=Paper=
{{Paper
|id=Vol-2016/paper14
|storemode=property
|title=Big Data Driven Supply Chain Network Design - Insights, Impacts, and a Framework for Assessment
|pdfUrl=https://ceur-ws.org/Vol-2016/paper14.pdf
|volume=Vol-2016
|dblpUrl=https://dblp.org/rec/conf/simpda/ArthanariKS17
}}
==Big Data Driven Supply Chain Network Design - Insights, Impacts, and a Framework for Assessment==
https://ceur-ws.org/Vol-2016/paper14.pdf
Big Data Driven Supply Chain Network Design –
Insights, Impacts, and a Framework for Assessment
Tiru Arthanari, Shohil Kishore and David Sundaram
University of Auckland, New Zealand
Abstract: There is a huge amount of hype associated with the term “Big Data”
(Walker, 2014; Chen, Chiang & Storey, 2012; Chen, Mao & Liu, 2014). Its ap-
plications to industry appear limitless (Chen, Alspaugh & Katz, 2012). However,
research applying big data to the supply chain management domain fails to
demonstrate useful results (Richey et al., 2016). We therefore propose the fol-
lowing research objective: Does big data have a practical impact on the future
of supply chain management?
In this article, we argue that the insights gathered from big data cannot effectively
be used to improve supply chain performance unless the supply chain is contex-
tually robust. Supply chain robustness is defined as “the ability of a system to
maintain its operational capabilities under different circumstances” (De
Neufville, 2004). In this scenario, if a supply chain is unable to adapt, decision
makers attempting to rapidly react to insights may actually disrupt supply chain
performance instead of improving it.
To effectively incorporate these insights, we must first evaluate the underlying
traditional supply chain network design [SCND] with the purpose of understand-
ing vulnerabilities and then attempting to discover processes that are more flexi-
ble, adaptive and robust. We hypothesise that seminal supply chain structures are
incapable of effectively utilising big data, significantly diminishing supply chain
performance in this context. To initially evaluate our hypothesis, we illustrate a
scenario that utilises the Lambda architecture’s speed layer to process our data
Fig. 1. Traditional Supply Chain Response To Real-Time/Stream Data
151
using Apache Storm (supported by HDFS and YARN) in real-time, then feed
insights gathered from the various data sources into a traditional SCND (Figure
1). We have chosen to focus on the speed-processing layer as a large proportion
of big data is useful within a relatively short period of time (Marz & Warren,
2015). This allows us to simulate the impact of real-time data on the supply chain
and identify potential weaknesses in traditional structures. We expect that these
SCND models will not be able to manage this data, resulting in a bottleneck, lack
of real-time usability, and/or a lack of sufficient usable insight. To evaluate ro-
bustness in the context of an actual experiment, we will be using the signal to
noise ratio of the Taguchi methods tool kit to carry out our experiments (Taguchi,
1986). In addition, simulation experiments representing real-life scenarios could
be evaluated against alternative SCNDs, as well as SCNDs with adjusted levels
of robustness.
Fig. 2. Big Data Driven Supply Chain Network Design
Secondly, through further experimentation, we also intend to construct a robust
and dynamically adaptive SCND, as we would like to understand how supply
chains could effectively adapt or react to large amounts of data. To do this, we
intend to build a model based on an augmented version of the framework in Fig-
ure 1 that allows us to utilise the insights we gather from data to drive SCND
instead of suffer from operational incapability (Figure 2).
Keywords: Supply Chain Network Design, Big Data, Supply Chain Manage-
ment, Assessment Framework.
Contact Details:
Tiru Arthanari (t.arthanari@auckland.ac.nz)
Shohil Kishore (s.kishore@auckland.ac.nz)
David Sundaram (d.sundaram@auckand.ac.nz)
152