Big Data Analytics and IoT in logistics: a case study

• Published date: 14 May 2018
• Pages: 575-591
• DOI: https://doi.org/10.1108/IJLM-05-2017-0109
• Date: 14 May 2018
• Author: John Hopkins,Paul Hawking
• Subject Matter: Management science & operations,Logistics

Big Data Analytics and IoT in

logistics: a case study

John Hopkins

Swinburne University of Technology, Melbourne, Australia, and

Paul Hawking

Victoria University, Melbourne, Australia

Abstract

Purpose –Advances in technology enable companies to collect and analyse data, which were previously not

accessible, to either enhance existing business processes or create new ones. The purpose of this paper is to

document the role and impact of Big Data Analytics (BDA), and the Internet of Things (IoT), in supporting a

large logistics firm’s strategy to improve driver safety, lower operating costs, and reduce the environmental

impact of their vehicles.

Design/methodology/approach –A single case with embedded units intrinsic case study method was

adopted for this research and data were collected from a “real-life”situation, to create new knowledge about

this emerging phenomenon.

Findings –Truck telematics were utilised in order to better understand, and improve, driving behaviours.

Remote control centres monitor live sensor data from the company’s fleet of vehicles, capturing the likes of

speed, location, braking, and engine data, to inform future training programs. A combination of truck

telematics and geo-information are being used to enable proactive alerts to be sent to drivers regarding

possible upcoming hazards. Camera-based technologies have been adopted to improve driver safety, and

fatigue management, capturing evidence of important driving events and storing data directly to the cloud,

and BDA is also being used to improve truck routing, recommend optimal fuel purchasing times/locations,

and to forecast predictive and proactive maintenance schedules.

Research limitations/implications –The type of data collected by Company A, and similar logistics

companies, has the potential to greatly inform researchers investigating autonomous vehicles, smart cities,

and the physical internet.

Practical implications –Eco-driving, a practice informed/improved by BDA at Company A, has been

linked to reductions in fuel consumption and CO

2

emissions, which bring both economic and environmental

benefits. Technologies similar to Truckcam are growing in popularity in some parts of the world, to the point

where it is now common practice to use dashcam assess of accidents to establish liability. This has

implications for logistics firms, in other parts of the world, where such practices might not yet be so

commonplace, and for drivers and society more broadly.

Social implications –Improvements in utilisation and routing have the potential to reduce traffic

congestion, which is responsible for losses in productivity, increases in fuel consumption, air pollution and

noise, and can incite stress, aggression, anger and unsafe behaviours in drivers. Predictive analytics, which

generate refuelling and maintenance schedules, have the potential to be adopted by all vehicle manufacturers,

and could generate reductions in customer fuel costs, whilst improving the performance, efficiency, and life

expectancy of future motor all vehicles. The high probability of occupations in the logistics industry being

replaced by computer automation in the near future is also discussed.

Originality/value –The findings from this research serve as a valuable case example of a real-world

deployment of BDA and IoT technologies in the logistics industry, and present implications for practitioners,

researchers, and society more widely.

Keywords Logistics, Internet of Things, Big Data Analytics, Case study, Transport, Innovation

Paper type Case study

Introduction and motivation

Back in 2013, DHL prophesied that Big Data would improve logistics operational efficiency

and customerexperience, and create usefulnew business models, adding,“Big Data has much

to offer the world of logistics. Sophisticated data analytics can consolidate this traditionally

fragmented sector, and these new capabilities put logistics providers in pole position as

search engines in the physical world”(Jeske et al., 2013, p. 1). However, the following year,

Accenture (2014) conducted a web-based survey of 1,014 supply chain professionals,

The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 575-591

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-05-2017-0109

Received 8 May 2017

Revised 10 July 2017

7 September 2017

Accepted 8 September 2017

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

www.emeraldinsight.com/0957-4093.htm

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Big Data

Analytics and

IoT in logistics

and concluded thatthe “actual use of Big Data Analytics (BDA)is limited”(p. 7). They found

that, despite the acknowledged benefits of BDA, most companies experienced difficulties in

adopting it, and werealso worried about the level of investment required, security risks, and

the lack of available business cases for analytics (Accenture, 2014).

More recently, a KPMG (2017) report described a number of emerging case examples that

reveal how logistics operations are utilising real-life Big Data solutions to reduce delivery

delays through the availability of GPS, traffic, and weather data. However, academic

examples describing “real-life”cases of BDA utilisation in the logistics industry are limited,

and when Wang et al. (2016, p. 107) conducted a systematic review of Big Data business

analytics literature with a logistics and supply chain management context, a “gap between

academic theory and supply chain practices”was confirmed.

Similarly, the Internet of Things (IoT) has also been predicted to play an important role

in the future of the logistics industry, as an increasing number of objects start to carry bar

codes, RFID tags, and sensors, generating geospatial data that enable accurate, real-time,

tracking of physical objects across an entire supply chain (Atzori et al., 2010; Da et al., 2014;

Razzaq Malik et al., 2017; Swaminathan, 2012).

The motivation for undertaking this programme of research was, therefore, to gain a

better understanding of how BDA and IoT are being utilised in today’s logistics industry.

It strives to present an interesting new case example, to benefit industry practitioners and

academic researchers alike, which contributes towards a closing of the gap between theory

and supply chain practices.

Research objectives and questions

The objective of the research is to collect data from a “real-life”situation and create new

knowledge about this emerging organisational phenomenon. Adopting an intrinsic case

study methodology, the investigation examines a large logistics company over a period of

almost four years, and seeks to gain a better understanding of how BDA and IoT initiatives

are being utilised to drive improvement. It then seeks to compare the findings that emerge

from this research with latest academic BDA frameworks. In order to achieve these two

objectives, the following research questions have been developed:

RQ1. How are BDA and IoT being utilised in the logistics industry to drive process and

performance improvement?

RQ2. How does the utilisation of BDA and IoT technologies, in the case organisation,

align with the latest research frameworks for BDA?

Literature review

BDA

The term “Big Data”was first suggested by Cox and Ellsworth (1997, p. 4), who identified a

“[…] Challenge for computer systems: data sets are generally quite large, taxing the

capacities of main memory, local disk, and even remote disk. We call this the problem of

Big Data”. However, that definition of Big Data is now somewhat limited, as it is dependent

on the technology capacity of an organisation at any particular time, and a more

contemporary approach to define Big Data is based around a description of its

characteristics. Laney (2001) employed the characteristics of velocity, variety, and volume

(3 V’s) to define Big Data, and described data management challenges that rapidly escalated

due to the emergence of e-commerce at that time. Since Laney (2001), additional

characteristics, such as value, validity, veracity, and visibility, have also been proposed to

define Big Data (Marr, 2015; Wamba et al., 2015). Boyd and Crawford (2012, p. 663) believed

that Big Data can be defined from a multi-faceted perspective, based on the interplay

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