Barriers of embedding big data solutions in smart factories: insights from SAP consultants
| DOI | https://doi.org/10.1108/IMDS-11-2018-0532 |
| Pages | 1147-1164 |
| Published date | 10 June 2019 |
| Date | 10 June 2019 |
| Author | Shuyang Li,Guo Chao Peng,Fei Xing |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Barriers of embedding big data
solutions in smart factories:
insights from SAP consultants
Shuyang Li
Information School, The University of Sheffield, Sheffield, UK, and
Guo Chao Peng and Fei Xing
School of Information Management, Sun Yat-sen University, Guangzhou, China
Abstract
Purpose –Big data is a key component to realise 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 purpose of this paper is 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/approach –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 project
managers. The qualitative data collected were then systematically analysed by using a thematicanalysis 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.
Keywords Barriers, Information systems, Big data, Smart factory
Paper type Research paper
1. Introduction
Remarkable improvements in autonomous technologies and significant changes in market
requirementare shifting the industrial evolutionaryjourney towards the fourth generation, or
so called Industry 4.0 (Shrouf et al., 2014; Peng et al., 2017). This has become an important
concept promoted by both dev eloped (e.g. the USA, the UK, G ermany and Japan) and
developing (e.g.China and India) countries, with theaims of profoundly enhancing efficiency
and maximising sustainability in manufacturing environment through new technologies.
Smart factoryis a key concept emergedtogether with the vision of Industry4.0. It utilises a set
of advanced technologies (including Internet of Things (IoT), cyber physical systems (CPS),
cloud computing, big data and artificial intelligence) to enable peer-to-peer communication
and negotiation between machines, systems and products, as well as to respond to
constantly growing amount of data generatedin manufacturing processes (Davis et al., 2015).
As a result, smart factory addresses vertical integration of different components and
facilitatesthe factory to reconfigure itselffor flexible production of different types of products
(Lopez Research, 2014).
Ever since the emergence of the concept, smartfactory has been heatedly investigated by
researchers and practitioners in fields of engineering and computer sciences. One of the most
critical and influential problems, widely recognised by researchers (e.g. Lee, Kao and Yang,
2014; Lee,Madnick, Wang, Wang and Zhang,2014), is how to utilise advancedtools to process Industrial Management & Data
Systems
Vol. 119 No. 5, 2019
pp. 1147-1164
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-11-2018-0532
Received 30 November 2018
Revised 6 March 2019
Accepted 16 April 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
This study was supported by the grant funded by the Guangdong Natural Science Foundation
(No. 2018A030313706) and the 100 Talent Grant offered by Sun Yat-sen University, China (No. 201603).
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Barriers
of embedding
big data
and analyse the huge amount of data generated in smart factories to support production
automation and decision making. In thiscontext, big data solutions are perceived as a crucial
component to ensure the success of smart factory development, by providing the needed
mechanisms in analysing, coordinating and making full use of the generated data. In
organisational practice, pioneers and practitioners pursuing leading-edge smart factory
initiativesare actively leveraging big data solutions(e.g. SAP Hana) for optimisingoperations
and automation on a real-time basis (Zhong et al., 2016).
Despite the strong need, however, there seems to be a scarcity of research and studies to
explore the phenomenon of embedding big datasolutions in smart factories. In particular, our
review of the literature showed that most studies in the field explored the issue of smart
factory or bigdata separately. There are few empirical studies assessing the combinationand
potential of big data solutions in the context of smart factories (Riggins and Wamba, 2015).
More importantly,current studies on smart factoryor big data are focussing on technical and
engineering aspects such as security aspects (Sadeghi et al., 2015), smart operators and
enhanced supply chains (Kolberg and Zühlke, 2015) and application of CPS in Industry 4.0
environments (Jazdi, 2014). In fact, although smart factory and big data analytics are driven
by advanced technologies, their success is highly dependent on the application environment
and organisational settings (Peng et al., 2017). In other words, challenges and problems
occurred when implementing big data solutions in smart factory cannot be addressed by
merely focussingon technology or engineering innovation, but also rely on how to effectively
adopt and manage such technology in organisation contexts. In light of this discussion, an
important omission identified in the current literature was the lack of study to investigate
challenges and barriers for embedding big data solutions in smart factories from a
socio-technical angle, especially from an information system (IS) perspective that takes into
account the intersections of technology, data, management and people.
The study reported in this paper aims to fill these knowledge gaps, by investigating and
exploring socio-technical barriers affecting the implementation and usage of big data
solutions in the context of smart factory. Considering that most user companies may still be
in infant stage towards embedding big data solutions in their new smart factory initiatives,
they may not be able to offer sufficient insights for the phenomenon under investigation.
As such, this study was specifically conducted from an IS consultancy perspective. A group
of experienced SAP consultants were interviewed, and the results of data analysis led to the
establishment of a framework that contains 12 critical barriers divided into three main
categories. This study contributes to the body of knowledge by extending current theory in
big data and smart factory, and producing a practical framework with guidance and
emphasis on its organisational implementation.
The rest of this paper is structured as follows. The next section provides a systematic
review of literature on smart factory and big data, followed by an explanation of the
research methodology. Subsequently, the findings derived from the interviews were
presented and discussed. The last section provides the overall conclusion, implications and
limitation of this study.
2. Related research on smart factory and big data
2.1 Overview of literature on smart factory
Smart factory is a term used to describe industrial operation improvements through
integration and automation of production systems, linking physical and cyber capabilities,
and maximising data power including the leverage of big data evolution (Moyne and
Iskandar, 2017). Companies initiating smart factory innovation seek to obtain competitive
advantages through adopting and applying cutting-edge information technologies (Kang
et al., 2016). By applying IoT technologies (e.g. wireless sensors, RFID tags, CPS, etc.), smart
factory can monitor real-time machine processes in the production line, create a virtual copy
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