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

Kim Hua Tan, Yuan Zhu Zhan*, Guojun Ji, Fei Ye, Chingter Chang

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

333 Scopus citations

Abstract

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.

Original languageEnglish
Article number5964
Pages (from-to)223-233
Number of pages11
JournalInternational Journal of Production Economics
Volume165
DOIs
StatePublished - 01 07 2015

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V.

Keywords

  • Analytic infrastructure
  • Big data
  • Competence set
  • Deduction graph
  • Supply chain innovation

Fingerprint

Dive into the research topics of 'Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph'. Together they form a unique fingerprint.

Cite this