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International Journal of Production Research
ISSN: 0020-7543 (Print) 1366-588X (Online) Journal homepage: https://www.tandfonline.com/loi/tprs20
Internet of things and supply chain management:
a literature review
Mohamed Ben-Daya, Elkafi Hassini & Zied Bahroun
To cite this article: Mohamed Ben-Daya, Elkafi Hassini & Zied Bahroun (2019) Internet of things
and supply chain management: a literature review, International Journal of Production Research,
57:15-16, 4719-4742, DOI: 10.1080/00207543.2017.1402140
To link to this article: https://doi.org/10.1080/00207543.2017.1402140
Published online: 16 Nov 2017.
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Internet of things and supply chain management: a literature review
and Zied Bahroun
Industrial Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates;
DeGroote School of Business,
McMaster University, Hamilton, Canada
(Received 6 August 2017; accepted 31 October 2017)
This paper explores the role of Internet of Things (IoT) and its impact on supply chain management (SCM) through an
extensive literature review. Important aspects of IoT in SCM are covered including IoT deﬁnition, main IoT technology
enablers and various SCM processes and applications. We offer several categorisation of the extant literature, such as
based on methodology, industry sector and focus on a classiﬁcation based on major supply chain processes. In addition,
a bibliometric analysis of the literature is also presented. We ﬁnd that most studies have focused on conceptualising the
impact of IoT with limited analytical models and empirical studies. In addition, most studies have focused on the deliv-
ery supply chain process and the food and manufacturing supply chains. Areas of future SCM research that can support
IoT implementation are also identiﬁed.
Keywords: Internet of Things (IoT); supply chain management; industry 4.0; supply chain processes; smart supply chain
In modern business management, individual businesses cannot compete as independent entities but rather as active
members of the wider supply chain involving a network of multiple businesses and relationships (Lambert and Cooper
2000). As such, supply chains are operating under an ever-changing environment and are vulnerable to a myriad of risks
at all levels. This environment is an ever-changing landscape because of many factors. Many supply chains extend over
wide geographical areas and are vulnerable to many global risks (Butner 2010). Customers are more and more demand-
ing in terms of product customisation, price and level of service (Christopher 2016). Products complexity is also increas-
ing due to the high clock speed in many industries following the rapid changes in technology and the continuous
introduction of new products to the market (Simchi-Levi, Kaminsky, and Levi 2003). Furthermore, the external environ-
ment is highly dynamic due to economic (energy cost, prices and availability of raw materials, currency exchange rates),
social (unrest, demanding customers) and natural factors (extreme weather conditions, earthquakes, tsunamis).
In order to survive in such a complex environment, companies need to be extremely agile and build a high level of
resilience and risk mitigation capabilities and structural ﬂexibility that allow rapid response to these challenges. Christo-
pher and Holweg (2011)deﬁne structural ﬂexibility as the ability of the supply chain to adapt to fundamental changes
in the business environment. However, ﬂexibility and resilience come at an additional cost in the form of additional
resources such as buffer inventory and extra capacity, and higher coordination cost (Christopher and Holweg 2011). In
order to balance the required level of resilience and ﬂexibility and the cost of achieving it, ﬁrms need to have high visi-
bility of the whole supply chain, the necessary velocity to respond quickly to changes and effective collaboration with
suppliers and customers. Christopher (2016) summarised the principles that can guide supply chain managers into what
he calls the ‘4Rs’: responsiveness, reliability, resilience and relationships.
Information technology (IT) has been, and continue to be, an essential enabler for effective supply chain manage-
ment (SCM) (Ross 2016). It plays a critical role in helping supply chains deal with the challenges of ever-changing
environment and a myriad of risks at all levels. IT has made a major impact on the nature and structure of supply chains
due to its ability of internal integration of various processes and more importantly external integration with suppliers
and customers. This has been achieved through improving communication, acquiring and transmitting data, thus
enabling effective decision-making and enhancing supply chain performance. Internet of Things (IoT), one of the latest
IT developments, is a new IT revolution providing a paradigm shift in several areas including SCM. IoT takes supply
chain communications to another level: the possibility of human to things communication and autonomous coordination
among ‘things’while being stored in a facility or being transported between different supply chain entities. These new
*Corresponding author. Email: firstname.lastname@example.org
© 2017 Informa UK Limited, trading as Taylor & Francis Group
International Journal of Production Research, 2019
Vol. 57, Nos. 15–16, 4719–4742, https://doi.org/10.1080/00207543.2017.1402140
capabilities offer tremendous opportunities to deal more effectively with SCM challenges. IoT provides new levels of
supply chain visibility, agility and adaptability to cope with various SCM challenges (Ellis, Morris, and Santagate
2015). The data emitted from smart objects, when effectively collected, analysed and turned into useful information, can
offer unprecedented visibility into all aspects of the supply chain, providing early warnings of internal and external situ-
ations that require remediation. Responding to these signals in time can drive new levels of supply chain efﬁciency.
What was lacking so far is not the availability of information but rather the technologies for collecting and processing
big data and the lag between data collection and action. IoT will allow the reduction in the time between data capture
and decision-making that enables supply chains to react to changes in real time allowing levels of agility and respon-
siveness never experienced before (Ellis, Morris, and Santagate 2015). IoT will also enable remote management of sup-
ply chain operations, better coordination with partners and can provide more accurate information for more effective
This paper deals with IoT and its impact on supply chain management (SCM) through an extensive literature review.
This review covered important aspects of IoT in SCM including IoT deﬁnition, main IoT technology elements needed
in its implementation in a supply chain context, and various SCM applications. The extant literature is categorised
according to several classiﬁcation schemes, including methodology, industry sector and focus and major supply chain
processes. A bibliometric analysis of the reviewed literature is presented as well. The current review reveals that the
research dealing with analytical models and empirical studies is very limited. Most studies have focused on conceptual-
ising the impact of IoT. In addition, most studies have focused on the delivery process, the food and manufacturing sup-
ply chains. Following the review, we identiﬁed areas of future SCM research that can support IoT implementation.
This paper is organised as follows. Section 2provides background information about IoT including historical back-
ground, deﬁnitions and enabling technologies and platforms. We explain our review methodology in Section 3where
we also provide summary bibliometric analysis. An extensive and representative literature review is presented in Sec-
tion 4. The focus is on IoT application in various SCM processes. Section 5contains a discussion of the reviewed litera-
ture. Finally, opportunities for future research directions and conclusions are included in Section 6.
2. About IoT
In this section, we provide a discussion on the history of IoT and its enabling technologies. We also offer a deﬁnition of
IoT that is relevant to supply chain management.
2.1 Historical background
The precursor to IoT is the concept of connected devices that started in the early 1990s at the Auto-ID Centre at MIT.
