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Supply Chain Forum: An International Journal
ISSN: 1625-8312 (Print) 1624-6039 (Online) Journal homepage: http://www.tandfonline.com/loi/tscf20
An examination of external risk factors in Apple
Inc.’s supply chain
Archie Lockamy III
To cite this article: Archie Lockamy III (2017) An examination of external risk factors in Apple
Inc.’s supply chain, Supply Chain Forum: An International Journal, 18:3, 177-188
To link to this article: http://dx.doi.org/10.1080/16258312.2017.1328252
Published online: 16 May 2017.
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ARTICLE
An examination of external risk factors in Apple Inc.’s supply chain
Archie Lockamy III
Department of Entrepreneurship, Management, & Marketing, Brock School of Business, Samford University, Birmingham, AL, USA
ABSTRACT
The Apple Computer Company has evolved into one of the most innovative technology-
based firms to transpire in the last three decades. Much of its success has been attributed to
effective supply chain management. The purpose of this study is to provide an examination
of external risk factors associated with Apple Inc.’s key suppliers through the creation of
Bayesian networks. The study sample consists of Apple Inc.’s top electronic equipment
suppliers based on its expenditure levels (e.g., cost of goods sold; selling, general and
administrative cost; research and development, etc.) directly associated with these firms.
Bayesian networks are used as a methodology for examining the supplier external risk profiles
for the study sample. The results of this study show that Bayesian networks can be effectively
used to assist managers in making decisions regarding current and prospective suppliers with
respect to their potential impact on supply chains as illustrated through their corresponding
external risk profiles.
KEYWORDS
Supply chain risk; Bayesian
networks; Apple Computer;
supplier external risk events;
global supply chains;
supplier riskmanagement
Introduction
Supply chains can be described as business entities
which coordinate with each other to achieve competi-
tiveness as well as their own interests (Long 2014). The
research literature demonstrates that the coordination
and integration of activities across the supply chain can
improve firm performance, and lead to a sustainable
competitive advantage for its membership
(Mackelprang et al. 2014). To facilitate supply chain
effectiveness, firms have adopted the tenets associated
with supply chain management (SCM) (Singh, Smith,
and Sohal 2005;Lietal.2006; Gunasekaran, Lai, and
Cheng 2008; Kirovska, Josifovska, and Kiselicki 2016).
SCM utilizes a comprehensive approach to addressing
the fundamental business problem of supplying pro-
duct to meet demand in a complex and uncertain
world (Kopczak and Johnson 2003). Hakansson and
Persson (2004) suggest that SCM can be characterized
as a strategic management concept that can contribute
to the competitiveness and profitability of the individual
firm as well as the entire supply chain. The execution of
SCM necessitates the management of information,
material and cash flows across multiple functional
areas both within and among organizations (Faisal,
Banwet, and Shankar 2006). A precondition to effective
SCM is the coordination of functional and supply chain
member activities with organizational strategies that are
aligned with organizational structures, core processes,
management cultures, incentive systems and human
capital (Abell 1999).
