A fuzzy-based House of Risk assessment method for manufacturers in global supply chains

DOIhttps://doi.org/10.1108/IMDS-10-2017-0467
Pages1463-1476
Published date13 August 2018
Date13 August 2018
AuthorHoi-Lam Ma,Wai-Hung Collin Wong
Subject MatterInformation & 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
A fuzzy-based House of Risk
assessment method for
manufacturers in global
supply chains
Hoi-Lam Ma and Wai-Hung Collin Wong
Department of Supply Chain and Information Management,
Hang Seng Management College, Shatin, Hong Kong
Abstract
Purpose Risk management is crucial for all organizations, especially those in the global supply chain
network. Failure may result in huge economic loses and damage to company reputation. Risk assessment
usually involves quantitative and qualitative decisions. The purpose of this paper is to apply fuzzy logic to
capture and inference qualitative decisions made in the House of Risk (HOR) assessment method.
Design/methodology/approach In the existing HOR model, aggregate risk potential (ARP) is calculated
by the risk event times the risk agent value and its occurrence. However, these values are usually obtained
from interviews, which may involve subjective decisions. To overcome this shortcoming, a fuzzy-based
approach is proposed to calculate ARP instead of the current deterministic approach.
Findings Risk analyses are conducted in five major categories of risk sources: internal, global environment,
supplier, customer and third-party logistics provider. Moreover, each category is further divided into different
sub-categories. The results indicate that the fuzzy-based HOR successfully inferences the inputs of the risk
event, risk agents and its occurrence, and can prioritize the risk agents in order to take proactive decisions.
Practical implications The proposed fuzzy-based HOR model can be used practically by manufacturers
in the global supply chain. It provides a framework for decision makers to systematically analyze the
potential risks in different categories.
Originality/value The proposed fuzzy-based HOR approach improves the traditional approach by more
precise modeling of the qualitative decision-making process. It contributes to a more accurate reflection of the
real situation that manufacturers are facing.
Keywords Risk management, Risk assessment, Global supply chain, Risk analysis, House of Risk
Paper type Research paper
1. Introduction
According to ISO, Guide 73 (2009), risk is defined as an effect which is a deviation from the
expectedpositive and/or negative.No doubt that risk should refer to the negative side,
e.g. hazard risks and uncertainty risks. It is known that risk is defined as a combination of
the consequences of an event and the associated likelihood. In reference to the definition
of risk management (RM), it should be the coordination of different activities in order to
direct and control an organization with regard to risk (Wang 2009). Similarly, Fone and
Young (2000) noted that RM could be taken as a function for general management so that
managers could evaluate risks in order to achieve a firms overall objectives.
Traditionally, RM involves two major processes: occurrence id entification and
evaluation, and consequences identification and evaluation. Smallman (1996) held the
view that the process is not necessarily formal and structured in effective RM while common
sense toward risk is more essential. Whereas in academia, there is a tendency to a more
formalized and structured methodology for managing risks (Cox and Townsend, 1998; Khan
and Burnes, 2007; Chung et al., 2017). White (1995) pointed out that most approaches toward
RM include three critical stages: risk identification, risk estimation and risk evaluation.
Later on, Sople (2012) further proposed four processes in RM: identify risk, measure risk,
manage risk and monitor risk.
Industrial Management & Data
Systems
Vol. 118 No. 7, 2018
pp. 1463-1476
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-10-2017-0467
Received 7 October 2017
Revised 6 December 2017
Accepted 13 December 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1463
Fuzzy-based
HOR
assessment
method

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