This paper is a first step to understand the role that a smart city with a distributed production system could have in changing the nature and form of supply chain design. Since the end of the Second World War most supply chain systems for manufactured products have been based on "scale economies" and "bigness"; in our paper we challenge this traditional view. Our fundamental research question is: how could a smart city production system change supply chain design? In answering this question we develop an integrative framework for understanding the interplay between smart city technological initiatives (big data analytics, the industrial internet of things) and distributed manufacturing on supply chain design.This framework illustrates synergies between manufacturing and integrative technologies within the smart city context and links with supply chain design.Considering that smart cities are based on the collaboration between firms, endusers and local stakeholders, we advance the present knowledge on production systems through case study findings at the product level. In the conclusion, we stress there is a need for future research to empirically develop our work further and measure (beyond the product level), the extent to which new production technologies such as distributed manufacturing, are indeed democratizing supply chain design and transforming manufacturing from "global production" to a future "city-oriented" social materiality.

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.

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PurposeThe emergence of distributed manufacturing (DM) is examined as a new form of localised production, distinct from previous manifestations of multi-domestic and indigenous production.Design/methodology/approachSupply network (SN) configuration and infrastructural provisioning perspectives were used to examine the literature on established localised production models as well as DM. A multiple case study was then undertaken to describe and explore the DM model further. A maximum variation sampling procedure was used to select five exemplar cases.FindingsThree main contributions emerge from this study. First, the research uniquely brings together two bodies of literature, namely SN configuration and infrastructure provisioning to explore the DM context. Second, the research applies these theoretical lenses to establish the distinctive nature of DM across seven dimensions of analysis. Third, emerging DM design rules are identified and compared with the more established models of localised production, drawing on both literature and DM case evidence.Practical implicationsThis study provides a rich SN configuration and infrastructural provisioning view on DM leading to a set of design rules for DM adoption, thus supporting practitioners in their efforts to develop viable DM implementation plans.Originality/valueThe authors contribute to the intra- and inter-organisational requirements for the emerging DM context by providing new perspectives through the combined lenses of SN configuration and infrastructural provisioning approaches.

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