دانلود رایگان مقاله انگلیسی زنجیره تامین داده (DSC): ترکیب تحقیق و جهت گیری آینده - تیلور و فرانسیس 2017

In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the pre-existing SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies.

6. Conclusion

DSCs have emerged relatively recently8 as data evolution expands business scope, disrupts existing operating models, changes industries and provides opportunities to work solely on data as the main ‘raw material’. Evolution of data and its processing, exchange and reselling transform the organisational and operational landscape and render the conceptualisation of DSC highly relevant. Waller and Fawcett (2013) proposed the use of data in SC management for improvement and expansion of the production (through the use of analytical skills for optimisation and visualisation of the SCs of their core business). Although data can be used along with the core business processes in different industries where we find data about the SCs, we put our specific focus on data around the SCs; and we emphasise the difference between data utilised to improve SC processes vs. data used as the main artefact. We find that the literature that considers data as the main artefact focuses on technical solutions and challenges around data and SC management but lacks discussion on the organisational, operational and industrial consequences of DSC (see Appendix 1).

An important distinction is to be made when data are the main artefact: data are not consumed nor do they perish in the process of production, nor do data necessarily depreciate. Moreover, data have atypical characteristics compared to physical raw materials in a SC: data can be inputs, intermediate goods as well as end products themselves. Therefore, DSC can be an iterative process where data lead to expansive value creation. As El Kadiri et al. (2016) suggests, data product cycle is not a closed loop system and more data iteratively feedback into different decision-making phases. These characteristics of data in the DSC lead to several consequences for the industrial and organisational setting. Collaboration, coordination and transparency within industries become more prevalent (Li, Nucciarelli et al. 2016; Janssen, van der Voort, and Wahyudi 2017) compared to persistence of competition and secrecy in SCs around physical goods. Sharing data is generally beneficial and creates positive externalities for processes and organisations (Kwon, Lee, and Shin 2014; Li, Nucciarelli et al. 2016; Janssen, van der Voort, and Wahyudi 2017).