An Internet of Things (IoT)-based risk monitoring system for managing cold supply chain risks
| Published date | 13 August 2018 |
| DOI | https://doi.org/10.1108/IMDS-09-2017-0384 |
| Pages | 1432-1462 |
| Date | 13 August 2018 |
| Author | Y.P. Tsang,K.L. Choy,C.H. Wu,G.T.S. Ho,Cathy H.Y. Lam,P.S. Koo |
| 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 |
An Internet of Things (IoT)-based
risk monitoring system for
managing cold supply chain risks
Y.P. Tsang and K.L. Choy
Department of Industrial and Systems Engineering,
The Hong Kong Polytechnic University, Hong Kong
C.H. Wu
Department of Supply Chain and Information Management,
Hang Seng Management College, Shatin, Hong Kong
G.T.S. Ho and Cathy H.Y. Lam
Department of Industrial and Systems Engineering,
The Hong Kong Polytechnic University, Hong Kong, and
P.S. Koo
AOC Limited, Hong Kong
Abstract
Purpose –Since the handling of environmentally sensitive products requires close monitoring under
prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e.
maintaining good environmental conditions, and ensuring occupational safety in the cold environment.
The purpose of thispaper is to propose an Internet of Things (IoT)-basedrisk monitoring system (IoTRMS)for
controllingproduct quality and occupationalsafety risks in cold chains. Real-timeproduct monitoring and risk
assessmentin personal occupational safetycan be then effectively establishedthroughout the entire cold chain.
Design/methodology/approach –In the design of IoTRMS, there are three major components for risk
monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic
approach. The wireless sensor network is deployed to collect ambient environmental conditions
automatically, and the collected information is then managed and applied to a product quality degradation
model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated
occupational safety risk of the different cold chain parties considering specific personal health status.
To examine the performance of the proposed system, a cold chain service provider is selected for conducting a
comparative analysis before and after applying the IoTRMS.
Findings –The real-time environmental monitoring ensures that the products handled within the desired
conditions, namely temperature, humidity and lighting intensity so that any violation of the handling
requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different
cold chain facilities, the personal occupational safety risk assessment is established by considering the
surrounding environment and the operators’personal health status. The frequency of occupational safety
risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker
satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing
and identifying product quality and occupational safety risks in cold chain activities.
Originality/value –The cold chain is developed for managing environmentally sensitive products in the
right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental
conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is
a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this
paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and
artificial intelligence techniques. The risk assessment and identification can be effectively established,
resulting in secure product quality and appropriate occupational safety management.
Keywords Internet of things, Fuzzy logic, Cold chain, Wireless sensor network, Risk monitoring
Paper type Research paper
Industrial Management & Data
Systems
Vol. 118 No. 7, 2018
pp. 1432-1462
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-09-2017-0384
Received 1 September 2017
Revised 24 November 2017
Accepted 25 December 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
The authors would like to thank the Research Office of the Hong Kong Polytechnic University for
supporting the project (Project Code: RUDV).
1432
IMDS
118,7
1. Introduction
Cold chain management (CCM) has been growing in the past few decades. Unlike traditional
supply chain management, the goods in cold chains, such as pharmaceutical products,
chilled food and frozen food, generally a have shorter shelf life and higher sensitivity to the
surrounding environment, i.e. temperature, humidity and lighting intensity (Gormley et al.,
2000). It thus requires certain refrigeration and dehumidification systems throughout the
entire cold chain in order to maintain the prescribed environmental conditions. In particular,
the ambient temperature for handling goods in a cold chain varies from −25°C to +10°C,
depending on the type of goods (Lana et al., 2005; Soyer et al., 2010). However, when
handling goods in an environment with such a low temperature, special attention should be
paid to the potential risks that may directly affect the product quality and operational
efficiency. Companies may suffer loss if any potential risks emerge along the cold chain.
For instance, in 2017, Lucky’s Market tossed all temperature-sensitive food, including
cheese, juices and fresh cut meat, because they were stored at 16°C (~60°F), and could not
meet the storage requirement for keeping the products below 40°F (Nerbovig, 2017). About 1
in 6 Americans get foodborne illness annually from tainted food which is handled under
improper temperature (Wein, 2014). On the other hand, excessive exposure of food handlers
to a cold environment may cause serious health effects and contribute to accidents of death
and injuries (Rice, 2014). In total, 15 workers died and 26 workers were injured at a Shanghai
cold storage facility due to unexpected ammonia leakage (Laurence, 2013). The above
reported cases show that the occurrence of cold chain risks affects not only the product
quality and consumer health, but also the safety of personnel who work in the cold
environment. Therefore, an effective risk monitoring system, especially for: product quality
risk; and occupational safety risk, is vital to track and evaluate the levels of risk throughout
the cold chain. In general, product quality risk is the degree to which a product does not
satisfy customers’requirements that is caused by product deterioration and contamination
throughout the cold chain; occupational safety risk is the degree of exposure to workplace
hazards, such as an extraordinary cold environment, among different supply chain facilities.
Figure 1 shows a typical cold chain, and the existing problems, for handing frozen food.
Among theentire cold chain, it is importantto ensure that the productsare stored and handled
under the proper environmental conditions for maintaining good product quality. Any
abnormalenvironmental changes shouldbe visible and realized by all other cold chainparties.
As shown in the figure,each party would performindividual quality checkingwhen receiving
goods. However, without the environmental information that is shared by the upstream
supply chain parties,only the goods arrival temperature can be measured. There is a chance
that the goods have already deteriorated or been contaminated during handling by other
parties or during the transportation. Real time environmental monitoring and control is
deemed to be essentialto increase product visibilityand traceability with the relatedparties in
the cold chain.The Internet of Things (IoT) is a globalstructured network for interconnecting
everyday objects which are equipped with intelligence, identification, and sensing
technologies (Yang, 2014). By applying the IoT paradigm, products and surrounding
conditions in the coldchain can be tracked and traced automatically,resulting in transparent
CCM. In addition, the quality degradation can be measured in a real-time manner.
On the other hand, depending on the product type, the condition of working
environment has to be kept constantly at a low temperature. For example, chilled food
should be kept at 0−10°C while frozen food should be kept below −15°C. To work under
such environment with low temperature, there is a high likelihood of certain occupational
safety risks, including cold-related illnesses (e.g. asthma and rhinorrhea), cold-related
symptoms (e.g. wheezing and chest pain), and cold injuries (e.g. frostbite and trench foot)
(Mäkinen and Hassi, 2009). The occurrence of occupational safety risks would directly
affect the working performance of staff, resulting in a decrease in operation efficiency,
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IoT-based risk
monitoring
system
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