Reportedly, Kevin Ashton, director of the Centre, has coined the term IoT in 1999 (Greengard 2015). In 1997, Ashton
considered the possibility of using radio-frequency identiﬁcation (RFID) tags to track products through Procter and
Gamble’s supply chain. RFID tags were used to read and identify objects and then transmit the information wirelessly
through a network. Prior to that, industry adoption of RFID tags started in 1980 (Xu, He, and Li 2014). Then a new
concept of sensors and actuators through a wireless sensor network (WSN) appeared to sense, track and monitor objects
with applications in healthcare and trafﬁc management (Xu, He, and Li 2014). Nowadays, these networks are enriched
with GPS devices, smartphones, social networks, cloud computing and data analytics to support the modern concept of
In Europe, and particularly Germany, IoT is one of the founding technologies of Industry 4.0 in the manufacturing
sector. Industry 4.0 refers to the fourth industrial revolution where the three ﬁrst industrial revolutions are related to
mechanical power (Industry 1.0), mass production (Industry 2.0) and digital revolution (Industry 3.0). Zhou, Liu, and
Zhou (2015)deﬁne the concept of industry 4.0 as the integration of information and communications technologies with
In addition to IoT technology, Industry 4.0 needs cyber–physical systems (CPS) and cloud manufacturing (CM). A
CPS is composed of machines, storage systems and production facilities that could autonomously exchange information,
trigger actions and monitor each other (Kagermann, Wahlster, and Helbig 2013). According to Cheng et al. (2016), a
CPS links a manufacturing entity virtual (computing) and physical (machines) elements by integrating analogue/digital
hardware. IoT provides the needed platform to connect the CPS using a network of sensors, actuators and devices. IoT
platforms use generally cloud-computing capabilities in external data centres, which led to the concept of cloud manu-
facturing (CM) in the industry 4.0 context.
4720 M. Ben-Daya et al.
2.2 Deﬁning IoT
Many deﬁnitions of the IoT are available in the literature. According to Atzori, Iera, and Morabito (2010), the main rea-
son for this is that IoT is composed of two words: ‘Internet’and ‘Things’and so we have two main visions. The ﬁrst
vision is mainly oriented towards the ‘Internet’or the network component and the second one is oriented towards the
‘things’component. The ﬁrst deﬁnitions of IoT are more ‘things’oriented (Atzori, Iera, and Morabito 2010) and concern
mainly the RFID tags that are connected to a network to transmit identiﬁcation information (Xu, He, and Li 2014).
Later, more ‘things’appeared as sensors and actuators to englobe today’s mobile devices in general. Below we include
a representative selection of deﬁnitions to reﬂect the variety in IoT interpretation:
Things having identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communi-
cate within social, environmental, and user contexts. (INFSO 2008)
The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the
Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with
embedded identiﬁcation, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physi-
cal entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class
of applications and services. (Miorandi et al. 2012)
Interconnection of sensing and actuating devices providing the ability to share information across platforms through a uniﬁed
framework, developing a common operating picture for enabling innovative applications. This is achieved by seamless large-
scale sensing, data analytics and information representation using cutting edge ubiquitous sensing and cloud computing. Gubbi
et al. (2013)
A loosely coupled, decentralized system of cooperating Smart Objects (SOs). An SO is an autonomous, physical digital object
augmented with sensing/actuating, processing, storing, and networking capabilities. SOs are able to sense/actuate, store, and
interpret information created within themselves and around the neighboring external world where they are situated, act on their
own, cooperate with each other, and exchange information with other kinds of electronic devices and human users. Fortino and
Dynamic global network infrastructure with self-conﬁguring capabilities based on standard and interoperable communication
protocols where physical and virtual ‘Things’have identities, physical attributes, and virtual personalities and use intelligent
interfaces, and are seamlessly integrated into the information network. Xu, He, and Li (2014)
Group of infrastructures interconnecting connected objects and allowing their management, data mining and the access to the
data they generate. (Connected objects are deﬁne as Sensor(s) and/or actuator(s) carrying out a speciﬁc function and that are
able to communicate with other equipment. It is part of an infrastructure allowing the transport, storage, processing and access
to the generated data by users or other systems. Dorsemaine et al. (2015)
Our goal here is to offer a deﬁnition of IoT as it relates to supply chain management. A supply chain is a set of enti-
ties and processes that are involved in fulﬁlling a customer order. The entities often include suppliers, factories, distribu-
tors, retailers and customers. According to the SCOR model (APICS 2015) supply chain processes are classiﬁed as
plan, source, make, deliver, return and enable. As deﬁned in Hassini (2008), the role of supply chain management is to
‘maximise the surplus: the price paid by the end customer minus all the costs incurred throughout the supply chain’.
Given our focus in this paper on IoT and supply chain management, we offer this deﬁnition for IoT:
The Internet of Things is a network of physical objects that are digitally connected to sense, monitor and interact within a com-
pany and between the company and its supply chain enabling agility, visibility, tracking and information sharing to facilitate
timely planning, control and coordination of the supply chain processes.
Our proposed deﬁnition includes four key features: (i) The requirement for digital connectivity of the physical things
in the supply chain; (ii) The nature of this connectivity is proactive allowing for data storage, analysis and sharing; (iii)
The communication involves processes within an organisation as well as interorganisation transactions covering all
major supply chain processes; and (iv) IoT will facilitate planning, control and coordination of the supply chain pro-
A closely related concept to IoT is Industry 4.0 or Industrial IoT (IIoT). As mentioned earlier, Industry 4.0 is the
product of combining CPS and IoT to the industrial automation domain (e.g. see Wollschlaeger, Sauter, and Jasperneite
2017). Thus, IoT is credited for being an enabler of Industry 4.0 that led to a fourth industrial revolution. The ‘things’
in Industry 4.0 could include smart products, smart machines and smart services such as quality-controlled logistics and
International Journal of Production Research 4721
2.3 IoT technology
As in Xu, He, and Li (2014), a typical IoT network includes four main essential layers:
(1) A sensing layer that integrates different types of ‘things’like RFID tags, sensors, actuators;
(2) A networking layer that supports information transfer through wired or wireless network;
(3) A service layer that integrates services and applications through a middleware technology; and
(4) An interface layer to display information to the user and that allows interaction with the system.
In Table 1, we provide some communication and data IoT protocols (Postcapes 2017). Recent protocols are speciﬁ-
cally designed for IoT devices such as NB-IoT, LoraWan or Sigfox. They all use low-power wide-area networks
(LPWAN) in order to connect at a low bit rate a large number of devices with low energy consumption and low cost.
Indeed, remote smart machines or embedded sensors usually only need to send small quantity of data at regular intervals
and sometimes they need to connect in remote areas without the traditional wireless or cellular infrastructure and with-
out a convenient power supply (Postcapes 2017).
Lee and Lee (2015)deﬁned ﬁve key IoT technologies:
(1) Radio-frequency identiﬁcation (RFID): It allows identifying, tracking and transmitting information. There are
ﬁve main classes of RFID tags (López et al. 2011). The class 1 tags are only passive tags with a read/write
memory. Some security related functionalities are added to class 2 tags. Semi-passive tags (class 3) are powered
by a battery and may include sensors. Active tags (class 4) are also battery-powered and can communicate with
similar tags. Finally, class 5 tags can activate other tags and are directly connected to back-end networks.
(2) Wireless sensor networks (WSN): It is a network composed of a set of sensors to monitor and track the status
of different devices like their location, movements or temperature. Sensors can be used for a multitude of pur-
poses such as temperature, pressure, ﬂow, level, imaging, noise, air pollution, proximity and displacement, infra-
red, moisture and humidity and speed (Rayes and Salam 2016). They also can cooperate and communicate with
RFID tags (Lee and Lee 2015).
Table 1. Some IoT protocols (Postcapes 2017).
