This document summarizes a case study on a large logistics firm's use of big data analytics (BDA) and the Internet of Things (IoT) to improve operations. Specifically, the company utilizes truck telematics to monitor driver behavior data and inform training. Sensor data is also used to send proactive alerts to drivers. Camera technologies capture driving events to improve safety. BDA helps optimize routing, fuel purchasing, and predictive maintenance scheduling. The case study provides a real-world example of how logistics companies can leverage emerging technologies to enhance performance.

1. International Journal of Logistics Management, The
Big data analytics and IoT in logistics: a case study
John Hopkins, Paul Hawking,
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To cite this document:
John Hopkins, Paul Hawking, "Big data analytics and IoT in logistics: a case study", International Journal of Logistics
Management, The, https://doi.org/10.1108/IJLM-05-2017-0109
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2. Big data analytics and IoT in logistics
a case study
Abstract
Purpose - Advances in technology enable companies to collect and analyse data, which was
previously not accessible, to either enhance existing business processes or create new ones. This
research attempts 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, data was 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.
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 Big Data Analytics, Internet of Things, Logistics, Transport
Paper type Case Study
Introduction and motivation
Back in 2013, DHL prophesied that Big Data would improve logistics operational efficiency and
customer experience, and create useful new 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, Grüner, & Weiß, 2013, p. 1). However, the following year Accenture conducted a
web-based survey of 1,014 supply chain professionals, and concluded that the “actual use of Big Data
Analytics (BDA) is limited (Accenture, 2014, p. 7).” They found, despite the acknowledged benefits
of BDA, most companies experienced difficulties in adopting it, and were also worried about the level
of investment required, security risks, and lack of available business cases for analytics (Accenture,
2014).
More recently, a KPMG report (2017) 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 enables accurate, real-time, tracking of physical objects
across an entire supply chain (Atzori, Iera, & Morabito, 2010; Da Xu, He, & Li, 2014; Razzaq Malik
et al., 2017; Swaminathan, 2012).
The motivation for undertaking this program 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
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3. 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 exploratory 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
Big Data Analytics
The term ‘Big Data’ was first suggested by Cox & 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 defining 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 describe 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, Akter, Edwards, Chopin, & Gnanzou, 2015). Boyd & Crawford (2012, p.
663) believe that Big Data can be defined from a multi–faceted perspective, based on the interplay
between technology, analysis and mythology. The technology perspective is related to the increased
computing capacity required to process and analyse large data sets, the analysis perspective is related
to the type of analysis performed on these large data sets, and the potential value achieve, whilst the
mythology perspective refers to a belief that, “…large data sets offer a higher form of intelligence.”
From this, it is apparent that the definition of Big Data is still evolving.
The business intelligence and analytics (BI&A) framework was developed in recognition of the
evolution, applications, and emerging research regarding Big Data (Chen, Chiang, & Storey, 2012).
Within the research component of that proposed framework, BDA is identified as one of the five key
foundation technologies and emerging areas for analytics research, alongside Text Analytics, Web
Analytics, Network Analytics and Mobile Analytics, and is used as a term to describe data mining and
statistical analysis using BI&A technologies (Chen et al., 2012). The field has grown in prominence in
recent times, as companies focus on the rapid growth in data acquisition and generation (Alexander,
Hoisie, & Szalay, 2011), typically originating from the following data sources;
• Transactional data; For many companies this is the traditional source of their structured data
generated from their transaction processing systems such as Enterprise Resource planning (ERP)
systems.
• Human data; A more recent source of data is human generated unstructured data through the use
of social media tools such as Twitter, Facebook etc.
• Sensor data; this data is generated by sensor networks which is often referred to as the Internet of
Things (IoT). IoT applications can produce large amount of data due to the continual monitoring
of events (Mishra, Lin, & Chang, 2014).
