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

• Author: Stanley E. Fawcett,Matthew A. Waller
• Date: 01 June 2013
• Published date: 01 June 2013
• DOI: http://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 definitions 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 specific 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 data”is 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 field of supply chain management (SCM), presenting a doozy

of an opportunity and a challenge to our field. 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 confluence of data science, predictive

analytics, and big data as DPB.

Data are widely considered to be a driver of better decision

making and improved profitability, 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 financial 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 profit-

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 DPB’s 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 DPB’s 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 significant 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): 77–84

© Council of Supply Chain Management Professionals