University of Plymouth
PEARL https://pearl.plymouth.ac.uk
Faculty of Arts and Humanities Plymouth Business School
2016-04
The impact of big data on world-class
sustainable manufacturing
Dubey, R
http://hdl.handle.net/10026.1/5175
10.1007/s00170-015-7674-1
The International Journal of Advanced Manufacturing Technology
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The impact of Big Data on World Class Sustainable Manufacturing
Abstract
Big data (BD) has attracted increasing attention from both academics and
practitioners. This paper aims at illustrating the role of Big Data analytics in
supporting world-class sustainable manufacturing (WCSM). Using an extensive
literature review to identify different factors that enable the achievement of
WCSM through BD and 405 usable responses from senior managers gathered
through social networking sites (SNS), we propose a conceptual framework that
summarizes this role, test this framework using data which is heterogeneous,
diverse, voluminous, and possess high velocity, and highlight the importance
for academia and practice. Finally we conclude our research findings and
further outlined future research directions.
Key words: Big Data, World Class Sustainable Manufacturing, Social
Networking Site, Confirmatory factor Analysis, Sustainable Manufacturing.
1. Introduction
In recent years Big Data Analytics (BDA) has been an important subject of
debate among academics and practitioners. McKinsey Global Institute has
predicted that by 2018 the BDA needs for the United States alone will be more
than 1.5 million managers who need to possess skills in analyzing Big Data for
effective decision making. In developing countries, in the recent 13
th
Confederation of Indian Industries manufacturing summit, BDA was at the
forefront of discussions among manufacturing professionals in India. The
Internet of things (IOT) and big data & predictive analytics are now within the
reach of the operations management community to begin to explore, with the
potential for measurable and meaningful impacts on the life of people in the
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developing world (Accenture, 2013). On the other hand, thinkers such as
Professor Nassim Nicholas Taleb, in his interview in the Economic Times
highlighted the impacts of BD, but was skeptical about its success.
The literature on the role of BDA in Operations and Supply Chain Management
(OM/SCM) (for example Wamba et al., 2015) has argued for benefits from its
use, including, inter alia, 15-20% increase in ROI (Perrey et al., 2013),
productivity and competitiveness for companies and public sector, as well as
economic surplus for customers (Manyika et al., 2011), and informed decision
making that allows visibility in operations and improved performance
measurement (McAfee and Brynjolfsson, 2012).
The majority of studies so far have endeavored to understand the different
dimensions of the concept and to capture the potential benefits to OM/SCM
(Chen et al., 2013; Wamba et al., 2015). There is little known about the
contribution of BDA to sustainability practices, and in particular the role of
BDA in achieving world class sustainable manufacturing, especially from a
developing countries perspective. “World-class manufacturing” (WCM) was
coined by Hayes and Wheelwright (1984) to denote “a set of practices, implying
that the use of best practices would lead to superior performance. This practice-
based approach to world class manufacturing has been echoed by numerous
authors since then”… (Flynn et al. 1999). In our study, world-class sustainable
manufacturing (WCSM) is defined as that set of practices that would lead to
superior sustainability performance. Keeso (2014), in his recent review of the
role of BDA for sustainability, suggests that “big data adoption has broadly
been slow to coalesce with sustainability efforts” (p.2), but still he has focused
on BDA and the environmental aspect of sustainability. In the present paper
our contribution is largely restricted to “big data and analytics” (BDA) in
extending the literature on WCSM and understanding how in future big data
can be exploited in other fields.
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Driven by the need to further explore the role of BDA for WCSM, this paper
acts to bridge this knowledge gap by achieving the following objectives: (i) to
clarify the definition of BDA and its relationship to WCSM; (ii) to propose a
conceptual framework that summarizes this role; (iii) to test the proposed
sustainability framework using data which is heterogeneous, diverse,
voluminous, and possesses high velocity; (iv) to develop future directions on the
role of BDA in WCSM.
The paper is organized as follows. The next section reviews the literature on
BDA and WCSM and identifies research gaps. In the third section, we will focus
on model development, whereas the fourth section focuses on research design.
The fifth and sixth sections present the psychometric properties of the
measuring items (i.e. reliability and validity of constructs) and findings. Finally,
the paper discusses the contribution to the literature, the limitations of the
work, and outlines further research directions.
2. Literature Review
2.1 Big Data
‘Big Data and Analytics’ (BDA) has attracted the attention of scholars from
every field including, genomics, neuroscience, economics and finance (Fan et
al. 2014). BDA is one of the fastest evolving fields due to convergence of
internet of things (IOT), the cloud and smart assets (Bughin et al. 2010).
Mayer-Schonberger and Cukier (2013) have argued that there is no rigorous
definition of “big data”. Manyika et al. (2011) have argued that BD is the next
frontier for innovation that may provide competitive advantage to organizations.
In this paper, we follow Dijcks (2013) with the definition of BD as: (i) traditional
enterprise data, machine generated, or data stemming from weblogs, sensors
and logs, and (ii) social data. Since there is a mass of information generated
from this data, this raises challenges for organizations with regard to data
storage, analysis and processing, and value, as well as concerns regarding the
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security and ownership. BD is characterized by (i) volume, denoting the large
amount of data that need to be stored or the large number of records; (ii)
velocity, denoting the frequency or speed by which data is generated and
delivered; and (iii) variety, which illustrates the different sources by which data
is generated, either in a structured or unstructured format (Wamba et al.,
2015). White (2012) has added the fourth dimension, veracity, to highlight the
importance of quality data and the level of trust in a data source. Besides the
four characteristics, scholars (e.g. Forrester, 2012) have also added another
dimension, value, to denote the economic benefits from the data.
In this research, we echo the views of Wamba and colleagues as well as McAfee
et al. (2012) and focus on the four main dimensions of BD. This is because
these characteristics affect decision-making behaviours, and also create critical
challenges. Boyd and Crawford (2012) have argued that big data is a cultural,
technological, and scholarly phenomenon that revolves around technology,
analysis, and mythology. According to Mark and Douglas (2012), BD is defined
as high-volume, high-velocity and high-variety information assets that demand
cost-effective, innovative forms of information for enhanced insight and
decision making. McGahan (2013) further argues that big data is too large to
handle with conventional software programs such as Excel, and thus requires
specialized analytics. Sun et al. (2015) have argued that big data is data whose
sources are heterogeneous and autonomous; whose dimensions are diverse;
whose size is beyond the capacity of conventional processes or tools to
effectively and affordably capture, store, manage, analyze, and exploit; and
whose relationships are complex, dynamic, and evolving.
Gandomi and Haider (2015) have attempted to further our understanding of
BD and of its potential applications. While the majority of the literature is
focussed more on BD technology and predictive analytics, Gandomi amd
Haider (2015) have attempted to provide detailed explanations for volume,
variety, velocity, veracity, variability and value. In the same work they have
outlined various techniques and tools that can enhance decision making