With an estimated 2.5 Quintillion bytes of data being created every day, the challenge that
organisations now face is less about how to collect and manage such a vast quantity of data, and more
about how they might extract meaningful value from it (Bakshi, 2012). There is a consensus that
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4. companies need to understand the implications and potential value of BDA, in order to secure
intelligence from this increased level of data, so that it can be translated into a business advantage
(Gobble, 2013; McAfee & Brynjolfsson, 2012). However, whilst many companies understand the
potential value of BDA, they have struggled to realise this potential. A study of 720 firms worldwide
found that, whilst 64 per cent of respondents were planning to invest in BDA projects over the
following 12-24 months, less than eight per cent of them had actually deployed a solution to date
(Kart, Heudecker, & Buytendijk, 2013). A more recent survey found that, whilst the goals of most
BDA projects are now more clearly defined, and involve the analysis of location data (70%) or free-
form text (64%), in order to enhance the customer experience, reduce costs, streamline existing
processes, and make marketing more targeted, a significant number of responding companies were
still unsure if their return on investment would be positive or negative. (Heudecker & Kart, 2015).
Traditional approaches of data management are limited with BDA. Boncz & Brodie (2014)
believe that these limitations or challenges of BDA can be classified into two categories; Engineering
and Semantic. Engineering referring to the challenges associated with the storage and querying of
these data sets, whilst Semantics describe the challenges of gaining meaning and value from the data
sets. However, these challenges are being better addressed with the advent of new technologies such
as Hadoop and in-memory databases (Thakur & Mann, 2014; Zaslavsky, Perera, & Georgakopoulos,
2013). These developments overturn ‘centuries of established practices and challenges our most basic
understanding of how to make decisions and comprehend reality’ (Mayer-Schönberger & Cukier,
2013, p. 7), paving the way for improved decision-making and forecasting, enabling deeper business
insights to be generated, supporting the optimization, automation and re-design of new processes,
improving the accuracy of how service offerings align with customer demands, with particular value
possible when combining data from diverse sources, such as mobile and social, to create new
knowledge that can be cloud-supported (Buytendijk, 2014; Martin, 2015).
Most academic articles in this field focus on attempting to define the term, describing its key
characteristics, and making predictions about the level of impact it will have in certain sectors in the
future (Bughin, Chui, & Manyika, 2010; Manyika et al., 2011; Mayer-Schönberger & Cukier, 2013;
Murdoch & Detsky, 2013; Rozados & Tjahjono, 2014; M. A. Waller & S. E. Fawcett, 2013). There
are a small but growing number of publications that do include industry use cases (Zikopoulos &
Eaton, 2011), in the health industry (Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014), education
(Hopkins, 2016; Sin & Muthu, 2015a, 2015b), public sector (Kim, Trimi, & Chung, 2014), mining
(Holdaway, 2014), and sport (Sellitto & Hawking, 2015), but to date there is still a lack of case
examples for the application of BDA initiatives in the logistics industry. The researchers believe that
this makes it a valid area of investigation.
Innovation is a key driver of competitiveness, making it critical to the success of logistics
firms, and the topic of innovation in the logistics industry is growing in importance amongst both
academic scholars and practitioners alike (De Martino, Errichiello, Marasco, & Morvillo, 2013; Ivan
Su, Gammelgaard, & Yang, 2011). BDA is now widely recognised as an innovative mechanism for
driving improvement in operational capability, overall supply chain performance, and optimising
vehicle routing in logistics (Hazen, Boone, Ezell, & Jones-Farmer, 2014; Hazen, Skipper, Boone, &
Hill, 2016; LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011; Novoa & Storer, 2009; Tan,
Zhan, Ji, Ye, & Chang, 2015; M. A. Waller & S. E. Fawcett, 2013). The logistics industry generates a
huge amount of data as shippers, service providers, and carriers collaborate on the efficient
distribution of products from the point of manufacture, to the point of consumption, via a network of
intermediary storage facilities (Russom, 2011; Wang et al., 2016). A recent article also highlighted ten
ways in which supply chain management is being revolutionised by BDA, including the enablement
of more complex logistics networks, and the use of big data geoanalytics, for merging and optimising
deliveries (Colombus, 2015).
