Big data analytics in supply chain and logistics: an empirical approach

• DOI: https://doi.org/10.1108/IJLM-05-2017-0116
• Date: 14 May 2018
• Pages: 767-783
• Published date: 14 May 2018
• Author: Maciel Manoel Queiroz,Renato Telles
• Subject Matter: Management science & operations,Logistics

Big data analytics in supply

chain and logistics: an

empirical approach

Maciel Manoel Queiroz

Department of Naval Architecture and Ocean Engineering,

University of Sao Paulo, Sao Paulo, Brazil, and

Renato Telles

Programa de Mestrado em Administração de Empresas,

Universidade Paulista (UNIP), Sao Paulo, Brazil

Abstract

Purpose –The purpose of this paper is to recognise thecurrent state of big data analytics (BDA) on different

organisational and supply chain management (SCM) levels in Brazilian firms. Specifically, the paper focuses

on understanding BDA awareness in Brazilian firms and proposes a framework to analyse firms’maturity in

implementing BDA projects in logistics/SCM.

Design/methodology/approach –A survey on SCM levels of 1,000 firms was conducted via

questionnaires. Of the 272 questionnaires received, 155 were considered valid, representing a 15.5 per cent

response rate.

Findings –The knowledge of Brazilian firms regarding BDA, the difficulties and barriers to BDA project

adoption, and the relationship between supply chain levels and BDA knowledge were identified. A framework

was proposed for the adoption of BDA projects in SCM.

Research limitations/implications –This study does not offer external validity due to restrictions for the

generalisation of the results even in the Brazilian context, which stems from the conducted sampling.

Future studies should improve the comprehension in this research field and focus on the impact of big data on

supply chains or networks in emerging world regions, such as Latin America.

Practical implications –This paper provides insights for practitioners to develop activities involving big

data and SCM, and proposes functional and consistent guidance through the BDA-SCM triangle framework

as an additional tool in the implementation of BDA projects in the SCM context.

Originality/value –This study is the first to analyse BDA on different organisational and SCM levels in

emerging countries, offering instrumentalisation for BDA-SCM projects.

Keywords Survey, Decision-making, South America, Supply chain innovation, Logistics competences,

Logistics strategy

Paper type Research paper

1. Introduction

Big data analytics (BDA) techniques have emerged as an approach to gain competitive

advantage for organisations in recentyears (Tan et al., 2015; Dave nport, 2006). It has received

significant attention not only by scholars but also by decision makers (Dubey et al., 2016).

Strawn (2012) refers to the impact of big data as “the fourth paradigm of science”and

Gobble (2013)calls it “the next big thing in innovation”becauseorganisations are remodelling

their business based on BDA perspectives. Recently, BDA received attention in several

supply chain management studies (Jin et al., 2015; Haze n et al., 2016; Zhong et al., 2016;

Fosso Wamba et al., 2017; Gunasekaran et al., 2017; Pauleen and Wang, 2017; Rothberg and

Erickson, 2017; Sivarajah et al., 2017).

BDA can address critical challenges of firms, for example, performance improvement

and alignment with the business strategy (Akter et al., 2016), organisational performance

(Gunasekaran et al., 2017), and world-class sustainable manufacturing (Dubey et al., 2016).

Recently, Sivarajah et al. (2017) highlighted the big data challenges and presented three

dimensions that they called “conceptual classification of BD challenges”, including data,

The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 767-783

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-05-2017-0116

Received 15 May 2017

Revised 5 October 2017

2 December 2017

6 December 2017

Accepted 7 December 2017

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

767

BDA in supply

chain and

logistics

process, and management challenges. Thus, it is possible to recognise a firm’s capabilities in

BDA based on successful projects. Therefore, in recent literature regarding supply chain

BDA, studies that explored the firm’s capabilities showed the importance of resource

management. Gunasekaran et al. (2017), with their resource-based view (RBV ) approach

(Wernerfelt, 1984; Barney et al., 2001), showed the importance and impact of resources and

capabilities in supply chain costs and efficiency. Fosso Wamba et al. (2017) used the RBV

approach and proposed a model to study the impact of big data analytics capability (BDAC)

on a firm’s performance. A recent study by Akter et al. (2016) supported the resource-based

theory (RBT) and highlighted firm performance enhancement by exploring BDAC.

Gupta and George (2016) also used RBT to identify resources in BDAC.

Many advancementswere achieved in literaturein the last few years, but several gaps and

challenges remain open (Comuzzi and Patel, 2016; Strawn, 2012), especially in empirical

research (Fosso Wambaet al., 2015; Kache and Seuring, 2017). Few empirical studies, such as

those that associate innovation with global collaborative partnerships (Akhtar et al., 2016),

have been developed recently. In the last five years, the supply chain literature has focussed

on firm performance with BDA capabilities (Akter et al., 2016), big data and predictive

analytics (BDPA), and the impact of such analyses on the assimilation of supply chain and

organisationalperformance (Gunasekaran et al.,2017). Additionally, it focussedon the effects

of BDAC on firm performance( FossoWamba et al., 2017; Gupta and George, 2016) and BDA

supportingworld-class sustainable manufacturing (Dubey et al.,2016). Nevertheless, there are

some gaps in the supply chain literature on BDA, including development of frameworks to

support decision makers in adopting BDA projects.Furthermore, in emerging countries such

as Brazil, there areno studies involving empirical research to identify and diagnose the main

difficulties and barriers for BDA adoption.

Organisations are not aware about the maturity level of BDA or whether the

organisation’s current capabilities are sufficient for conducting an implementation of a BDA

project in SCM. The literature on BDA and BDAC is not broad enough and does not offer

models and/or frameworks to analyse the feasibility of implementing a big data project.

In this context, the Brazilian literature about BDA in logistics and supply chain

management (LSCM) can be understood to be relatively limited. To contribute to the

advancement of knowledge and reduce the comprehension gaps associated with BDA in

SCM, this study aims to answer the following questions:

RQ1. What are the difficulties and barriers for the adoption of BDA in Brazilian supply

chains?

RQ2. What are the main differences and impacts of BDA on different organisational and

supply chain levels?

The primary contribution of this paper is the identification of the main difficulties and

barriers for implementation of BDA processes in SCM environments in Brazilian firms.

The second contribution is the proposal of a reference framework (BDA-SCM triangle) to

support decision makers in BDA projects in the context of SCM. Moreover, this paper

contributes to the BDAC supply chain literature (Akter et al., 2016; Fosso Wamba et al., 2017;

Gupta and George, 2016) by investigating components of supply chain partnerships (SCP),

human knowledge (HK), and innovation culture (IC) (BDA-SCM triangle).

This paper is structured as follows. Section 2 consists of a literature review, including the

impact of Industry 4.0 on BDA, recent advances in BDA, BDA as a competitive advantage,

and BDA in knowledge management (KM). Section 3 explains the research methodology,

which is based on a survey. Section 4 analytically presents the results and is supported by

the statistical approach. Finally, Section 5 critically discusses the results, theoretical bases,

and focus of the research questions, and highlights indications, managerial implications,

and potential subjects for future research.

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IJLM

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