Type Protocol Features
Infrastructure IPV6 Internet layer protocol for transmission across IP networks
6LoWPAN Low-power wireless personal area networks. Adaption of IPV6
Physical layer and media access control used as a basic standard for other
communication protocols such as Zigbee
Bluetooth Transfer data up to 3 Mbps and a maximum range of 100 m
Zigbee Can handle a maximum number of 1024 nodes with a maximum range of
300 m and based on IEEE 802.15.4 standards
WiﬁWireless local area networking based on the IEEE 802.11 standards. An access
point has usually a range of 20-m indoors a greater range outdoors
WiMax Wireless metropolitan area networks based on IEEE 802.16 standards. The
range for ﬁxed stations can reach 50 km and for mobile stations between 5 and
wide-area networks (LPWAN)
NB-IoT Narrow Band IoT is a radio technology standard speciﬁcally designed for
indoor coverage of a large number of devices with low cost, long battery life
and using cellular telecommunications bands
LoraWan Wireless Network protocol for battery-operated devices in regional or global
Sigfox Global wireless network for securely connecting devices to the cloud with a
low energy consumption and low cost
Data protocols MQTT Message Queuing Telemetry Transport to enable publishing messaging model in
a lightweight way for a machine-to-machine connectivity
MQTT-SN Speciﬁcally designed for sensor networks
XMPP Open source technology for Extensible Messaging and Presence Protocol
mainly used for real-time communication and people to people communication
Speciﬁcally designed for machine-to-machine and machine-to-people
4722 M. Ben-Daya et al.
(3) Middleware: It is a service-oriented software layer that allows software developers the possibility to communi-
cate with heterogeneous devices like sensors, actuators or RFID tags.
(4) Cloud computing: It is an internet-based computing platform where a pool of different computing resources
(computers, networks, storage, software, etc.) can be shared and accessed on demand. Cloud computing is criti-
cal to IoT deployment because of the huge volume of data generated by IoT devices and the need for it to be
analysed with high-speed processing computers to enable real-time and efﬁcient decision-making (Lee and Lee
2015). Many IoT cloud platforms are available on the market. They play the same role as the middleware soft-
ware and their main purpose is to connect IoT devices and IoT applications. They help transmit and secure data
from IoT devices to ERP systems and business intelligence software to provide decision-makers with real-time
information. Table 2includes the most common IoT platforms and their key characteristics. In this table, we
classiﬁed the features into four categories: connectivity, security, event monitoring and advanced analytics, as
per the platform’s machine learning capabilities. Cloud-computing services represent an efﬁcient alternative to
own and manage data centres (Bonomi et al. 2014). However, for some latency-sensitive applications, companies
may need local and on premise storage, computing and communication capabilities (Bonomi et al. 2014). The
concept of fog computing mixes local and cloud-computing services and consists in ‘a highly virtualized plat-
form that provides compute, storage, and networking services between end devices and traditional cloud comput-
ing data centres’(Bonomi et al. 2012). Indeed, IoT deployment requires mobility support, geo-distribution,
location awareness and low latency that can only be achieved through fog computing capabilities (Bonomi et al.
(5) IoT applications: they enable device to device and humans to device interactions. IoT applications constitute the
interface between the user and the devices. They should be able to present data in an intuitive way, identify
problems and suggest solutions (Lee and Lee 2015).
3. Review methodology and summary
In this section, we describe how we conducted our literature search as well as the procedure we used to select the
reviewed literature. We also categorise the studied literature and present several summary statistics. Finally, inspired by
Fahimnia, Sarkis, and Davarzani (2015) and Mishra et al. (2016), we include the results of a bibliometric and network
analysis of the reviewed papers.
3.1 Review methodology
The main objective of a literature review is to map and evaluate the relevant and existing literature to identify future
research questions (Tranﬁeld, Denyer, and Smart 2003). Our literature review method has two stages: a systematic litera-
ture review followed by a bibliometric analysis. This approach has been called ‘Systematic Literature Network Analysis
(SLNA)’by Colicchia and Strozzi (2012). The method has been found to be more objective and suitable for studying
emerging ﬁelds and their trends (e.g. see Fahimnia, Sarkis, and Davarzani 2015; Mishra et al. 2016; Strozzi et al. 2017).
To map the existing literature, we started by deﬁning the best key words for collecting the most relevant literature to
Table 2. Representative list of IoT Platforms (M&S Consulting 2017).
IoT platform Connectivity (more than internet) Security Event monitoring Machine learning
Amazon Web Services (AWS) x x x x
Carriots x x x
Cisco IoT Cloud Connect x x x
GE Predix x x x x
IBM Watson x x x x
Microsoft Azure x x x x
Kaa x x x x
Oracle IoT x x x x
Salesforce IoT platform x x x
SAP Leonardo x x x x
Thingworx x x x x
International Journal of Production Research 4723
our topic. We have chosen a combination of ‘Internet of Things’,‘supply chain’,‘supply chain management’,‘Industry
4.0’,‘smart supply chains’. We mainly used Google Scholar (www.scholar.google.com) to look for different types of
papers. The application of IoT concepts in supply chains is still recent and for that reason, we used Google Scholar
rather than Web of Science or Scopus to search the relevant literature because it is a wider database. We also searched
directly on some publishers’websites such as Elsevier (www.sciencedirect.com), Springer (www.springerlink.com), Tay-
lor & Francis (www.tandf.co.uk) and Emerald (www.emeraldinsight.com). We then reﬁned our search and studied care-
fully each paper to only select the most relevant papers to our topic. Indeed, there are many studies that have looked,
for example, at the impact of RFID on supply chain operations. In this review, we focused only on the studies that have
explicitly addressed IoT implications on the supply chain, for example, when the RFID tag has the capability to commu-
nicate wirelessly with other ‘things’in the supply chain. For this reason, we only considered papers that were published
post-2008 and for RFID papers that were published pre-2008, the reader can refer to the reviews by Sarac, Absi, and
Dauzère-Pérès (2010) and Lim, Bahr, and Leung (2013).
We repeated this cycle of searching, studying and selecting relevant papers many times from January 2017 to June
2017 given that the topic is still new and very dynamic. During each iteration, we enriched our literature review by new
papers. We ﬁnally selected 166 studies. Table 3details the selected studies by type of publication. We note that about
20% of the reviewed literature appeared in conference proceedings and books.
3.2 Bibliometric analysis
We conducted a bibliometric analysis using BibExcel software (Persson, Danell, and Schneider 2009). We have chosen
this software for its ﬂexibility and compatibility with network analysis tools such as Gephi software (Mishra et al.
(2016). The initial research information system (RIS) citations ﬁle data were obtained from Scopus and Google Scholar.
We then obtained some relevant statistics using BibExcel.
Figure 1(a) shows the publications distribution from 2008 to 2017. It is clear that there is a sharp increase in the
number of publications since 2015 (2017 publications include only publications up to June 2017) indicating an
increased interest in the subject.
Table 3shows the top contributing journals. We included only those journals that had at least ﬁve or more papers.
We note that close to 21% of the studies appeared in the International Journal of Production Economics (13.2%) and
the International Journal of Production Research (7.2%). This may be an indication of the openness of these two jour-
nals to publications in the area of new disruptive and innovative technologies. It may also be because these two journals
are highly ranked according to their recent impact factors as well as their ABDC rankings.