Bughin, Chui, & Manyika (2010, p. 12) discuss how logistics services company CHEP capture
data on a ‘significant portion of the transportation volume of the fastest-moving consumer goods’ and
how they are ‘building a transportation-management business to take advantage of this visibility,’ but
no further details are discussed as to what data is being captured and how they are leveraging benefit.
Similarly, Waller & Fawcett (2013, p. 77) discuss how the field of BDA intersects with data science
and predictive analytics, and why research is needed from investigators with domain knowledge in
logistics, as they believe these tools will “transform the way supply chains are designed and
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5. managed.” They describe the combination of quantitative and qualitative data, to approximate the
“past and future behaviour of the storage and flow of inventory, in conjunction with the related costs
and service levels,” but only possible applications are suggested (M. Waller & S. Fawcett, 2013, p.
80). Similarly, Rozados & Tjahjono (2014) employed a systematic literature review, supplemented
with an undisclosed number of interviews with consultants, to investigate BDA-driven solutions in
supply chain management. This research discussed some practical applications of how BDA might be
used to transform real-time route optimisation problems occurring in transportation, utilising traffic
density, weather conditions, transport systems constraints, intelligent transport systems, and GPS-
enabled Big Data telematics, but stopped short of identifying practical examples of where this method
has been adopted (Rozados & Tjahjono, 2014).
Internet of Things
The term ‘Internet of Things’ (IoT) emerged in the late 1990s, originating from the Auto-ID Centre
at MIT, to describe work concerning RFID infrastructure (Sarma, Brock, & Ashton, 2000). It has
now evolved to define a global network of infrastructure where ‘things,’ wireless transmissions, and
computing capabilities combine to form a network of information (Atzori et al., 2010; Boos, Guenter,
Grote, & Kinder, 2013), enabling new channels of communication between people and things, and
things and other things (Vongsingthong & Smanchat, 2014, p. 1). Examples of these things are
sensors, actuators, pumps, engines, vehicles, thermometers, nuclear reactors, washing machines, air
conditioning units, weighbridges, water meters, lights, RFID tags or CCTV cameras (Atzori et al.,
2010; Gubbi, Buyya, Marusic, & Palaniswami, 2013; Kopetz, 2011; Kortuem, Kawsar,
Sundramoorthy, & Fitton, 2010; Wortmann & Flüchter, 2015; Xia, Yang, Wang, & Vinel, 2012), and
IoT is responsible for connecting these physical items to the digital world. Such is the potential of IoT
that a recent global survey identified that 43% of responding companies were planning to implement
IoT initiatives by the end of 2016 (Geschickter & Tully, 2016).
Logistics is one area where IoT is predicted to have an extremely significant impact, as
transportation systems evolve and vehicles are fitted with an increasing level of sensing, networking,
and communication capability, enabling vehicles to interact with each other and their environment
(Da Xu et al., 2014; Zhou, Liu, & Wang, 2012). As sensory technology becomes increasingly
sophisticated vehicles will be expected to interact with their surroundings, using a range of sensors,
cameras, maps and radar equipment to perform a range of tasks, including driving themselves,
averting impacts and collisions, detecting pedestrians and animals, and finding parking spaces
(Fagnant & Kockelman, 2015; Gerla, Lee, Pau, & Lee, 2014; Jo, Chu, & Sunwoo, 2012; Keller et al.,
2011; Lozano-Perez, 2012). The possibilities for the logistics industry are obviously endless.
The body of academic knowledge in IoT is growing rapidly, and the fields of IoT and logistics are
so closely connected that a different approach to defining IoT is actually derived from logistics, and
its mission to deliver “the right product in the right quantity at the right time at the right place in the
right condition and at the right price” (Uckelmann, Harrison, & Michahelles, 2011, p. 7). However,
as with BDA, case examples that describe the real-life usage of IoT technologies in the logistics
industry are lacking. Sun (2012) discusses the critical role that RFID technology has in the future of
IoT, from a supply chain context, whilst Miorandi et al. (2012) propose some possible applications for
IoT in environmental monitoring and smart cities, that include traffic control systems, but practical
business case applications are lacking in both articles. Similarly, Bandyopadhyay & Sen (2011)
discuss the potential of IoT to reduce stockouts, the carbon footprint of logistics, and over-
production/under-production, but do not present any case examples either.
