Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management

Date01 June 2013
Published date01 June 2013
DOIhttp://doi.org/10.1111/jbl.12010
Data Science, Predictive Analytics, and Big Data: A Revolution
That Will Transform Supply Chain Design and Management
Matthew A. Waller
1
and Stanley E. Fawcett
2
1
University of Arkansas
2
Weber State University
We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive ana-
lytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to
supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth
of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills
and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We
propose denitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and pro-
vide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from manage-
ment theories. Finally, we propose specic steps interested researchers can take to respond to our call for research on the intersection of SCM
and DPB.
Keywords: data science; predictive analytics; big data; logistics; supply chain management; design; collaboration; integration; education
INTRODUCTION
Big datais the buzzword of the day. However, more than the
typical faddish fuzz, big data carries with it the opportunity to
change business model design and day-to-day decision making
that accompany emerging data analysis. This growing combina-
tion of resources, tools, and applications has deep implications in
the eld of supply chain management (SCM), presenting a doozy
of an opportunity and a challenge to our eld. Indeed, more data
have been recorded in the past two years than in all of previous
human history.
1
Big data are being used to transform medical
practice, modernize public policy, and inform business decision
making (Mayer-Sch
onberger and Cukier 2013). Big data have
the potential to revolutionize supply chain dynamics.
The growth in the quantity and diversity of data has led to
data sets larger than is manageable by the conventional, hands-
on management tools. To manage these new and potentially
invaluable data sets, new methods of data science and new appli-
cations in the form of predictive analytics, have been developed.
We will refer to this new conuence of data science, predictive
analytics, and big data as DPB.
Data are widely considered to be a driver of better decision
making and improved protability, and this perception has some
data to back it up. Based on their large-scale study, McAfee and
Brynjolfsson (2012) note, [t]he more companies characterized
themselves as data-driven, the better they performed on objective
measures of nancial and operational results companies in the
top third of their industry in the use of data-driven decision mak-
ing were on average, 5% more productive and 6% more prot-
able than their competitors(p. 64). To make the most of the
big-data revolution, supply chain researchers and managers need
to understand and embrace DPBs role and implications for sup-
ply chain decision making.
DATA SCIENCE, PREDICTIVE ANALYTICS, AND BIG
DATA
There is growing popular, business, and academic attention to
DPB. For instance, the October 2012 issue of Harvard Business
Review contained three articles that are relevant to this editorial:
Big Data: The Management Revolution(McAfee and Bry-
njolfsson 2012), Data Scientist: The Sexiest Job of the 21st
Century(Davenport and Patil 2012), and Making Advanced
Analytics Work for You(Barton and Court 2012). MIS Quar-
terly had a special issue on business intelligence and the lead
article was titled, Business Intelligence and Analytics: From Big
Data to Big Impact(Chen et al. 2012). There is also a plethora
of articles in trade and even lay publications on these topics.
There is even a new journal, Big Data, which premiered in
March 2013.
Over the past few years, we have been trying to understand
the DPBs implications for research and education in business
logistics and SCM. We believe that these new tools will trans-
form the way supply chain are designed and managed, presenting
a new and signicant challenge to logistics and SCM. Meeting
this challenge may require changes in foci of research and educa-
tion. Many traditional approaches will need to be re-imagined.
Some standard practices may even be discarded as obsolete in
the new data-rich environment. Some may see the possibilities as
threats rather than opportunities. Yet DPB and SCM are funda-
mentally compatible, thus the tremendous value of DPB lies
within our grasp.
Corresponding author:
Matthew A. Waller, Sam M. Walton College of Business, WCOB
308, University of Arkansas, Fayetteville, AR 72701-1201, USA;
E-mail: MWaller@walton.uark.edu
1
Source: IBM, http://www-01.ibm.com/software/data/bigdata/
accessed March 27, 2013.
Journal of Business Logistics, 2013, 34(2): 7784
© Council of Supply Chain Management Professionals

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