Lookup NU author(s): Professor Savvas Papagiannidis,
Dr Eric See-To,
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
In this paper we propose using a novel big-data data-mining methodology and the Internet as a new source of useful meta-data for industry classification. The proposed methodology can be utilised as a decision support system for identifying industrial clusters in almost real time in a specific geographic region, contributing to strategic co-operation and policy development for operations and supply chain management across organisational boundaries through big data analytics. Our theoretical discussion on discerning industrial activity of firms in geographical regions starts by highlighting the limitations of the Standard Industrial Classification (SIC) codes. This discussion is followed by the proposed methodology, which has three main steps revolving around web-based data collection, pre-processing and analysis, and reporting of clusters. We discuss each step in detail, presenting the experimental approaches tested. We apply our methodology to a regional case, in the North East of England, in order to demonstrate how such a big data decision support system / analytics can work in practice. Implications for theory, policy and practice are discussed, as well as potential avenues for further research.
Author(s): Papagiannidis S, See-To E, Assimakopoulos D, Yang Y
Publication type: Article
Publication status: Published
Journal: Computers & Operations Research
Print publication date: 01/10/2018
Online publication date: 21/06/2017
Acceptance date: 13/06/2017
ISSN (print): 0305-0548
ISSN (electronic): 1873-765X
Publisher: Pergamon Press
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