Firms involved in the strategic use of SCM meth-
odologies may find it necessary to alter their business
focus to reap its potential benefits (Kopczak and
Johnson 2003). These alterations may include
improvements in their ability to acquire and manage
reliable demand information (Croxton et al. 2002;
Wang, Ye, and Tan 2014); better management of phy-
sical goods flow through suppliers, manufacturers,
distributors and retailers for enhanced value to final
customers (Jammernegg and Reiner 2007; Melnyk,
Narasimhana, and DeCampos 2014); more focus on
cross-functional and cross-enterprise integration
(Chen and Kang 2007; Danesea and Bortolotti 2014);
and an increased emphasis on strategy alignment,
innovation and continuous improvement (Kushwaha
2012; Mandal and Korasiga 2016). Kushwaha notes
that effective SCM provides the means for organiza-
tions to mitigate the effects of rapid wage inflation in
previously low cost labour markets, spikes in com-
modity prices and escalating fuel prices via enhanced
flexibility and agility. Additionally, Mandal and
Korasiga argue that supply chains need to innovate
constantly in order to maintain their position in the
marketplace and to depress the impacts of uncertain-
ties. Enhancements in supply chain flexibility, agility
and innovation result in improved supply chain reac-
tivity, which leads to increased customer satisfaction
and value (Gaudenzi and Borghesi 2006). Supply chain
reactivity is defined as the network’s ability to com-
press lead times, adapt to unanticipated changes in
demand and to adjust to uncertainty in the business
CONTACT Archie Lockamy III aalockam@samford.edu Department of Entrepreneurship, Management, & Marketing, Brock School of Business,
Samford University, Birmingham, AL, USA
SUPPLY CHAIN FORUM: AN INTERNATIONAL JOURNAL, 2017
VOL. 18, NO. 3, 177–188
https://doi.org/10.1080/16258312.2017.1328252
© 2017 Kedge Business School
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environment. However, the integration of supply
chain networks via SCM creates interdependencies
among participating trading partners which make
them more susceptible to supply chain disruptions,
resulting in increased risks within the network.
The Apple Computer Company has evolved into
one of the most innovative technology-based firms
to transpire in the last three decades (Finkle and
Mallin 2010). Apple Inc. is responsible for bringing to
market such products as the Macintosh computer and
laptop; the iPod and iTunes; the iPhone; and the iPad.
The initial success of the firm is attributed to its
founder, Steven Jobs, and his friend, Steve Wozniak.
These individuals founded and built Apple Inc. into a
firm that achieved a market capitalization of over 700
billion dollar in 2014 (Bloomberg 2014). Jobs and
Wozniak incorporated a new product development
philosophy within Apple Inc. which focused on the
creation of leading-edge technological devices which
were visually aesthetic with a stylish design (Noble
and Kumar 2010).
Keen and Williams (2013) cites Apple Inc. as a firm
who has successfully developed a business strategy
based upon innovation, and the consistent creation of
‘value’as defined by its chosen markets. The authors
note that these firms are able to ‘leverage adaptive
“ecocomplexes”of relationships rather than go it
alone’. Apple Inc.’s ecocomplex is characterized by
its reliance on value chain members to produce its
tangible products and digital content that includes
music, video and photos (Keen and Williams 2013).
Moreover, Mitchell (2014) attributes Apple Inc.’s skill
as a ‘value chain integrator’to its ability to develop
products that continue to increase in demand among
targeted consumers. The author cites Apple’s skill in
this area via its demonstrated ability to face market
challenges, integrate new product development with
its value-creation proposition and differentiate pro-
duct offerings in its served markets.
Korkeamäki and Takalo (2013) conducted a study of
Apple Inc.’s global supply chain for its iPhone product.
The study estimates that 10–13% of the firm’smarket
capitalization is generated within this supply chain. The
authors argue that the majority of the value created
within the iPhone supply chain comes from the firm’s
unique dynamic managerial and organizational capabil-
ities in globally competitive and innovative industries.
Additionally, they credit Job’s focus on organizing Apple
to create a lasting culture of innovation as a critical
component of the firm’s consistent ability to outperform
its competition. Their study also notes that ‘rents’within
global supply chains tend to accrue to firms possessing
unique capabilities and resources. Therefore, it is essen-
tial that Apple Inc. continues to formulate supply chains
for its products which contain entities that consistently
supply it with the necessary inputs for sustaining its
unique ecocomplex.
The purpose of this study is to provide an exam-
ination of external risk factors associated with Apple
Inc.’s key suppliers through the creation of Bayesian
networks. These networks were constructed to deter-
mine the external risk probabilities of these suppliers
for the creation of supplier risk profiles. The risk pro-
files were used to assess a supplier’s potential impact
on the firm’s supply chain delivery system for its
served markets. Thus, this study attempts to provide
insights regarding Apple Inc.’s ability to sustain its
competitive advantage through its unique ecocom-
plex vis-à-vis the external risk profiles of key value
chain members which constitute its supply chain.
