Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh

Moktadir, MA; Ali, SM; Paul, SK; Shukla, N

Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh

Moktadir, MA Ali, SM Paul, SK

Shukla, N

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Publication Type:: Journal Article
Citation:: Computers and Industrial Engineering, 2019, 128 pp. 1063 - 1075
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Moktadir, MA	en_US
dc.contributor.author	Ali, SM	en_US
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Shukla, N	en_US
dc.date.available	2021-04-09T19:01:08Z
dc.date.issued	2019-02-01	en_US
dc.identifier.citation	Computers and Industrial Engineering, 2019, 128 pp. 1063 - 1075	en_US
dc.identifier.issn	0360-8352	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123309
dc.description.abstract	© 2018 Elsevier Ltd Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.	en_US
dc.relation.ispartof	Computers and Industrial Engineering	en_US
dc.relation.isbasedon	10.1016/j.cie.2018.04.013	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh	en_US
dc.type	Journal Article
utslib.citation.volume	128	en_US
utslib.for	1503 Business and Management	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	128	en_US

Abstract:

© 2018 Elsevier Ltd Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.

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http://hdl.handle.net/10453/123309