We then classiﬁed the papers according to six categories: two based on applications (IT enablers and special applica-
tions) and the remaining four based on the main SCOR model processes (Source, Make, Deliver, Return and Enable)
(APICS 2015). We note that a paper can belong to more than one category. Figure 1(b) shows that most of the papers
are related to the ‘make’or ‘deliver’processes. Few researchers were interested in applying IoT concepts in the ‘Source’
or ‘Return’processes. This can be explained by the fact that by design IoT technologies are more amenable to the man-
ufacturing and transportation operations in the supply chain. However, we expect there will be more interest in the sour-
cing and return functions given the increase of supplier disruptions and online shopping.
We also classiﬁed the reviewed literature according to the type of contribution (review, conceptual framework or IT
enabler) as well as methodology (analytical, empirical or case study). We note that one paper can belong to more than
Table 3. Statistics for the reviewed literature.
Type of publication Frequency
Book series 6
Book Chapter 4
International Journal of Production Economics 22
International Journal of Production Research 12
4724 M. Ben-Daya et al.
one type. We can see in Figure 1(c), that most of the authors developed conceptual frameworks or detailed IT enablers
for implementing IoT related concepts in supply chain management. There are less analytical or empirical studies which
is understandable for a new ﬁeld. However, we note that their number is increasing in 2016 and 2017.
Using BibExcel and Gephi, we conducted co-citation analysis on the reviewed papers to obtain insight on the differ-
ent studies topics and their relationships. We have used modularity partition and PageRank for ranking (Mishra et al.
2016). The resulted network graph is shown in Figure 2. We can see that there are three main clusters. Cluster 1, 2 and
3 accounted for about 58, 19 and 15% of the co-cited papers, respectively. The top ﬁve papers, according to PageRank
ranking, related to these clusters are summarised in Table 4.
Cluster 1 is formed by early studies that looked at the use of RFID tags in supply chains with a focus on inventory accu-
racy. Cluster 2 contains studies that proposed IoT technology frameworks as well as reviewed the literature related to IoT
technologies. Finally, Cluster 3 groups studies that addressed supply chain sustainability performance measures and how
the adoption of innovative technologies can help in that ﬁeld. From Figure 2, we note that the papers in Cluster 2 are inde-
pendent. Furthermore, the clusters are not connected to each other. One possible interpretation is that this ﬁeld is still in its
infancy and most studies are early independent attempts at understanding the impact of IoT on the supply chain. This justi-
ﬁes the need for our review to synthesise the different studies and offer possible future research directions.
4. Literature review
In this section, we report on our literature review. We start by positioning our review with respect to other recent
reviews in this area. We then classify the literature into six categories based on the type of applications and the studied
supply chain process.
a. Distribution by year
b. Distribution by category c. Distribution b
e of contribution
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Number of Publications
Number of Publications
Number of Publications
Figure 1. Bibliometric analysis results.
International Journal of Production Research 4725
4.1 Related literature reviews
There are several literature reviews on the subject of IoT and supply chain. In this section, we summarise those that are
most relevant to our review and explain how our review is different. Musa and Dabo (2016) focus on RFID uses in sup-
ply chain management in the period 2000–2015. Strozzi et al. (2017) review the literature on smart factories and thus
focus only on the manufacturing sector. In addition, the authors restrict their review to only those papers that are listed
on Web of Science between 2007 and 2016. Liao et al. (2017) have focused on Industry 4.0, with no particular attention
to supply chain management or the wider applications of IoT that extend beyond Industry 4.0, as we have explained in
Section 2. Liu et al. (2017) conducted a citation and content analysis of IoT literature with a focus on technology. Nas-
kar, Basu, and Sen (2017) study applications of RFID in the supply chain. They have classiﬁed the literature based on
the type of studies and supply chain processes. We ﬁnd that these recent reviews either focus on a subset of IoT tech-
nologies or address a particular application area. Our review takes a more comprehensive look at IoT technologies with
a focus on impacts on supply chain management in different sectors and application areas.
Figure 2. Co-citation clusters graph.
Table 4. Co-citations cluster topics.
Cluster Cluster 1 Cluster 2 Cluster 3
Papers Heese (2007), Angeles (2005),Sarac,
Absi, and Dauzère-Pérès (2010),
Gaukler, Seifert, and Hausman (2007),
Karakostas (2013), Meyer, Främling, and
Holmström (2009), Li et al. (2012),
Atzori, Iera, and Morabito (2010), Lim,
Bahr, and Leung (2013)
Barratt and Oke (2007), Srivastava
(2007), Vachon and Mao (2008),
Vachon and Klassen (2008), Zhu and
Topics Inventory accuracy, RFID application in
Technology frameworks and reviews Performance measurement,
4726 M. Ben-Daya et al.
4.2 IoT Applications in SCM processes
One framework for understanding supply chains is the process centric view of the supply chain (Chopra and Meindl
2013). The SCOR model is a well know such framework that divides the supply chain processes into Plan, Source,
Make, Deliver, Return and Enable (APICS 2015). This model has been widely accepted in practice largely because of
its ability to link processes to performance metrics and as such has been employed to conduct process centric views of
supply chain-related literature reviews (e.g. see Naskar, Basu, and Sen 2017). IoT brings several capabilities to aid sup-
ply chain management, such as cost-saving, inventory accuracy and product tracking. However, the extent of IoT impact
on the different supply chain processes is not known. It is thus our goal in this review to identify the role of IoT on sup-
ply chain management through a systematic analysis of the literature based on which supply chain process is being
impacted. Since the Plan process is implicated in all other SCOR processes, it our literature analysis we focus on the
other SCOR processes. We start with the Enable process to explain the different IoT technology that will be relevant for
application in the other processes.
4.2.1 IT Enablers
Most of the authors generally agree that the enabling technologies for the IoT are usually composed of four main layers:
(i) a data collection layer using mainly RFID objects and sensors, (ii) a transmission layer such as ﬁxed and mobile net-
works, (iii) service layer and (iv) interface layer (Lou et al. 2011; Gubbi et al. 2013; Verdouw, Beulens, and van der
Vorst 2013; Borgia 2014; Xu, He, and Li 2014). The third and fourth layers are sometimes merged into one layer. Fer-
reira, Martinho, and Domingos (2010)deﬁned the logistics functions of different internet of things in terms of identify-
ing, tracing, location tracking, monitoring, real-time responsiveness and optimisation. Yuvaraj and Sangeetha (2016)
combined RFID tags for indoor product tracking with GPS technology to track the same products outdoor in order to
monitor the goods anywhere at any time. Yan et al. (2014) developed a new concept of Cloud of Things to facilitate
resources sharing and collaborating between supply chain partners.
Some authors were interested in deﬁning more speciﬁcally IT enablers for Industry 4.0 and smart factories concepts.
