An Ontology-based Bayesian network modelling for supply chain risk propagation
| Published date | 09 September 2019 |
| DOI | https://doi.org/10.1108/IMDS-01-2019-0032 |
| Date | 09 September 2019 |
| Pages | 1691-1711 |
| Author | Shoufeng Cao,Kim Bryceson,Damian Hine |
| Subject Matter | Information & knowledge management |
An Ontology-based Bayesian
network modelling for supply
chain risk propagation
Shoufeng Cao and Kim Bryceson
School of Agriculture and Food Sciences,
The University of Queensland, Brisbane, Australia, and
Damian Hine
UQ Business School, The University of Queensland, Brisbane, Australia
Abstract
Purpose –Supplychain risks (SCRs) do not workin isolation and have impactboth on each member of a chain
and the performanceof the entire supply chain. Thepurpose of this paper is to quantitativelyassess the impact
of dynamic risk propagation within andbetween integrated firms in global fresh produce supply chains.
Design/methodology/approach –A risk propagation ontology-based Bayesian network (BN) model
was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier
Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower
and exporter and a downstream Chinese integrated importer and online retailer.
Findings –An ontology-basedBN can be generated to accuratelyrepresent the risk domain of interestusing
the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses
revealedthat supply discontinuity,product inconsistencyand/or delivery delayoriginating in the upstreamfirm
can propagate to increase the downstreamfirm’s customer value risk and business performance risk.
Research limitations/implications –The work was conducted in an Australian-China table grape supply
chain, so results are only product chain-specific in nature. Additionally, only two statevalueswereconsideredfor
all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it
may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain.
Practical implications –This study supports the backward-looking traceability of risk root
causes through the ACTG SC and the forward-look ing prediction of risk pr opagation to key risk
performance measures .
Social implications –The methodology used in this paper provides an evidence-based decision-making
capability as part of a system-wide risk management approach and fosters collaborative SCR management,
which can yield numerous societal benefits.
Originality/value –The proposed methodology addresses the challenges in using a knowledge-based
approachto develop a BN model,particularly witha large-scale modeland integrates riskand performancefor a
holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.
Keywords Supply chain risk management, Global supply chain management, Risk performance,
Fresh produce, Ontology-based Bayesian network, Risk propagation
Paper type Research paper
1. Introduction
Companies traditionally focus risk management efforts within their boundaries
(Revilla and Saenz, 2017). As such, previous studies have mostly investigated supply
chain risks (SCRs) at a firm level (Świerczek, 2016). However, SCRs do not work in
isolation and risk outcome of one firm can be easily transformed into a risk event for
another firm (Manuj and Mentzer, 2008). Risk dependency and propagation has thus
become a critical issue in today’s supply chains, particularly in globally dispersed supply
chains where companies increasingly align key suppliers and buyers for competitive
advantage. It is thus vital to understand and manage risk dependencies across inter-firm Industrial Management & Data
Systems
Vol. 119 No. 8, 2019
pp. 1691-1711
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-01-2019-0032
Received 20 January 2019
Revised 28 May 2019
19 June 2019
Accepted 5 July 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
This study was supported by an “Australian Government Research Training Program Scholarship”.
The authors thank the two anonymous reviewers whose comments and suggestions greatly assisted
with improving the quality of the manuscript.
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An Ontology-
based Bayesian
network
modelling
operations as practical examples prove that risk consequences at the inter-firm level may
be costly for supply chains (Świerczek, 2016). Knowing how to anticipate risk propagation
within and between firms can enable predictive or proactive risk management strategies
for risk interdependence, thus creating competitive advantage for firms and the entire
supply chain (Ouabouch and Paché, 2014).
To investigate the issue of supply chain risk propagation, a BN approach is often used to
develop a risk network map in a supply chain (e.g. Badurdeen et al., 2014, Garvey et al., 2015,
Qazi et al., 2017; Ojha et al., 2018). However, a significant challenge in a knowledge-based BN
for SCRs is the proper identification of risk events and risk categories that can impact a
supply chain (Lockamy and McCormack, 2012). There is also a debate around building a
suitable structural model from expert knowledge due to possible subjective judgements
(Cowell et al., 2007).
Agri-food supply chains that involve the production of raw and/or processed food
products to the consumer are more vulnerable to risk propagation due to specific product
and process characteristics, such as variable harvest and production yields and the huge
impact of weather conditions on product availability, perishability of end products and
consumer demand (Van der Vorst and Beulens, 2002). Moreover, agri-food supply chains
often have a dynamic and non-transparent structure due to the prevalence of transactional
relationships (Roth et al.,2008).Asaresult,firmsfind it more difficult to see the
detrimental eff ects flowing from o ne part of a supply c hain to another (W u et al., 2007).
Without understanding risk interactions within and between firms, food supply chain
firms may attempt to implement company-specific risk mitigation strategies, which may
not lead to the desired performance (Srivastava et al., 2015) and even negatively impact
another firm’s performance.
While increasing works have examined risk propagation in a supply chain context
(e.g. Wu et al., 2007; Shin et al., 2012; Badurdeen et al., 2014; Garvey et al., 2015;
Qazi et al., 2017; Qazi et al., 2018; Ojha et al., 2018), risk quantification in the food supply
chain has received limited attention in the literature (Rathore et al.,2017),andthereisan
apparent shortage of quantitative investigation of risk propagation in global fresh
produce supply chains (GFPSCs). While some studies have quantified risk dependency
and propagation in food supply chains, their investigations are mainly focussed on the
firm level, rather than a whole chain (e.g. Diabat et al., 2012; Chaudhuri et al., 2016;
Prakash et al., 2017).
To fill these research gaps, this research developed a risk propagation modelling
framework for a two-tier GFPSC featured with an upstream Australian integrated grower
and exporter and a downstream Chinese integrated importer and online retailer to answer
the two questions:
(1) What risks would propagate within and between firms to impact the supply chain
performance along the GFPSC?
(2) What influence does risk propagation have on upstream firms’customer value risk
and furtheron downstream firms’customervalue risk and business performancerisk?
1.1 Purpose
The purpose of this research was to create a risk propagation ontology-based BN
model to address the challenges in a knowledge-based approach and to quantitatively
measure the impact of dynamic risk propagation in a GFPSC. The methodology
directly used the knowledge and inference capabilities inherent in a risk propagation
ontology, which could facilitate the creation of a BN to accurately represent the risk
domain of interest.
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