IoT-based production logistics and supply chain system – Part 2. IoT-based cyber-physical system: a framework and evaluation
| Date | 05 February 2018 |
| Published date | 05 February 2018 |
| DOI | https://doi.org/10.1108/IMDS-11-2016-0504 |
| Pages | 96-125 |
| Author | Mengru Tu,Ming K. Lim,Ming-Fang Yang |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
IoT-based production logistics
and supply chain system –Part 2
IoT-based cyber-physical system:
a framework and evaluation
Mengru Tu
Department of Transportation Science,
National Taiwan Ocean University, Keelung, Taiwan
Ming K. Lim
Centre for Business in Society, Coventry University, Coventry, UK, and
Ming-Fang Yang
Department of Transportation Science,
National Taiwan Ocean University, Keelung, Taiwan
Abstract
Purpose –The purpose of this paper is threefold: to present internet of things (IoT)-based cyber-physical
system (CPS) architecture framework to facilitate the integration of IoT and CPS; to implement an IoT-based
CPS prototype based on the architecture framework for a PL application scenario of in a case study; and to
devise evaluation methods and conduct experimental evaluations on an IoT-based CPS prototype.
Design/methodology/approach –The design research method, case study, emulation experiment method,
and cost-benefit analysis are applied in this research. An IoT-based CPS architecture framework is proposed,
and followed by the development of prototype system and testbed platform. Then, the emulation and
experimental evaluation of IoT-based CPS are conducted on the testbed, and the experimental results
are analyzed.
Findings –The emulation experiment results show that the proposed IoT-based CPS outperforms current
barcode-based system regarding labor cost, efficiency, and operational adaptiveness. The evaluation of the
IoT-based CPS prototype indicates significant improvements in PL tasks and reduced part inventory under a
dynamic changing shop-floor environment.
Practical implications –The case study shows that the proposed architecture framework and prototype
system can be applied to many discrete manufacturing industries, such as automobile, airplane, bicycle, home
appliance, and electronics.
Originality/value –The proposed IoT-based CPS is among the first to address the need to integrate IoT and
CPS for PL applications, and to conduct experimental evaluations and cost-benefit analysis of adopting IoT-based
CPS for PL. This paper also contributes to the IoT research by using diverse research methods to offer broader
insights into understanding IoT and CPS.
Keywords RFID, Internet of things (IoT), Emulation, Production logistics, Cyber-physical system (CPS)
Paper type Research paper
1. Introduction
Enterprises around the world are facing increasingly severe global competition, the shorter
life cycle of new products, and changing customer demands. Therefore, they must transform
their business operations to provide greater product variety and customization through
flexibility and quick responsiveness, and also to remove the data latency, analysis latency,
as well as decision latency as much as possible (Hackathorn, 2003). Mass customization
often requires firms to manufacture and deliver customer-specific products or services with
the same price and efficiency as mass-produced products. In response to the new business
model, they must adapt new information systems that can manage dynamic manufacturing
activities and take immediate action to resolve any events that disrupt production or cause
customer dissatisfaction (Byrd et al., 2006). Coupling mass customization, just in time ( JIT),
Industrial Management & Data
Systems
Vol. 118 No. 1, 2018
pp. 96-125
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-11-2016-0504
Received 20 November 2016
Revised 18 May 2017
Accepted 24 June 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
96
IMDS
118,1
and lean production with real-time business intelligence will enable a firm to compete in
today’s hyper competition environment (Du et al., 2006). In other words, firms must
re-engineer their current businesspractices to a real-time enterprise(RTE) operational model,
which uses up-to-date information to eliminate business process delays (Kopitsch, 2005).
However, in the mass customization environment, the execution process of a production
system is frequentlydisrupted by internal and external dynamics, such as equipment failure
and changing customer orders (Qu et al., 2016). The term production logistics (PL) describes
these execution processes as logistics activities related to material transfer between
production stages, and PL often accounts for nearly 95 percent execution time of the entire
manufacturing process (Qu et al., 2016). To effectively employ mass customization and JIT
production for RTE models, auto-ID methods are required for near real-time process control
(Hansen and Gillert, 2008). Many manufacturing firms already adopted auto-ID to manage
their PL activities. The enabling technologies for auto-ID that attracted the most attentionin
recent years include radio frequency identification (RFID) and internet of things (IoT). More
specifically, IoT extends into our everyday lives through a wireless network of uniquely
identifiable objects and forms a global infrastructure of networked physical objects
(Welbourne et al., 2009). This paper (part B of the research) extended the implementation
architecture proposed in part A of this research. In Part A of the research, we proposed an
implementation architecture which employs employing IoT technologies and comprises one
IoT cloud and several iNodes, where eachiNode manages multiple IoT devices.We called the
proposed implementation architecture an IoT-based cyber-physical system (CPS) for PL and
supply chain applications. Therefore, this paper is clearly linked to Part A of this research.
IoT technology has been adopted by a wide range of industries in both indoor assets
tracking (Thiesseet al., 2006; Zhang et al., 2007; Wang et al., 2010) and outdoorassets tracking
(Choi et al., 2012). Recent studies also show that integrating IoT technology, such as RFID,
in shop floor operations can greatly optimize and improve manufacturing and PLoperations
(Qiu, 2007;Zhou et al., 2007; Huang et al., 2008; Ruey-Shun et al., 2008; Wang et al., 2012; Zhong
et al., 20 13). The basicinfrastructure of IoT consistsof electronic product code and EPCglobal
network (Thiesse et al., 2009; Yan and Huang, 2009), which provide a flexible and
scalable information system architecture for implementing a range of applications, such as
anti-counterfeit (Kwok et al., 2010) and information sharing (Yan et al., 2016). To fully realize
the potential benefits of IoT technology, firms must adopt a new IT infrastructure that can
better track and managea large volume of distributed objects withintheir organizations and
beyond. As we are moving toward the world of IoT, millions of embedded devices and
industrial machines empowered with internet technologies will be able to communicate,
collaborate,and offer their functionality as a machine to machine (M2M) service (Karnouskos
et al., 2009). A device-to-business integration infrastructure is also required to manage
dynamic business processes in the shop-floor environment (Karnouskos et al., 2007).
Interacting with objects/things is the inherent nature of IoT systems, implying that IoT
systems must relate and handle both physical and cyber worlds together. Hence, CPSs are
introduced to bridge the gap between the physical and digital divide in IoT systems.
PL involves manymanufacturing and logistics activities beyond thefour walls of a company
and includes many supply chain partners. These PL activities are supported by many
resources, including CNC machines, robots, conveyors, operators, and all kinds of sensors to
facilitate thesmooth operation of PL tasks. ThusPL must integrate production resources and
activities across the entire manufacturing supply chain, and the integration poses a great
challenge to PLinformation systems. The CPS showsthe promise to integrate theseactivities
and resources by synchronizing information between cyber and physical worlds and sharing
production information between different stakeholders at different locations across a
distributed and collaborative supply chain (Wang et al., 2015). CPS must also integrate with
IoT, cloud computing,and many other information technologies to support PL activities in a
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IoT-based CPS
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