Cheng et al. (2016) presented cyber–physical systems (CPS) to interconnect the physical and cyber world by integrating
analogue/digital hardware and the cloud-based manufacturing (CM) characterised by its high scalability, agility, resource
pooling, virtualisation, ubiquitous access, etc. Lee, Bagheri, and Kao (2015) developed a CPS architecture for manufac-
turing systems. Li (2016) introduced a technological framework of a smart factory in the petrochemical industry. Theo-
rin et al. (2015) developed an event-driven information system architecture for industry 4.0 to enable ﬂexible factory
integration and data utilisation. Thoben, Wiesner, and Wuest (2017) indicated that smart manufacturing includes differ-
ent technologies such as cyber–physical production systems (CPPS), IoT, robotics/automation, big data analytics and
Some other authors deﬁned IoT enablers for particular SCM issues. Tao et al. (2014) designed an IoT-based frame-
work to support the cloud manufacturing with manufacturing resource intelligent perception and access. Gnimpieba
et al. (2015) used different IT enablers to set a framework for a collaborative supply chain describing data storage and
real time event processing with the cloud platform. Kinnunen et al. (2016) discussed the IoT technologies related to data
acquisition in industrial asset management. Karakostas (2013) suggested a Domain Names Server (DNS) architecture
adapted to IoT. Singh and Gupta (2015) discussed the recent trends in intelligent transportation. Sund, Foss, and Bakas
(2011) presented the intelligent goods in the intermodal freight system, which includes technologies for goods identiﬁca-
tion, sensors for status monitoring, embedded logic and communication networks. Shih and Wang (2016) developed a
time–temperature indicator (TTI) for controlling the temperature of a cold supply chain using for that an IoT architec-
In addition to IT enablers’deﬁnition, some authors raised issues and challenges related to these technologies. Haller,
Karnouskos, and Schroth (2009) identiﬁed four main issues: internet scalability, identiﬁcation and the addressing of bil-
lions of ‘things’, heterogeneity of ‘things’and service paradigms. Bi, Xu, and Wang (2014) and Agrawal and Lal Das
(2011) added security and privacy issues. El Khodr, Shahrestani, and Cheung (2013) raised governance and trust con-
cerns. Finally, Atzori, Iera, and Morabito (2010) suggested paying special attention to resources efﬁciency in terms of
computation and energy capacity besides classical scalability issues.
Overall, we noticed that the main IT enablers are still RFID objects and sensors. Many authors studied the RFID
applications in supply chains. For papers published before 2010, the reader can refer to reviews by Sarac, Absi, and
Dauzère-Pérès (2010), Lim, Bahr, and Leung (2013), Zhu, Mukhopadhyay, and Kurata (2012). More speciﬁcally, Chang,
Klabjan, and Vossen (2010) proposed a novel approach for RFID optimal deployment in a supply chain network.
Wamba (2012) conducted a study to assess the role of RFID objects as enablers for supply chain integration. In the
International Journal of Production Research 4727
same way, Zelbst et al. (2012) considered the impact of RFID technology on manufacturing and supply chain efﬁciency.
Leung, Cheung, and Chu (2014) studied how to align RFID applications with supply chain strategies.
Sourcing is the process by which ﬁrms acquire materials and services. A successful supply chain plans its sourcing
activities strategically across the supply chain. Among the strategic decisions are in-house or outsourcing, supplier selec-
tion and spend management. A supply chain should also carefully consider supplier incentives and partnership develop-
ment programmes. In this section, we report on the literature that looked at IoT impacts on this important supply chain
Verdouw, Beulens, and van der Vorst (2013) argue that IoT enables the virtualisation of supply chains. A virtual con-
trol of supply chains allows the buyer to track and trace goods as they move through the supply chain as well as per-
form advanced quality control and planning. Ng et al. (2015) have proposed a model to integrate data collected from
IoT into strategic planning for product assortments. One interesting idea that was proposed is to allow for product differ-
entiation to be postponed until after delivery to the customer. Yu et al. (2015) studied the impact of IoT on supplier
selection. They recommend the adoption of IoT technologies that would offer higher ﬂexibility. Decker et al. (2008)
have identiﬁed several beneﬁts of IoT in regards to sourcing. While IoT promises to provide valuable real-time visibility
to the supplier (Lou et al. 2011), it comes at a cost. Decker et al. (2008) developed a simple linear cost model to anal-
yse the impact of the cost of sensors and alerts on the unit purchase cost.
A report published by the World Economic Forum in 2012 states that ‘manufacturing has been immensely important to
the prosperity of nations, with over 70% of the income variations of 128 nations explained by differences in manufac-
tured product export data alone’(World Economic Forum (WEF) Deloitte Touche Tohmatsu (Firm) 2012). Historically,
the evolution of manufacturing is divided into four phases known as industry 1.0 to 4.0. Each of these phases was a
major shift in the manufacturing paradigm. Industry 1.0 was the introduction of mechanical production with the help of
water and steam power. Industry 2.0 was mass production due to division of labour with the help of electrical energy.
Industry 3.0 brought electronics, IT and control systems to the shop ﬂoor to further automate production, and now
Industry 4.0 with the help of IoT is promising an unprecedented paradigm shift that will have profound implications on
manufacturing and its supply chain.
Manufacturing companies have been implementing automation systems for decades. However, these systems are
often organised in a hierarchical fashion within data silos. In particular, programmable logic controllers (PLC), and PC-
based controllers and management systems, are largely disconnected from IT and operational systems (Lopez Research
LLC 2014). Security issues being the main reason cited for these legacy structures. However, availability of data-collect-
ing storage and analysis technology (e.g. sensors, controllers, analytics software, telemetry, Big Data and cloud comput-
ing) are providing unprecedented opportunities for smarter manufacturing.
In this review, we focus on the supply chain literature dealing with smart manufacturing and not deal with the litera-
ture focusing mainly on IT aspects. A survey on technologies in an industry 4.0 environment can be found in Lu
(2017). Smart manufacturing enables smarter decisions and more efﬁcient operations through factory and supply chain
visibility based on real-time information. The areas related to the make process that can be enhanced by IoT applications
include: factory visibility, connected supply chain, production planning and scheduling, proactive maintenance, quality
beyond the factory, sustainability and addition to speciﬁc applications. Table 5summarises the literature related to these
The delivery function is one of the main important tasks of logistics. Logistics involves planning and control of ﬂow
and storage of goods and services (e.g. see Lummus, Krumwiede, and Vokurka 2001). Delivery in the supply chain is
concerned with warehousing, order and inventory management and transportation. In Table 6, we list the major IoT
impacts on the supply chain delivery processes, the technology involved and their literature sources. We note that the
majority of studies focused on transportation, followed by inventory management and warehousing. There is a need for
more studies to look at the impact on order management and the interface between different parties in the supply chain.
A promising area of research is that of quality-controlled logistics (QCL). QCL allows dynamic and real-time quality
control of products as they move through the supply chain (Giannakourou and Taoukis 2003; Dada and Thiesse 2008;
4728 M. Ben-Daya et al.
Jedermann and Lang 2008; Osvald and Stirn 2008; Bowman et al. 2009; Van der Vorst, Kooten, and Luning 2011;
Haass et al. 2015; Lang and Jedermann 2015; Pang et al. 2015; Tadejko 2015; Bogataj, Marija, and Domen 2017; Heis-
ing, Claassen, and Dekker 2017).
Bowman et al. (2009) point to the challenge of having different sensing and measurement standards across the sup-
ply chain. The lack of compatibility between supply chain partners’IoT systems can block large amounts of data and
result in a lost opportunity to use it for predictive modelling and decision-making. Sund, Foss, and Bakas (2011) looked
at IoT adoption for intermodal shipping and its potential for facilitating information sharing between different modes.