Therefore, this research is aimed at exploring and documenting practical applications of BDA and
IoT technologies, by investigating a case example of a logistics company who have successfully
implemented initiatives in these areas.
Research methodology
The methodology selected by this research was that of intrinsic case study. The case study method
has a capacity to create new knowledge, utilising both ongoing and past data from ‘real life’
situations, that might not be possible to capture using alternative research methods (Baxter & Jack,
2008; Easton, 2010; Eisenhardt, 1989; Ragin & Becker, 1992; Stake, 1995; R. K. Yin, 2013), and
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6. enables contemporary organisational phenomena to be studied in situ (Ketokivi & Choi, 2014). A
single case, with embedded units approach, was chose to be the most appropriate method for gaining
insight into the multiple business units of a single organisation. A single case study is capable of
generating a significant amount of data, typically qualitative in nature, that can offer important
insights into a phenomena (Easton, 2010), and this constructivist approach places the researchers
within the context of the phenomenon being examined (Andrew, Pedersen, & McEvoy, 2011). Yin
(2013) agrees that the case study methodology excels in situations where phenomena and context are
aligned like this, and that a single, in-depth, case study is an appropriate research approach to take
under these conditions. This case study was undertaken with the intent of better understanding the use
of BDA and IoT in the case organisation, and was not performed with the belief that it would
necessarily represents all/any other logistics companies, or that it illustrates a particular trait or issue
experienced by other logistics companies when utilising these technologies (Stake, 1995).
The subject organisation selected for this research was chosen specifically for their reputation as
an exemplar of an early adopter, of emerging technologies, in the logistics field. Multi-site
observations were made of Company A, between 2012 and 2016, at the time when the described
technologies were being introduced. The longitudinal data collection process included the
examination of existing secondary data, from organisational documents (Hodder, 1994), press
releases, videos (Cronin, 2014; Iedema et al., 2009) and content analysis of industry presentations
(Hawking & Sellitto, 2015), in addition to direct observations (Lincoln & Guba, 1985) and interviews
(Lincoln & Guba, 1985; Spradley, 2016). As part of the conditions for this research, it was agreed that
the identity of the case organisation would not be revealed, and they will therefore only be known as
‘Company A.’ As such, a number of details had to be withheld, or modified slightly, in order to
ensure their anonymity (Adelman, Jenkins, & Kemmis, 1976).
The data collected from these multiple sources was stored in an MS Access database to assist with
the process of cataloguing, sorting, querying and analysis. The convergence and integration of data
from different sources in this manner ‘promotes a greater understanding of the case’ (Baxter & Jack,
2008, p. 554). However, as these findings are restricted to the bounded context of a single case
organisation, despite the fact that this particular organisation is recognised as an exemplar of
innovation in the logistics industry, the researchers do not claim the outcomes form a strong basis for
any scientific generalisations to be made (Miles & Huberman, 1994; R. Yin, 1994; Zainal, 2007). In
order to propose more credible generalisations, and gain an improved understanding of the
phenomenon, more extensive research involving multiple case studies should to be conducted (Baxter
& Jack, 2008).
Findings
Company A is a large privately owned logistics company employing close to 30,000 people, across 10
countries, and specialises in complex supply chain design, IT systems integration, distribution
operations, linehaul, freight forwarding, and warehouse management. The Company manages 4
million square meters of distribution centres and warehouses for a wide variety of customers, ranging
from supermarkets and consumer electronics, to hospitals and sportswear companies. As part of this
case data was collected across multiple business units within the organisation.
