Big data analytics in logistics and supply chain management

• Date: 14 May 2018
• Pages: 478-484
• Published date: 14 May 2018
• DOI: https://doi.org/10.1108/IJLM-02-2018-0026
• Author: Samuel Fosso Wamba,Angappa Gunasekaran,Thanos Papadopoulos,Eric Ngai
• Subject Matter: Management science & operations,Logistics

Guest editorial

Big data analytics in logistics and supply chain management

Introduction

In recent years, big data analytics (BDA) capability has attracted significant attention from

academia and management practitioners. We are living in an era where there has been an

explosion of data (Choi et al., 2017). Kiron et al. (2014) argued that a majority of fortune 1,000

firms is pursuing BDA-related development projects. Chen and Zhang (2014) argued that big

data (BD) has enough potential to revolutionize many fields including business, scientific

research and public administration and so on. The use of BDA in the field of marketing and

finance is on the rise. However, the operations and supply chain professionals are yet to exploit

the true potential of the BDA capability in order to improve the supply chain operational

decision-making skills (Srinivasan and Swink, 2017). Operations and supply chain

professionals have access not only to data, which is continuously generated by traditional

devices such as POS, RFID, but also GPS to a vast amount of data generated from unstructured

data sources such as digital clickstreams, camera and surveillance footage, imagery, social

media postings, blog/wiki entries and forum discussions (Sanders and Ganeshan, 2015). Today,

supply chains are highly supported by advanced networking technologies –sensors, tags,

tracks and other smart devices, which are gathering data on real-time basis (Wang et al., 2016;

Gunasekaran et al., 2017), which provides end to end demand and supply visibility

(Gunasekaran et al., 2017; Srinivasan and Swink, 2017). Schoenherr and Speier-Pero (2015)

argued that supply chain managers need to process a large amount of data to make decisions

that may help reduce costs and increase the product availability to the customers.

The extant literature defines a BDA capability as a technologically enabled ability

which can help process large volume, high velocity and several varieties of data to extract

meaningful and useful insights; hereby enabling the organizations to gain competitive

advantage (Fosso Wamba et al., 2015, 2017). Galbraith (2014) further noted that historically,

supply chain managers used to analyze data gathered from traditional data warehouses to

gain insights.Moreover, Hazen et al. (2014) arguedthat the effectiveness of decision making in

supply chains often hinges upon the quality of the data processed via organizational

infrastructure, which enables the supply chain managers to quickly acquire, process and

analyze data.Papadopoulos et al. (2017) arguedthat insights gained via increasedinformation

processing capability can reduce uncertainty, especially when operational tasks such as

disaster relief operations are highly complex. However, despite increasing efforts from the

operations and supply chain community to understand the associations between different

types of operational visibility andanalytics capabilities, the theory-driven research is limited.

Hazen et al. (2016)further outlined how the use of organizational theoriescan help explain the

complexity associated with the use of BDA capability to explainsupply chain sustainability.

Waller and Fawcett (2013a) noted that the intersection of logistics and supply chain

management field with data science, predictive analytics and BD can provide numerous

opportunities for research. However, in the absence of adequate skills, the supply chain

managers often face a myriad of challenges to extract information from BD to take effective

supply chain operational decisions (Waller and Fawcett, 2013a; Dubey and Gunasekaran,

2015a; Gupta and George,2016). The role of contextual factors in developing BDA capability

is well discussed in the information systems literature. What is less understood is how BDA

under the effect of contextual factors affect logistics and supply chain processes. Waller and

Fawcett (2013b) argued that recent experience with BD may help to explain some of the

complex phenomena and unanswered questions in logistics and supply chain management.

The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 478-484

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-02-2018-0026

478

IJLM

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