Verdouw, Beulens, and van der Vorst (2013) propose a conceptual framework for the use of IoT in supply chains. They
argue that IoT enables the virtualisation of supply chains. Yan et al. (2014) proposed a framework for using IoT data to
Table 5. Literature summary for main areas of Make processes.
Visibility and traceability framework Wang, Zhang, and Zang (2016)
Ubiquitous manufacturing Chen and Tsai (2017)
Connected supply chain
Collaboration mechanisms Schuh et al. (2014)
Management of innovative production networks Veza, Mladineo, and Gjeldum (2015)
Highly modular multi-vendor production lines Weyer et al. (2015)
Smart design and production control Zawadzki and Żywicki (2016)
Production planning and scheduling
Systematic design of the virtual factory Choi, Kim, and Noh (2015)
IoT-based production performance measurement system Hwang et al. (2016)
A real-time production performance analysis Zhang et al. (2014,2016)
Supply chain performance measurement approach Dweekat, Hwang, and Park (2017)
Real-time scheduling Ivanov et al. (2016)
Industry 4.0 elements and the lean approach Kolberg and Zühlke (2015)
Predictive manufacturing systems Lee et al. (2013)
Intelligent products for decentralised monitoring and control Meyer, Wortmann, and Szirbik (2011)
Big data analytics for RFID logistics data Zhong et al. (2015)
Smart city production system and supply chain design Kumar et al. (2016)
Autonomous maintenance Jasiulewicz-Kaczmarek, Saniuk, and Nowicki (2017)
IoT for prognostics and systems health management
Predictive maintenance using data mining and smart algorithms Kwon et al. (2016)
Remote monitoring and diagnosis of machines in real time Chukwuekwe et al. (2016)
Computing and visualisation technologies in maintenance Alexandru et al. (2015)
Application of data-driven analytics to maintenance Roy et al. (2016)
Platform for real-time and automatic maintenance cloud orders O’Donovan et al. (2015)
RFID technology to improve pipe inspection Yamato, Hiroki, and Fukumoto (2016)
RFID value in the maintenance of aircraft El Ghazali, Lefebvre, and Lefebvre (2013)
Maintenance organisations in the context of industry 4.0 Ngai et al. (2014)
IoT impact on product–service systems Bokrantz et al. (2017)
Predictive maintenance in accordance with industry 4.0 Rymaszewska, Helo, and Gunasekaran (2017)
Maintenance in digitalised manufacturing Spendla et al. (2017), Bokrantz et al. (2017)
Quality beyond the factory
Smart objects and quality management functions Putnik et al. (2015)
Zero defects by applying automatic virtual metrology Cheng et al. (2016)
Challenges of Industry 4.0 for quality management Foidl and Felderer (2016)
Quality management in product recovery using IoT Ondemir and Gupta (2014)
Information management for supply chain quality management Xu (2011)
Opportunities for sustainable manufacturing in Industry 4.0 Stock and Seliger (2016)
IoT-enabled system in green supply chain Chen (2015)
Customisation of mass-produced parts and Industry 4.0 Gaub (2016)
RFID system for the manufacturing and assembly of crankshafts Velandia et al. (2016)
Smart factory in the petrochemical industry Li (2016)
International Journal of Production Research 4729
Table 6. IoT impact on supply chain delivery process.
Delivery function IoT impact IoT technology Source
Warehousing Enabler of Joint Ordering Time savings
in the order of 81 to 99%
Smart things RFID
Lou et al. (2011), Chen, Cheng, and Huang
(2013a), Chen et al. (2013b), Choy, Ho, and
More than 1000% savings in processing
RFID Tags and
Yan et al. (2014)
Collaborative warehousing Smart things and
Reaidy, Gunasekaran, and Spalanzani (2015)
Warehouse and yard management Smart things Tadejko (2015), Alyahya, Wang, and Bennett
Safety and security Smart things and
Trab et al. (2015)
Order management Information sharing EPCglobal Bowman et al. (2009) Qiu et al. (2015)
Inventory Management Enabler of VMI through real time
Smart things Lou et al. (2011)
Inventory shrinkage RFID tags Dai and Tseng (2012), Fan et al. (2014,
Inventory misplacement RFID tags Fan et al. (2015), Mathaba et al. (2017)
Shelf replenishment RFID tags Condea, Thiesse, and Fleisch (2012), Metzger
et al. (2013)
Inventory accuracy and out-of-stocks RFID tags Goyal et al. (2016), Cui et al. (2017), Qu
et al. (2017)
Transportation Positive beneﬁts to shipper, receiver
and customer, with higher beneﬁts
going to shipper
Wireless networks Decker et al. (2008)
Autonomous decision-making Sensor Networks Jedermann and Lang (2008)
Product condition Sensor-enabled RFID
Bowman et al. (2009)
Quality monitoring, real-time
responsiveness and price optimisation
Sensor Networks Ferreira, Martinho, and Domingos (2010)
Visibility, theft reduction Smart items, multi-
Hribernik et al. (2010), Qu et al. (2017)
Real-time visibility and joint shipping Smart things Lou et al. (2011)
Intermodal shipping Smart containers Sund, Foss, and Bakas (2011), Harris, Wang,
and Wang (2015)
Rerouting based on quality level Sensors, information
fusion and cloud
Pang et al. (2015)
Accurate and timely delivery Sensor-enabled RFID
Xu, Yang, and Yang (2013), Kong et al.
(2016), Yao (2017)
More than 300% savings in scanning
and recording times
RFID tags and
Yan et al. (2014)
Fleet management, dynamic route
Smart things Tadejko (2015), Haass et al. (2015)
Quality control Time-Temperature
Giannakourou and Taoukis (2003), Dada and
Thiesse (2008), Shih and Wang (2016)
Quality-controlled logistics Smart packaging Bogataj, Marija, and Domen (2017), Haass
et al. (2015), Heising, Claassen, and Dekker
4730 M. Ben-Daya et al.
exchange locations information using smartphones. Tadejko (2015) discussed the application of IoT for end-to-end visi-
bility allowing for real-time monitoring, timely decisions and reducing delays. Qiu et al. (2015) discussed how IoT
could aid in information sharing to allow for synchronisation between production and transportation. Alyahya, Wang,
and Bennett (2016) have proposed a method for programming automated guided vehicles to autonomously store RFID-
tagged items within warehouses.
In Table 7, we report on the potential impact of IoT on the supply chain decisions and models under the delivery
We note that most studies have focused on production, inventory and order management as well as vehicle routing
decisions and models. In particular, there is a lack of studies that look at IoT long-term impact and models in the area
of facility and supply network design as well as transportation mode selection.
Long before the emergence of IoT, Thierry et al. (1995) suggested putting sensors in products to record information dur-
ing their life cycles to make logistics decisions. However, the idea was not promoted at that time due to technology and
cost limitations. With the introduction of RFID in supply chain management, researchers started looking at its potential
application in reverse logistics. Zhiduan (2005) suggested building an information-sharing platform for electronic waste
recovery supply chain through electronic product code. Kiritsis (2011) introduced the idea of intelligent products and
their important role in product lifecycle management. Martínez-Sala et al. (2009) proposed a solution that tracks a
returnable ecological system for packaging, transport, storage and display of products over the entire supply chain.