Two of the strategic focuses for the Company are safety and environmental impact. These two
focuses were initially driven Company A’s key customers. Consequently, Company A developed and
implemented a strategic safety initiative (Plan Zero), and the goals of this initiative are:
• Zero Fatalities
• Zero Injuries
• Zero Motor Vehicle Incidents
• Zero Net Environmental Emissions
• Zero Tolerance for Unsafe Behaviour and Practices
Vehicle Tracking
Company A has adopted technology extensively throughout their organisation and believe that it
provides a point of differentiation for them and facilitates the realisation of the goals of Plan Zero. In
accordance with this, the Company implemented a range of truck telematics (IoT) to better understand
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7. and change driver behaviour. They established control rooms which monitored sensor data from the
Company’s trucks (5,000) which indicated:
• Speed
• Distance travelled
• Harsh braking
• Location
• Engine data
• Fatigue management
The Vehicle Tracking solution enabled the Company to gain insight as to how drivers are performing
in terms of accelerating, braking, swerving etc. This information was then used as basis of training to
reinforce safe driving practice. It also enabled drivers to be debriefed to reinforce safe practices and
highlight areas of improvements. This monitoring and behaviour modification resulted in reduction in
accidents and associated injuries. Another goal of Plan Zero was the reduction environmental
emissions (Green House Gases (GHG)). The Company believed that they could achieve this goal
through more efficient trucks and changes in driving behaviour. Utilising the sensors in the trucks the
Company introduced training programs to enable the drivers to drive more efficiently. By 2016, the
Company had reduced their GHG emissions by 42% of which 32% of this reduction was due to
changes driver behaviours.
Truckcam
Another IoT application is Truckcam. All new vehicles are also now fitted with a two-way
‘truckcam’ camera, simultaneously capturing video and audio in the cabin of the vehicle, as well as
the road ahead, in the result of an event exception. If the vehicle experiences increases in G-force such
as a sudden braking or swerving action, which is detected by an on-board accelerometer, sixteen
seconds of video (eight seconds before the incident and eight afterwards) is automatically stored in the
cloud. This evidence can be used in investigating incidents, improving safety, assessing liability in
legal disputes, and in driver training. Truckcam can also be operated manually, via a button on the
dashboard, and is able to capture the license plate and video footage of other drivers who commit
dangerous manoeuvres near to their vehicles and, in serious instances; these recordings can be sent to
the police for prosecutions. In 2015, footage from TruckCam was first used by the Company to clear
one of their drivers of accountability after a fatal accident. A coroner was able to view footage of the
incident, which included video and audio of events that occurred on the road, and within the truck
cabin, at the time of the accident, the speed of the truck and the position of the drivers hands on the
steering wheel at the moment of impact, and as a result of this evidence was able to exonerate the
Company driver of any wrongdoing. In the past cases like these would often result in lengthy, and
costly, court proceedings.
When the technology was first piloted, it gained strong support from both the Company and their
drivers’ union for its ability to protect drivers, before being rolled out to their fleet nationally. Since
its implementation, a clear increase in responsible behaviours amongst drivers has been observed.
Drivercam
New cabins are also fitted with a ‘drivercam’ system, that directly monitors the driver of a vehicle for
distraction and fatigue events, initiating a series of real-time interventions should the in-cab sensors
detect that the driver’s eyes have not been looking at the road ahead of them, for a longer than
acceptable period, where they might be either looking elsewhere, in what is defined as a distraction
event, or because they are closed or blinking slowly during a micro-sleep event. In these instances,
more common in line haul movement, an audible alarm is automatically generated in the cabin,
accompanied by a physical vibration in the driver’s seat, to indicate to the driver that they need to
regain focus and re-establish safe operating conditions. A signal is also sent to the Company’s
Emergency Situation Rooms located throughout the country to alert them of a possible incident, to
ensure that manual operatives are aware of the situation, and may initiate further fatigue management
interventions if required. Examples of these include; telephoning the driver, enforcing a rotation or
rest period, or recommending an adjustment to future driver schedules.
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8. Finally, the seeing-eye system offers analysis and reporting, to provide fleet managers with
ongoing information to help them manage their vehicles and drivers more effectively.