A review of the literature in the areas of SCM and
supply chain risks is presented in the next section of
the article to provide a theoretical foundation for the
study. Provided afterwards is an overview of the
research methodology used in the study, which
includes a discussion on Bayesian networks and data
collection procedures. Results and conclusions are
offered in the final sections of the article, which
include managerial implications, limitations and direc-
tions for future research.
Literature review
The commencement of the twenty-first century has
been marked by widespread disruptions in supply
chains caused by fuel protests, disease outbreaks,
terrorist attacks and the threat of weapons of mass
destruction (Jüttner 2006; Wagner and Neshat 2012).
Thus, the research literature has reflected a growing
interest in the area of supply chain risk management
by academics and practitioners over the past 16 years
(Lavastre, Gunasekaran, and Spalanzani 2014; Micheli,
Mogre, and Peregoa 2014; Ali and Shukran 2016). Risk
can be defined as the probability of variance in an
expected outcome (Spekman and Davis 2004).
Therefore, it is possible to quantify risk since it is
possible to assign probability estimates to these out-
comes (Khan and Burnes 2007). However, uncertainty
is not quantifiable and the probabilities of its possible
outcomes are not known (Knight 1921). A joint assess-
ment of risk and uncertainty conducted by Yates and
Stone (1992) reveals that risk implies the existence of
uncertainty associated with a given outcome, for if
the probability of an outcome is known, there is no
unknown risk. Thus, uncertainty can be viewed as a
key determinant of risk that may not be entirely era-
dicated, but can be mitigated through the deploy-
ment of risk reduction action steps (Slack and Lewis
2001). In business environments, managers are
expected to reduce the firm’s exposure to uncertainty
through the deployment of effective risk manage-
ment strategies. Thus, firms need to adopt systematic
approaches for the management of supply chain risks
(Jüttner 2006; Oehmen et al. 2009; Wagner and
178 A. LOCKAMY III
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Neshat 2012; Macdonald and Corsi 2013; Hachicha
and Elmsalmic 2014; Lavastre, Gunasekaran, and
Spalanzani 2014; Manuj, Esper, and Stank 2014;
Micheli, Mogre, and Peregoa 2014; Rajesh, Ravia, and
Rao 2015; Ali and Shukran 2016).
Uncertainties caused by economic business cycles,
consumer demands, and natural and man-made dis-
asters all provide sources for supply chain risks (Tang
2006; Wagner and Neshat 2012). These sources of
uncertainty can be categorized as ‘risk events’that
can lead to supply chain disruptions that impede
overall performance (Lockamy 2014). Researchers
Handfield and McCormack (2007) defined operational,
network and external factors as categories of supply
chain risks. Operational risk is defined as the risk of
loss resulting from inadequate or failed internal pro-
cesses, people or systems. Quality, delivery and ser-
vice problems are examples of operational risks.
Network risk is defined as risk resulting from the
structure of the supplier network, such as ownership,
individual supplier strategies and supply network
agreements. External risk is defined as an event driven
by external forces such as weather, earthquakes, poli-
tical, regulatory and market forces. This research study
examines external risk factors associated with Apple
Inc.’s key suppliers through the creation of Bayesian
networks.