Nativi and Lee (2012) study a manufacturer and two suppliers, one of whom is a material recycler, supply chain.
They use simulation and ﬁnd that using RFID increases environmental beneﬁts and returns. Gu and Liu (2013) looked
at the IoT application in the reverse logistics information management. Kiritsis (2011) modelled a closed loop product
lifecycle management (PLM) using the smart product concept. The author integrated active product tracking product-em-
bedded information device (PEID) information, PLM agent (e.g. mobile reader) and PLM system (PLM DB). Parry
et al. (2016) conducted a study to demonstrate how the IoT may be operationalised to capture data on a consumer’s use
of products and the implications for reverse supply chains.
Paksoy et al. (2016) proposed a closed-loop supply chain model for meeting the demand of a sales and collection
centre using both new and remanufactured products. The proposed model makes use of lifecycle information, which is
monitored and collected using IoT technology. Xing et al. (2011) provided a design of an e-reverse logistics framework.
IoT technology is used to keep product lifecycle integrity. Fang et al. (2016) proposed an integrated three-stage model
based on IoT technology for the optimisation of procurement, production and product recovery, pricing and strategy of
Thürer et al. (2016) proposed the architecture of an IoT-driven Kanban system for solid waste collection. The pro-
posed framework overcomes the difﬁculties of applying a Kanban system in this context due to a large number of col-
lection points and geographical distances.
4.2.6 Special supply chains
In this section, we cover the literature dealing with applications in speciﬁc areas. In particular, many papers appeared
recently dealing with IoT application in the food supply chain. First, we overview three studies of general nature and
then we summarise, in Table 8, the literature that focused on speciﬁc areas of the food supply chain.
Table 7. IoT impact on supply chain delivery decisions and models.
Production, inventory & order
Role Location Capacity Production Frequency
Tracking X X X X X
Costing X X X X
Pricing X X X X
Dynamic Optimisation X X
International Journal of Production Research 4731
Sundmaeker et al. (2016) discussed the envisaged Internet of food and farm in the year 2020. It is a path for
research and technological development to develop innovative solutions that will help to feed the global population and
to reduce emissions and resource usage. It can also help consumers to make an informed decision when selecting speci-
ﬁc produce. Pang et al. (2015) presented a value-centric business–technology joint design framework. Identiﬁed beneﬁts
Table 8. Literature relating to speciﬁc applications in the food supply chain.
Application area Sources
Information sharing Yan et al. (2016), Chen (2017), Grunow and Piramuthu (2013), Bibi et al. (2017), Lorite et al. (2017),
Condition monitoring Bowman et al. (2009), Jedermann et al. (2014), Badia-Melis et al. (2015), Shih and Wang (2016),
Food safety Liu et al. (2016), Gautam et al. (2017), Wang and Yue (2017),
Virtual supply chains Verdouw, Beulens, and van der Vorst (2013), Verdouw et al. (2016)
Table 9. Role of IoT in supply chain management.
Process Role of IoT Impact References
Source Link with sub-tier
More visibility in supply chain, improve quality
and reduce lead time
Verdouw, Beulens, and van der Vorst (2013)
and inspection data
Better quality at lower cost Bowman et al. (2009)
Supply chain data
Strategic planning for suppliers selection and
product assortment and differentiation
Ng et al. (2015), Yu et al. (2015)
Make Visibility on more
parts and raw
Reduce lead time and costs Wang, Zhang, and Zang (2016)
Combine product and
after sales service
Increase revenue Rymaszewska, Helo, and Gunasekaran (2017)
Real-time quality and
maintenance data from
Improve product design and time to market Putnik et al. (2015)
Ondemir and Gupta (2014)
Increase product life and customer satisfaction Chukwuekwe et al. (2016)
Deliver Inventory tracking,
and joint ordering
Signiﬁcant time savings and real-time visibility;
efﬁcient use of space and resources; collaborative
warehousing; timely delivery, increase inventory
accuracy and reduce shrinkage and misplacement
Bowman et al. (2009), Lou et al. (2011), Chen,
Cheng, and Huang (2013a), Chen et al. (2013b),
Yan et al. (2014), Reaidy, Gunasekaran, and
Spalanzani (2015), Qiu et al. (2015), Choy, Ho,
and Lee (2017)
Saves time, space and money Jedermann and Lang (2008), Hribernik et al.
(2010), Dai and Tseng (2012), Xu, Yang, and Yang
(2013), Condea, Thiesse, and Fleisch (2012),
Metzger et al. (2013), Fan et al. (2014,2015),
Haass et al. (2015), Tadejko (2015), Goyal et al.
(2016), Kong et al. (2016), Mathaba et al. (2017),
Cui et al. (2017), Qu et al. (2017), Yao (2017)
Improve quality standards and reduce waste Dada and Thiesse (2008), Bowman et al. (2009),
Ferreira, Martinho, and Domingos (2010), Sund,
Foss, and Bakas (2011), Giannakourou and
Taoukis (2003), Harris, Wang, and Wang (2015),
Pang et al. (2015), Shih and Wang (2016)
Return Enhances reverse
Reduce costs Gu and Liu (2013), Kiritsis (2011)
Reduce lead time Xing et al. (2011)
More traceability Reduce costs Parry et al. (2016)
Increase customer satisfactionCapturing product
data while in use
4732 M. Ben-Daya et al.
included shelf life prediction, sales premium, precision agriculture and reduction of assurance cost. They provided exam-
ples about acceleration data processing, self-learning shelf-life prediction and real-time supply chain re-planning. Lang
and Jedermann (2015) presented a review of key enabling technologies for the food supply chain. In particular, they dis-
cussed the impact of sensor networks on food logistics. Noletto et al. (2017) looked at the Brazilian food supply chains
current technological state and their receptivity to the Intelligent Packaging and IoT technologies adoption and noted
that cost and the lack of knowledge of these technologies are the greatest barriers. Kaloxylos et al. (2013) discussed
how information management in the agri-food sector would take place under a highly heterogeneous group of actors
and services, based on the EU Smart Agri-Food project.
4.2.7 Other applications
Publications reporting IoT applications in various areas include pharmaceutical supply chain (Datta 2016; Papert,
Rimpler, and Pﬂaum 2016), retail industry (Vlachos 2014; Shin and Eksioglu 2015; Thiesse and Buckel 2015;
Nowodzinski, Łukasik, and Puto 2016; Balaji and Roy 2017), construction industry ( Shin et al. 2011; Demiralp, Guven,
and Ergen 2012; Dave et al. 2016; Zhong et al. 2017) and petrochemical industry (Li (2016)), among other applications.
In this section, we provide a number of observations regarding the application of IoT in supply chain management and
identify the gaps in the literature with respect to the potential of IoT in helping address supply chain management chal-
Despite the strong interest in the IoT issue due to its huge potential and disruptive nature, applications that address
supply chain challenges are still in their early stages. As mentioned in the introduction, IoT offers unprecedented visibil-
ity into all aspects of the supply chain, providing early warnings of internal and external situations that require remedia-
tion. Therefore, IoT enables ﬁrms to respond quickly to changes through effective internal operations and collaboration
with suppliers and customers. Current solutions and applications are still short of unlocking this potential. We only have
piecemeal applications in isolated areas with limited work that addresses the entire supply chain, as evidenced by the lit-
erature network analysis in Figure 2.