Big Data Pilot
In 2013, the Company wanted to investigate the potential of in-memory technology to provide real
time analysis of large data sets. The Company partnered with SAP to undertake a pilot study. The
Company has implemented SAP solutions extensively to support their key business processes. They
believe that their strategic partnership with SAP provides a differentiation primarily as their
customer’s also use SAP solutions to support their business processes. This provides a greater
opportunity for the integration of processes and visibility of information between the Company’s
customers and the Company. It enables the customers to easily adopt Company’s “best practice”
processes supported by customised SAP solutions resulting in improvements across the supply chain
In 2010 SAP launched their in-memory database (HANA). SAP HANA provides significant
improvements in processing speed as the data is stored in-memory rather than a physical hard disk
and uses a range of technologies to accelerate analysis of the data. This enables companies to process
large data sets very quickly and achieve insights that they would not have thought possible previously.
For example, a large Japanese retailer took 3 days of data processing each month to calculate
customer loyalty incentives. Utilising SAP HANA they can now perform the same task in 2 seconds
(Word, 2014).
The Company collaborated with SAP as how better collect and analyse data associated with safety
and environmental initiatives utilising SAP HANA. Initially the Company provided SAP with sample
data from a portion of their truck fleet (approximately 12 million data records) in an attempt to
identify how the data could be used to predict possible risk areas and identify where efficiencies could
be achieved. Through the combination of truck telematics and geo-information, they could gain
insight into dangerous roads, driving conditions, driver behaviour. Utilising this information,
proactive alerts can be sent to drivers about possible impending hazards such as an upcoming long
ascent, that can result in an over-speed incident.
Another area investigated in the SAP pilot was to identify factors that contributed to idling times
of Company A’s truck fleet. If these factors could be reduced then there would be a reduction in
GHG emissions as well as improvement in truck utilisation. For example if a truck was idle at a
customers’ site for a prolonged period over what stipulated in the contract then appropriate recourses
could occur such as contract renegotiation. Capturing the enabled the Company to identify such things
as road congestion. Twelve million truck data records (speed of trucks, GPS locations and date and
temporal data) and fifty million road data records (time of day usage of roads) were analysed in real
time to develop congestion maps based on location, day, month and time.. The Company believed
that if road congestion trends could be identified then they improve truck utilisation and routing.
They would be able to reschedule or reroute trucks from high congestion route to low congestion
routes to improve fuel consumption and delivery times and thus reduce costs. This would have a cost,
time and environmental impact on Company A’s operations and improve customer service and be a
competitive differentiator. Another area of further investigation is the analysis of truck routing and
the impact and cost of paying tolls. Does a route that includes toll roads provide any efficiencies and
does the cost of tolls justify these efficiencies?
Other use cases include the collection and analysis of data to improve decision-making associated
with the:
• Identification of optimal fuel purchasing based on location and price
• Predictive and proactive maintenance of vehicles
• Real time track and trace of customer goods
• Dynamic job scheduling and redirects
• Pickup and delivery window planning
• Real time warehouse activity.
The observations made, regarding the implementation of BDA and IoT in the case organisation, will
now be evaluated in line with two of the latest BDA research frameworks emerging from academic
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9. literature. In Heesen’s (2016) new textbook, Big Data Analytics: Revolutionizing Strategy Execution,
a number of new models and frameworks are suggested. The two most appropriate of those models,
The World of Strategic Big Data™ methodology and Big Data Analytics Framework™, will be
employed in this instance.
The World of Strategic Big Data™ Methodology
The World of Strategic Big Data™ methodology is composed of 5 key pillars based on ROI,
Stakeholder Engagement, Key Metrics, Collection and Analysis of Information, and the Stakeholder
Communication Channels (Heesen, 2016). In terms of the case study;
Return on Investment – As of yet there has been no formal ROI calculated although a formal business
case was proposed. This is not unusual, as identified by Heudecker & Kart (2015), as many
companies Big Data projects are about exploring what is possible and identifying challenges. There is
no doubt that the Big Data initiatives have had a positive impact on driver behaviour at the Company
and thus reducing the number of incidents in line with the Plan Zero initiative.
Stakeholder Engagement - the capturing and analysis of data from vehicles provide all stakeholders
with greater visibility to possible risks and areas of improvements. For management it enables them
to design preventative processes and practices. While for the drivers, they gain feedback on their
driving practices and, where necessary, it enables them to change their behaviour based on best
practices to improve their safety.
