Li, S. orcid.org/0000-0002-0147-8888, Peng, G. and Xing, F. (2019) Barriers of embedding big data solutions in smart factories: insights from SAP consultants. Industrial Management & Data Systems, 119 (5). pp. 1147-1164. ISSN 0263-5577
Abstract
Purpose: Big data is a key component to realize the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The study reported in this paper aimed to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers.
Design/Methodology: The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP Consultants and Projects Managers. The qualitative data collected was then systematically analysed by using a thematic analysis approach.
Findings: A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers.
Originality /value: This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision-making.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Emerald Publishing Limited. This is an author-produced version of a paper subsequently published in Industrial Management and Data Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Smart Factory; Big Data; Barriers; Information Systems |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Mar 2019 12:18 |
Last Modified: | 20 Jun 2019 13:43 |
Status: | Published |
Publisher: | Emerald |
Refereed: | Yes |
Identification Number: | 10.1108/IMDS-11-2018-0532 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143945 |