Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities

• Published date: 01 September 2019
• Date: 01 September 2019
• Author: Tonya Boone,Nada R. Sanders,John D. Wood,Ram Ganeshan
• DOI: http://doi.org/10.1111/jbl.12224

Sustainable Supply Chains in the Age of AI and Digitization:

Research Challenges and Opportunities

Nada R. Sanders

1

, Tonya Boone

2

, Ram Ganeshan

2

, and John D. Wood

3

1

Northeastern University

2

The College of William and Mary

3

Econautics Sustainability Institute

Sustainability has become a global corporate mandate with implementation impacted by two key trends. The first is recognition that global

supply chains have a profound impact on sustainability which requires “greening”the entire supply chain. The second is technology—digi-

tization, artificial intelligence (AI), and “big data”—which have become ubiquitous. These technologies are impacting every aspect of how com-

panies organize and manage their supply chains and have a powerful impact on sustainability. In this essay, we synthesize current dominant

themes in research on sustainable supply chains in the age of digitization. We also highlight potential new research opportunities and challenges

and showcase the papers in our STF.

Keywords: sustainability; supply chain management; artificial intelligence; digitization; big data

INTRODUCTION

Sustainability has become a global corporate mandate.

Accepted practice of sustainable business calls for measures of

business success that include social, economic, and environ-

mental factors; and stewardship of resources that create lasting

value and opportunity from one generation to the next (Sanders

and Wood 2019). Achieving sustainability, however, has been

impacted by the convergence of two prevailing trends. The first

is the dominance of global supply chains and their profound

impact on sustainability (Carter and Washispack 2018). The

truth is that the vast majority of adverse impacts, whether envi-

ronmental, economic, or social, come not from direct opera-

tions but rather from end-to-end supply chain activities

required for sourcing, production, distribution, and logistics.

For example, issues such as electronic waste, greenhouse gas

emissions, sourcing of conflict minerals, or human trafficking

arise in the context of supply chain management and must be

addressed within that domain, not simply in the context of

marketing or operations.

Where once supply chains focused on delivery, today’s supply

chains are designed to support customer-centric business models.

Global supply chains are seen as strategic assets, capabilities,

and sources of competitive advantage (Min et al. 2019). This is

forcing managers at all levels to re-evaluate how they view, man-

age, deploy, design/redesign, and measure supply chain perfor-

mance. Sustainability has become a critical part of that

evaluative process. Authentic sustainability requires greening the

entire supply chain. It also requires transparency with respect to

secondary and even tertiary partners regarding social and envi-

ronmental performance.

The second megatrend impacting attainment of corporate

sustainability comes from technology in the forms of digitiza-

tion, artificial intelligence (AI), big data, and robotics applica-

tions. Digital applications are affecting every industry and all

supply chains (Bell and Griffis 2011; Klumpp and Zijm

2019). Data-driven technologies and software-managed pro-

cesses (henceforth “digital”platforms) such as social media,

mobile, analytics, embedded devices, distributed and additive

manufacturing, and the like hold significant promise for

enhancing the mission of corporate sustainability. The growth

of the Internet, social media, and web-centric software has

interconnected customers and firms selling to them; and suppli-

ers with the firms who are making products or delivering ser-

vices. However, the challenge for both researchers and

practitioners is to determine how to leverage these technolo-

gies and the enormous amounts of data they generate for per-

formance measurement and transparency; effectively integrating

the capabilities of digital platforms into supply chain sustain-

ability decisions; and to develop innovative tools, techniques,

and models that can leverage these technologies to unlock

value (Waller and Fawcett 2013, 2014).

The term “big data”underlays these technologies and has

dominated both the popular and academic press in recent past.

An excellent definition of “big data”is offered by Maniyaka

et al. (2017) who define big data as datasets whose size is so

large that the quantity can no longer fit into the memory that

computers use for processing. We define the term “big data”in

its most generic form: data sets that are large (“volume”); that is

collected in near real-time (high “velocity”); present in myriad

forms (“variety”); and at various levels of trust (“veracity”)

(McAfee et al. 2012).

Three trends have fueled the Big Data revolution in the supply

chain. First, there has been an explosion of data available within

the company and outside the company in the public domain. In

addition to the data generated by traditional transaction-based

enterprise systems (POS, RFID, ERP, etc.), supply chain plan-

ners now have access to vast amounts of data generated from

unstructured data sources such as digital clickstreams, camera

Corresponding author:

Nada R. Sanders, D'Amore-McKim School of Business, Northeast-

ern University, 325 D Hayden Hall, Boston, MA 02115, USA; E-

mail: nadasanders@gmail.com

Journal of Business Logistics, 2019, 40(3): 229–240 doi: 10.1111/jbl.12224

© 2019 Council of Supply Chain Management Professionals