Key Performance Indicators – The Company established a number of KPIs to Support their Plan Zero
strategy. Whether these were associated with greenhouse gas reductions or driver injuries these KPIs
are continually reviewed as to their applicability.
Collection and Analysis of Information – Plan Zero is a multi-pronged initiative, which is reliant data,
being collected and analysed from a range of data sources. The truck telemetrics provides vast
volumes of data in real time while contextual data, retrieved from other sources, is used in assisting
with the analysis. As the Big Data initiatives mature, the Company have already identified a range of
use cases where this data can be further leveraged.
Communication Channels – A range of communication channels existing dependent on various
stakeholders. These could vary from on-screen reports and alerts in the Company’s Emergency
Situation Rooms to in vehicle notifications of events which drivers which need to respond to.
Big Data Analytics Framework™
The Big Data Analytics Framework™ consists of a foundation layer and a layer of front-end
applications. The foundation layer acknowledges structured and unstructured data, from both internal
and external systems, whilst the front-end applications support past, real-time and predictive analytics
(Figure 1). When applying this framework to the research findings, firstly with the Company’s
Vehicle Tracking, it can be acknowledged that the IoT-driven truck telematics were captured in real
time, stored, and used later for performance monitoring, continuous learning, and operational
reporting. Truckcam was also observed as capturing live unstructured video and audio data in the
cabin of the vehicle, at times when an incident occurs, for the purpose of assessing liability later,
whilst Drivecam was seen to utilise live data in order to identify real-time instances of driver
distraction and fatigue, for the purpose of alerting. Finally, the Big Data Pilot enabled real-time
analysis of large datasets, facilitating improved collection and analysis of structured and unstructured
data associated with safety and environmental initiatives. The data collected is now being used by the
Company to generate predictive analytics, that inform future refuelling and maintenance plans,
provide real-time analytics for tracking customer goods, and store past analytics for operational
reporting.
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10. Fig. 1 - The Big Data Analytics Framework (Source: Heesen 2016, p.46)
Implications
The findings from this research have a range of implications for practitioners, academic researchers
and society more widely. The evidence regarding safety performance improvements in driving
practices, achieved via the introduction of truck telematics, will be of interest to many logistics
managers, and have implications for other road users as well as truck drivers. The changes in driving
behaviour arising from this initiative also resulted in significant GHG emission reductions for
Company A, which has implications for the environment and public health, and will be significant to
other logistics firms, driving policy makers, and academic researchers. ‘Eco-driving,’ involving
behaviours such as moderate acceleration, better anticipation of traffic flows and signals, maintaining
a more steady speed, better adherence to speed limits and a reduction/elimination of excessive engine
idling, has been established in previous research as reducing both fuel consumption and CO2
emissions (Barkenbus, 2010).
As the adoption of technologies such as Truckcam continue to rise, road users will be increasingly
conscious of the fact that their driving conduct is being recorded by cameras in other vehicles, which
has the potential to promote more responsible driving behaviours amongst all road users. Dashboard-
mounted cameras (dashcams) are already very popular amongst both truck fleets and car users alike,
in countries such as Russia, Korea and, in particular, Taiwan, where all new cars sold since 2013 have
been required to be fitted with a standard dashcam, and where it is common practice to use dashcam
recordings of accidents to establish liability (Chan, Chen, Xiang, & Sun, 2016). Similarly,
Drivecam’s ability to provide an early intervention, in instances of driver distraction and fatigue, has
considerable potential for reducing accidents and injuries to the driver and other road users. The
cameras fitted on vehicles could evolve to also be used for surveillance, when parked or in motion, to
capture evidence of thefts or other crimes, which could lead to a reduction in crime.
Improvements in the process of truck utilisation and routing, through BDA, have the potential to
reduce traffic congestion, which is a major concern in cities around the world and a barrier to
economic growth. It creates losses in productivity, increases in fuel consumption, air pollution and
noise, and can incite stress, aggression, anger and unsafe behaviours in drivers (Deffenbacher, Lynch,
Filetti, Dahlen, & Oetting, 2003; Emo, Matthews, & Funke, 2016; Hennessy & Wiesenthal, 1999), in
addition to increased challenges for the efficient transportation of road freight (Gargett & Gafney,
2006). Reducing congestion has benefits for society, and positively affects quality of life, in addition
to having clear commercial implications. Similarly, a system of predictive analytics that generate
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11. refueling 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 vehicles.