Supply chain external risks
An examination of the supply chain literature in the
area of external risks factors reveals that the primary
focus has been on mitigating the effects of unfore-
seen events such as terrorist attacks (e.g. New York
2001, Madrid 2004, London 2005, Jakarta 2009), nat-
ural disasters (e.g. Tsunami 2004, Hurricane Katrina
2005, Taiwan earthquakes 1999, 2009, and 2010) and
diseases (e.g. SARS 2003, avian/bird flu 2005, swine flu
2009) on the integrity of the supply chain (Wagner
and Neshat 2012; Lockamy 2014). Although these risk
events can have detrimental effects on a firm’s supply
chain, there are additional external risk factors that
should be considered by firms while attempting to
develop practices which reduce supply chain vulner-
ability. External risk factors examined in the study of
Apple Inc.’s key suppliers are (1) country risk; (2) busi-
ness climate risk; (3) commercial risk; (4) logistics risk;
and (5) corruption risk.
Country risk refers to the exposure of a loss that an
investor is endangered to due to economic and sover-
eign issues in a country (Fruet-Cardozo, Cañas-
Madueño, and Millán de la Lastra 2014). This category
of external risk creates an unpredictable environment
for international buyer–supplier relationships, resulting
in the reduction of both parties’ability to effectively
manage the relationship (Griffith and Zhao 2015).
Political instability, changes in economic policy,
economic turbulence and exchange rate volatility are
all factors that lead to pronounced country business risk.
Due to the proliferation of global supply chains, country
risk analysis has become increasingly important as firms
attempt to define the potential for these risk events
(Stankevičienė, Sviderskė, and Miečinskienė2014).
Business climate risk can be viewed as a compo-
nent of country risk comprised of specific economic,
financial and political risk factors, which collectively
influence the level of unpredictability encountered by
firms who choose to operate in a particular sovereign
nation. Noneconomic and financial risk factors include
(1) government stability; (2) internal and external con-
flicts; (3) military’s political influence; (4) religious and
ethnic tensions; (5) laws and regulations; (6) demo-
cratic accountability; and (7) bureaucracy quality.
These factors, along with GDP, debt and export levels,
liquidity and exchange rate stability, are used to
establish international ratings of the level of business
climate risk associated with a country (Karabiyik and
Kara 2015). During the past decade, business climate
improvement has become a significant element of the
work conducted by governments in many countries,
which has been primarily encouraged and measured
by the World Bank (Mojsovska and Janeska 2015).
The World Bank’s Doing Business project, which
was launched in 2002, gathers quantitative data to
compare regulations faced by small and medium-
sized enterprises across economies and over time
(Besley 2015).
Commercial risk relates to cross-country differences
in equity volatility and uncertainty that are explained
by the interaction between firm and country charac-
teristics (Favara, Schroth, and Valta 2012). The
International Monetary Fund (IMF) provides a coun-
try-level assessment of this risk in its World Economic
Outlook and Global Financial Stability reports
(Vernengo and Ford 2014). The level of commercial
risk associated with a country directly affects its sover-
eign credit risk and its potential macroeconomic
impact on global financial markets (Augustin and
Tedongap 2016). For example, the degree to which
countries can mitigate the affect of country-specific
income shocks on their own domestic financial instru-
ments and markets is a function of their inherent level
of commercial risk (Mimir 2016).
Logistics risk refers to the degree to which a coun-
try can systematically and consistently fulfil essential
activities associated with the transportation, distribu-
tion, warehousing and packaging of materials within a
firm’s supply chain (Salanțăand Popa 2015). Inventory
management and reverse logistics are also cited as
critical components of effective SCM requiring mini-
mal levels of logistics risk (Vikulov and Butrin 2014).
Moreover, Jereb, Cvahte, and Rosi (2012) defined four
primary logistics resources which are necessary for
logistics processes to take place: (1) resources to
SUPPLY CHAIN FORUM: AN INTERNATIONAL JOURNAL 179
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facilitate the flow of goods and services from the
point of origin to the point of use in order to meet
the requirements of customers; (2) resources to
accommodate information flows which can trigger a
change in the state of the logistical system based
upon a change of knowledge; (3) logistics infrastruc-
ture and superstructure resources needed for the pro-
pagation of efficient and effective supply chain
operations; and (4) human resources to plan, orga-
nize, acquire, implement, deliver, support, monitor
and evaluate logistical systems and services.
