Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph

Tan, Kim Hua and Zhan, YuanZhu and Ji, Guojun and Ye, Fei and Chang, Chingter (2015) Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. International Journal of Production Economics, 165 . pp. 223-233. ISSN 0925-5273

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Abstract

Today, firms can access to big data (tweets, videos, click streams, and other unstructured sources) to extract new ideas or understanding about their products, customers, and markets. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm’s existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.

Keywords: Big Data; Analytic Infrastructure; Competence Set, Deduction Graph, Supply Chain Innovation.

Item Type: Article
Keywords: Big data; Analytic infrastructure; Competence set; Deduction graph; Supply chain innovation
Schools/Departments: University of Nottingham UK Campus > Faculty of Social Sciences > Nottingham University Business School
Identification Number: https://doi.org/10.1016/j.ijpe.2014.12.034
Depositing User: Fuller, Stella
Date Deposited: 14 Apr 2016 10:16
Last Modified: 15 Sep 2016 07:47
URI: http://eprints.nottingham.ac.uk/id/eprint/32769

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