinct clusters. Further, a qualitative review through content analysis was performed to interrogate our quantitative results. Research implications, and directions for future research are also discussed.

Purpose The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model. Design/methodology/approach A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. Findings This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers. Research limitations/implications The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities. Practical implications The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013). Social implications The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system. Originality/value Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

Purpose The application of digital twins to optimise operations and supply chain management functions is a bourgeoning practice. Scholars have attempted to keep pace with this development initiating a fast-evolving research agenda. The purpose of this paper is to take stock of the emerging research stream identifying trends and capture the value potential of digital twins to the field of operations and supply chain management. Design/methodology/approach In this work we employ a bibliometric literature review supported by bibliographic coupling and keyword co-occurrence network analysis to examine current trends in the research field regarding the value-added potential of digital twin in operations and supply chain management. Findings The main findings of this work are the identification of four value clusters and one enabler cluster. Value clusters are comprised of articles that describe how the application of digital twin can enhance supply chain activities at the level of business processes as well as the level of supply chain capabilities. Value clusters of production flow management and product development operate at the business processes level and are maturing communities. The supply chain resilience and risk management value cluster operates at the capability level, it is just emerging, and is positioned at the periphery of the main network. Originality/value This is the first study that attempts to conceptualise digital twin as a dynamic capability and employs bibliometric and network analysis on the research stream of digital twin in operations and supply chain management to capture evolutionary trends, literature communities and value-creation dynamics in a digital-twin-enabled supply chain.

This is a repository copy of Do makerspaces represent scalable production models of community based redistributed manufacturing?.

This paper investigates the impact of human and political capitals of entrepreneurs on enterprise performance in four emerging nations.The rent generation potential of these capitals is a well established fact, however, much less is known concerning the contingent nature of their value creation prowess. In this work, we draw on institutional theory and dynamic managerial capabilities perspective to examine the interactive effect of country of origin economic developement level and the international experience of entrepreurs, on the capitals, with respect to a set of financial indicators. Employing a quantitative methodology, our findings reveal that the relationship between the capitals and enterprise performance are indeeed contingent with the capitals of home-grown entrepreneurs, rather than those of returnee migrant entrepreneurs, exhibiting a greater propensity to influence enterprise performance. We conclude with implications for theory and practice.