Corruption risk refers to the degree to which poli-
tical institutions in sovereign nations can exercise
undue influence on the economic mechanisms of
the country. According to Ionescu (2010), corruption
raises the barriers to entry and exit of corrupt markets,
restrains exchange to insiders and augments the
importance of local partners. Additionally, this
researcher states that corruption leads to less effective
government, rests upon a foundation of an unfair
legal system and remains high in countries with low
trust and high levels of inequality. A major outcome
of political corruption is the inefficient allocation of
limited resources necessary for economic growth and
national prosperity (Hauser and Hogenacker 2014).
Heywood and Meyer-Sahling (2013) cite the following
indicators of corruption in political bureaucracies: (1)
personal versus impersonal exchange relations; (2)
mutual dependencies or multiple principals; (3)
absence of screening mechanisms; and (4) a lack of
incentives to develop reputations for honesty. This
research study incorporates country-specific data on
the aforementioned external risk factors associated
with Apple Inc.’s key suppliers.
Research methodology
This study employs a research methodology that
includes empirical data retrieved from the
Bloomberg Supply Chain Analysis Database (BSCAD),
and the creation of Bayesian networks used to con-
struct external risk profiles for Apple Inc.’s key suppli-
ers. Following is an overview of Bayesian networks,
along with a discussion of these suppliers.
Bayesian networks
Bayesian networks are annotated directed acyclic
graph that encode probabilistic relationships among
nodes of interest in an uncertain reasoning problem
(Pai et al. 2003). The representation describes these
probabilistic relationships and includes a qualitative
structure that facilitates communication between a
user and a system incorporating a probabilistic
model. Bayesian networks are based on the work of
the mathematician and theologian Rev. Thomas Bayes
who worked with conditional probability theory in the
late 1700s to discover a basic law of probability which
came to be known as Bayes’theorem. Bayes’theorem
states that
PHjE;cðÞ¼
PHjc
ðÞ
PEjH;c
ðÞ
PEjcðÞ
The posterior probability is given by the left-hand
term of the equation [P(H|E, c)]. It represents the
probability of hypothesis H after considering the
effect of evidence E on past experience c. The term
P(H|c) is the a-priori probability of H given c alone.
Thus, the a-priori probability can be viewed as the
subjective belief of occurrence of hypothesis H based
upon past experience. The likelihood, represented by
the term P(E|H,c), gives the probability of the evidence
assuming the hypothesis H and the background infor-
mation c is true. The term P(E|c) is independent of H
and is regarded as a normalizing or scaling factor
(Niedermayer 2003). Thus, Bayesian networks provide
a methodology for combining subjective beliefs with
available evidence.
Bayesian networks represent a unique class of gra-
phical models that may be used to depict causal depen-
dencies between random variables (Cowell, Verrall, and
Yoon 2007). Graphical models use a combination of
probability theory and graph theory in the statistical
modelling of complex interactions between such vari-
ables. Bayesian networks have evolved as a useful tool in
analysing uncertainty. When Bayesian networks were
first introduced, assigning the full probability distribu-
tions manually was time intensive. Solving a Bayesian
network with a considerable number of nodes is known
to be a nondeterministic polynomial time hard problem
(Dagum and Luby 1993). However, significant advance-
ments in computational capability along with the devel-
opment of heuristic search techniques to find events
with the highest probability have enhanced the devel-
opment and understanding of Bayesian networks.
Correspondingly, the Bayesian computational concept
has become an emergent tool for a wide range of risk
management applications (Cowell, Verrall, and Yoon
2007). The methodology has been shown to be espe-
cially useful when information about past and/or cur-
rent situations is vague, incomplete, conflicting and
uncertain.
Bayesian analysis in supply chain research
Pai et al. (2003) were among the first researchers to
analyse supply chain risks using Bayesian networks.