Based on our classiﬁcation of the literature by supply chain processes, we found that studies are still conﬁned to iso-
lated areas of the supply chain. Most of the research activities are in two of the supply chain processes, namely make
and deliver. In fact, there are logical explanations for this.
The roots of IoT in logistics are not new. Using technology for the tracking of objects has been around for decades
through various forms of information and communication technologies. Therefore, improvements brought by IoT to the
logistics function can be viewed as a continuation to previous developments. The basic logistics functions are to trans-
port ‘the right goods in the right quantity and right quality at the right time to the right place for the right price’(Decker
et al. 2008). Product identiﬁcation through RFID informs the system about the right goods. Tracing allows the detection
of when items are lost and guarantees the right quantities. Location tracking guarantees the right place aspect. Monitor-
ing the product state ensures the right quality. This information provides the necessary visibility that allows responsive-
ness to unforeseen events and taking action at the right time and the optimisation of the whole process.
Similarly, the roots of IoT in manufacturing go back to the 80s with various forms of automation, robotics, com-
puter-integrated manufacturing and computer-aided manufacturing. What was computer-based then is now web-based in
addition to smart objects capable of machine-to-machine communication and decision-making. What is now smart man-
ufacturing and industry 4.0 is building on these early developments. This explains the high research activities in the
‘make’supply chain process. This can also be explained by several aspects of the ‘make’process that can be enhanced
with IoT applications such as quality, maintenance and several aspects of internal integration as mentioned in Sec-
Another area of great interest to both researchers and practitioners is the applications of IoT in the food supply
chain. The food supply chain is an extremely challenging domain from a management perspective as it deals with per-
ishable products and involves many actors along the chain. One-third of the food produced worldwide is lost or wasted
according to the FAO (Gustavsson et al. 2011;FAO2013). Stuart (2009) estimates that in North America and Europe,
30–50% of the food supply is discarded. In addition, food supply chains are an integral part of every economy that can-
not be offshored. Thus, preventing avoidable food waste generation, food safety and efﬁciency throughout the food sup-
ply chain is a compelling potential of the application of IoT and explains the growing interest in this particular area. We
expect that research in this area will continue to grow.
International Journal of Production Research 4733
Although we did not focus on the technology aspects of IoT in our review, it is worth noting that there is a lot of
research in the technology area whether software or hardware for understandable reasons since technology is the main
enabler of IoT. This can be seen from the many reviews and surveys that appeared in the literature (Atzori, Iera, and
Morabito 2010; Miorandi et al. 2012; Xu, He, and Li 2014; Li, Xu, and Zhao 2015; Whitmore, Agarwal, and Xu 2015;
Dey et al. 2016; Mishra et al. 2016; Ng and Wakenshaw 2017).
Based on our literature review, we summarise the role and impact of IoT on major supply chain processes in Table 9.
We include the four major SCOR processes: source, make, deliver and return. We list the role that IoT plays for each
process as well as the impact and the corresponding references.
There are several gaps in the current literature dealing with IoT applications in SCM that can be identiﬁed from the
current review. These gaps can be summarised as follows:
•Lack of solid frameworks that provide guidance of IoT adoption in a supply chain context with clear guidelines
and a roadmap. These would help in advising companies as to which process and where in the supply chain would
they deploy IoT, given that supply chain partners may be at different stages of the IoT implementation. In addi-
tion, these frameworks would provide help with change management practices within the company and across the
•Lack of models that address supply chain problems in an IoT environment. Management of smart supply chains is
different from that of traditional supply chains. Decision-making in an IoT context requires new tools and models
that take into account this new environment, such as the abundance of big data generated from sensors and con-
nected things. IoT will affect procurement, production planning, the management of inventory, quality and mainte-
nance, among other issues.
•There are several barriers to the implementation of IoT in SCM from both technological and managerial perspec-
tives. A world where all things are connected opens the door for less security and privacy (Tadejko 2015). This is
especially true in a supply chain context where information sharing has always been a big challenge. Another
challenge is interoperability. Research by McKinsey suggests that 40% of the value of the IoT will need to be
unlocked via interoperability (Manyika et al. 2015). There is not much research addressing how to deal effectively
with these challenges.
6. Conclusion and future research directions
In this paper, we provided an account of the latest developments in the application of IoT to various supply chain pro-
cesses and areas of supply chain management. As such, we explored IoT in an SCM context, presented its main technol-
ogy enablers and provided an IoT deﬁnition in an SCM context. We organised IoT applications around key supply
chain processes. We provided a bibliometric analysis of a representative body of literature up to mid-2017. We identiﬁed
the gaps in the literature with respect to the potential of IoT role in helping address supply chain management chal-
lenges. The aim is to provide an informative overview of the latest development in this emerging and growing area,
which is of interest to both researchers and practitioners.
We conclude this paper by pointing out several possible venues for future research. Below we describe some possi-
bilities, with a focus on modelling and optimisation in different application area:
•Maintenance: Two research questions are relevant in the area of maintenance. First, what is the optimal placement
of sensors and alert initiation? Second, with the predominance of ‘smart’items what is the optimal scheduling of
autonomous repair operations? Other interesting topics include looking at optimal inspection under discrete moni-
toring sensors and condition-based maintenance analytics (Bowman et al. 2009). Finally, an important research
topic is exception analytics: How do we decide an exception is serious enough to take a certain action?
•Virtual Network Flow Design and Optimisation: Virtualisation of supply chains is making it possible to decouple
physical ﬂow from coordination and planning (e.g. Verdouw, Beulens, and van der Vorst 2013). It thus becomes a
challenge to optimise ﬂow in virtual supply networks that dynamically change their conﬁguration depending on
the state of the physical supply chain system.
•Costing: An important question for ﬁrms is the costing of IoT technology. For example, how to best determine the
economic and ecological value of sensor information (Bowman et al. 2009)?
•Vehicle routing: Real-time tracking and re-optimisation of routing and schedules will be a daily reality in the IoT
age. For example, how to re-optimise routes in humanitarian logistics, where some routes/truck may be interdicted,
or in food supply chains, where products could be rerouted based on their quality level, as alluded to by Pang
et al. (2015)?
4734 M. Ben-Daya et al.
•Quality-controlled logistics: Quality-controlled logistics for perishable goods that incorporates decisions from sup-
pliers to issue replacement shipments, engineers to perform corrections (Bowman et al. 2009, p 19) as well as the
possibility of customers looking for an alternative source. As in Heising, Claassen, and Dekker (2017), an interest-
ing question is looking at joint optimal quality and pricing in food supply chain logistics where expiry dates may
be dynamic. An important application is in perishable goods monitoring analytics (Bowman et al. 2009) that looks
at both supply and demand aspects. For the supply of perishable products, we would consider the material compo-
sition, physical changes, environmental factors (such as temperature, humidity, gas concentrations and shock) and
chemical reactions. For demand, we would take into account several measures such as aesthetic appearance, tex-
ture, ﬂavour and nutritional value.
The authors are grateful to four reviewers for their careful reading of the paper and their valuable feedback.
No potential conﬂict of interest was reported by the authors.
This work was supported by American University of Sharjah; Natural Sciences and Engineering Research Council of Canada.
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