Finally, the BDA captured by the case organisation’s fleet of road vehicles will also hold
significant value for the developers of autonomous vehicles, smart cities, and the Physical Internet
(PI). As sensory technologies become increasingly ubiquitous in all road vehicles, the interactions
between vehicles and other vehicles, and between vehicles and the built environment, will become
ever more sophisticated. This complex level of real-time communication, between the vehicle and its
surroundings, has the potential to facilitate faster speeds on highways, and vehicle platooning, which
could lead to reduced journey times, less congestion, and an increase in the capacity of existing
infrastructure. The rapid emergence of sensory technology in vehicles has now led to predictions that
occupations in the 3PL industry, in addition to taxi drivers, ambulance drivers, couriers and
chauffeurs etc., are amongst the most highly susceptible to being replaced by automation over the next
two decades (Frey & Osborne, 2017). This has significant implication for careers, skills and training
requirements for future professions. Whilst there are many positive social implications for
autonomous vehicles, such as increased levels of mobility for non-drivers, children, the disabled and
the elderly, the risk of cyber-attacks and hacking is obviously a concern.
In the shorter term, it is hoped that the findings of this paper serve as a proof of concept business
case for logistics practitioners, examining the potential benefits of investing in BDA projects for their
business, and will assist future academic researchers who wish to investigate practical applications, or
develop use case frameworks, for testing BDA theories within a logistics context. When the key
findings from this research were aligned with the World of Strategic Big Data™ methodology’s five
pillars for big data strategy, it was evident that key metrics and the collection and analysis of
information, were clearly present in the case company’s BDA strategy. However, whilst there was
also evidence to suggest the existence of stakeholder engagement, and stakeholder communication
channels, insufficient data was available. This offers numerous opportunities for authors looking to
build future research in this field. Similarly, the findings from this research also support the Big Data
Analytics Framework™, with instances of both structured and unstructured data, and past, real-time
and predictive analytics observed during the case study. This provides further endorsement for the
significance of the model, in categorising the characteristics of BDA projects, and further informs
future academic researchers (Milakis, Van Arem, & Van Wee, 2017).
Conclusions
It is believed that the objective of this research, to create new knowledge and gain a better
understanding of how BDA and IoT initiatives are being implemented in the logistics industry, has
been achieved. Throughout the four-year observation of the case organisation it was clear that the
primary focus, and therefore most significant impact of IoT-driven BDA initiatives, was in achieving
process and performance improvements in the areas of safety and environmental impact. The
utilisation of in-memory technology (SAP HANA), to analyse large diverse data sets in real time,
enabled the identification of opportunities that drove positive impact in these areas, many of which
had residual commercial, economic or social benefits. Truck telematics and tracking data have
become a valuable component in their training programs, and have led to improvements in driver
behaviour, as well as a measurable reduction in GHG emissions for their fleet of vehicles. Whilst
TruckCam and Drivercam are delivering significant value, capturing video and audio of road
incidents, providing evidence and assisting in determining liability in legal disputes, and monitoring
the driver for signs of distraction and fatigue and providing early interventions. The researchers
believe that the levels of success achieved in this case were due, in part, to the fact that an existing
initiative, Plan Zero, formed the basis of the BDA and IoT projects.
This research provides an insight into a large logistics organisation and presents a number of real-
life examples as to how BDA and IoT are being used to enhance safety, achieve cost savings, and
reduce environmental impact in the logistics industry. The authors believe the findings make a
significant contribution, to an area that is currently lacking in academic research, which has practical
implications for both academic researchers and industry practitioners. It is hoped that this paper
bridges the gap between industry practice, and academic theory, through the contribution of new
knowledge on a contemporary issue and, in doing so, augments current discussions on the topic.
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