Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

• DOI: https://doi.org/10.1108/IJLM-06-2020-0237
• Published date: 29 April 2021
• Date: 29 April 2021
• Pages: 742-765
• Subject Matter: Management science & operations,Logistics
• Author: Surajit Bag,Sunil Luthra,Sachin Kumar Mangla,Yigit Kazancoglu

Leveraging big data analytics

capabilities in making reverse

logistics decisions and improving

remanufacturing performance

Surajit Bag

Department of Transport and Supply Chain Management,

School of Management, College of Business and Economics,

University of Johannesburg, Johannesburg, South Africa and

Department of Marketing and International Business,

School of Business and Economics, North South University, Dhaka, Bangladesh

Sunil Luthra

Department of Mechanical Engineering,

Ch Ranbir Singh State Institute of Engineering and Technology, Jhajjar, India

Sachin Kumar Mangla

Jindal Global Business School, O.P. Jindal Global University, Haryana, India and

Plymouth Business School, University of Plymouth, Plymouth, United Kingdom, and

Yigit Kazancoglu

Department of International Logistics Management, Yasar University, Izmir, Turkey

Abstract

Purpose –The study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics

(strategic and tactical) decisions and finally on remanufacturing performance.

Design/methodology/approach –The primarydata were collected using a structured questionnaire and an

online survey sent to South African manufacturing companies. The data were analysed using partial least

squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.

Findings –The results indicate that data generation capabilities (DGCs) have a strong association with

strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a

positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs),

data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both

SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.

Practical implications –The theoretical guided results can help managers to understand the value of big

data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing

processes for achieving sustainability.

Originality/value –This research explored the relationship between BDA, reverse logistics decisions and

remanufacturing performance. The study was practice oriented, and according to the authors’knowledge, it is

the first study to be conducted in the South African context.

Keywords Africa, Information technology, Structural equation modelling, Reverse logistics, Logistics

competences

Paper type Research paper

1. Introduction

In this era of the Fourth Industrial Revolution, big data analytics (BDA) is leveraged by

various firms to enhance business performance (Arunachalam et al., 2018). BDA can be a

IJLM

32,3

742

The authors would like to thank the anonymous reviewers and the Editor for their insightful comments

and suggestions.

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

https://www.emerald.com/insight/0957-4093.htm

Received 9 June 2020

Revised 8 September 2020

24 January 2021

16 March 2021

Accepted 6 April 2021

The International Journal of

Logistics Management

Vol. 32 No. 3, 2021

pp. 742-765

© Emerald Publishing Limited

0957-4093

DOI 10.1108/IJLM-06-2020-0237

valuable asset for managers when making quality decisions (Schoenherr and Speier-Pero,

2015;Brinch et al., 2018). BDA benefits motivate organisations to build capabilities and

extract value from big data. With the aid of BDA tools, both structured and unstructured data

generated in real time can be examined (Arunachalam et al., 2018). However, BDA application

has not been fully explored by past researchers and the understanding of BDA seems

rather subtle (Brinch et al., 2018). Moreover, managers lack the understanding of requisite

capabilities for converting big data into important pieces of information (Arunachalam et al.,

2018). From the literature, the indication is that studies related to BDA applications on reverse

logistics and closed-loop chains are limited (Nguyen et al., 2018). The main reason behind the

scarcity of research in this direction is due to the difficulty in gathering data related to old/

used items that act as a barrier in applying BDA in reverse logistics (Nguyen et al., 2018).

However, this can be overcome in this Industry 4.0 era through the application of the Internet

of things (IoTs), sensors and wireless technologies for collecting data (Telukdarie et al., 2018;

Bag et al., 2020). Supply chain managers face various challenges in managing reverse

logistics and remanufacturing practices due to uncertainties in product returns and the

presence of contradictory organisational targets. In this context, BDA can prove useful in

capturing the perceptions of supply chain partners, developing proper goals, scrutinising the

supply chain process and taking corresponding strategic actions (Nguyen et al., 2018). IoT in

the remanufacturing processes can identify the flaws within no time. IoT also helps in real-

time production scheduling for the remanufacturing of automobile engines by the Internet of

manufacturing things process (Zhang et al., 2018). IoT and BDA have a significant role

related to the issues concerning safety and the operations environment (Hopkins and

Hawking, 2018). The application of real time technology to assess the diverse data sets has

facilitated many opportunities that further resulted in a profit for a company. Reverse

logistics is a critical need in our society to encourage the recycling of old products (Kumar and

Putnam, 2008). However, using advanced technologies, the challenges related to coordination

and communication need to be eliminated (Govindan and Soleimani, 2017). BDA has been

found to enhance the ecological quality (Song et al., 2012,2018;Chuai and Feng, 2019;Rumson

and Hallett, 2019). BDA used in making management decisions can impact an organisation’s

power relative to its customers and suppliers (Waller and Fawcett, 2013).

From the literature, the indication is that big data has gained importance to create superior

business value relative to an information and communication technology point of view

(Raguseo et al., 2020). Although studies are available that have investigated the effect of BDA

capabilities, both on an organisation’s performance (Akter et al., 2016) and supply chain

agility (Dubey et al., 2019b) but the effect of BDA on reverse logistics decision-making is

under researched. Therefore, we aim to answer the question below:

RQ1. What is the impact of BDA capabilities on reverse logistics decisions?

The world is moving towards a stage where there will be no more landfills for disposing of

waste material generated from industry sources. Plastics waste, rubber waste, end-of-life

electronic parts and end-of-life vehicles result in the quick degradation of the environment

(Mohajerani et al., 2020;Ullah and Sarkar, 2020;Saidani et al., 2020). Various researchers have

proposed frameworks for the environmental management of end-of-life items (Rizzi et al.,

2013;Saidani et al., 2020). Of all the available options, remanufacturing has emerged as the

most popular option (Kumar et al., 2017), due to its ability to increase the life of resources by

restoring its properties for the resale of the same item to the market (Abbey et al., 2015). Ferrer

and Swaminathan (2006) argued that remanufacturing is very profitable as organisations can

improve savings and lower production costs. However, various reverse logistics challenges

are associated with remanufacturing, which minimises its original appeal (Sundin and

Dunb€

ack, 2013). The process involved in the acquisition of core is a challenge for the

automotive sector. Additionally, information that is available for reverse engineering is a big

The effect of

BDACs on

reverse

logistics

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