Their study examined the risk profile associated with a
US Department of Defense (DoD) supply chain for
trinitrotoluene (TNT). The supply chain was comprised
of TNT recovery plants, storage facilities and ammuni-
tion depots. Using Bayesian networks, the researchers
were able to establish risk factors and acceptable risk
limits for all assets contained in the DoD supply chain.
180 A. LOCKAMY III
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Bayesian networks have also been used to conduct
diagnostics (Kauffmann, Jacobs, and Fernandez 2002;
Kao, Huang, and Li 2005), cost optimization studies
(Narayanan, Raman, and Singh 2005), flexibility analy-
sis (Milner and Kouvelis 2005; Wu, Blackhurst, and
Chidambaram 2006) and manufacturing resource allo-
cation planning (Wu et al. 2013) in supply chains.
Since the work of Pai et al. (2003), researchers have
continued to explore the use of Bayesian networks to
analyse and manage supply chain risks. For example,
there have been a number of studies which examine
the use of Bayesian networks as part of a decision
support system to manage such risks (Li and Chandra
2007; Meixell, Shaw, and Tuggle 2008; Shevtshenko
and Wang 2009; Makris, Zoupas, and Chryssolouris
2011; Taskin and Lodree 2011). Studies by Tomlin
(2009) and Chen, Yusen Xia, and Wang (2010) demon-
strate how Bayesian networks can be used to manage
supply chain uncertainty. The integration of Bayesian
networks into supply chain forecasting methodolo-
gies to mitigate risks has also been examined by
several researchers (Yelland 2010; Yelland, Kim, and
Stratulate 2010; Rahman, Sarker, and Escobar 2011).
Lockamy and McCormack (2009) conducted a study
which uses Bayesian networks to examine operational
risks in supply chains. The authors have also used
these networks to analyse outsourcing risks in supply
chains (Lockamy and McCormack 2010). Finally,
Bayesian networks have been used to develop a
methodology for benchmarking supplier risks
(Lockamy 2011) and to create supplier disaster risk
profiles (Lockamy 2014).
Study sample and data collection
The study sample consists of Apple Inc.’s top electro-
nic equipment suppliers based on its incurred expen-
diture levels (e.g., cost of goods sold [COGS]; selling,
general and administrative cost; research and devel-
opment, etc.) directly associated with these firms. A
listing of these suppliers including their location and
relative rankings based on Apple’s cost exposure to
them is presented in Table 1. Five of Apple Inc.’s top
electronic equipment suppliers are location in Taiwan,
while three are located in Japan, two in South Korea,
and one in both the United States and Germany.
Information pertaining to the suppliers was
obtained via queries made in the BSCAD. The data-
base is accessible through a Bloomberg terminal
which provides users the ability to review quarterly,
semi-annual and annual financial reports of over
28,000 public companies. Additionally, the BSCAD
allows for the creation of a firm’s‘supply chain map’,
which includes its major suppliers and customers.
Data relating to the level of country, business cli-
mate, commercial, logistics and corruption risks asso-
ciated with each supplier were obtained in order to
develop their external risk profile in these areas. A.M.
Best Rating Services was used as a source for acquir-
ing country risk data for Apple Inc.’s key suppliers. A.
M. Best is the oldest and one of the most widely
recognized provider of ratings, financial data, and
news on over 3,500 companies in more than 80 coun-
tries worldwide. Best’s Credit Ratings are recognized
as a benchmark for assessing a rated organization’s
financial strength as well as the credit quality of its
obligations. A.M. Best defines country risk as the risk
that country-specific factors could adversely affect an
insurer’s ability to meet its financial obligations.
Countries are placed into one of five tiers, ranging
from Country Risk Tier 1 (CRT-1), denoting a stable
environment with the least amount of risk, to Country
Risk Tier 5 (CRT-5) for countries that pose the most
risk and, therefore, the greatest challenge to an
insurer’s financial stability, strength and performance.
Data obtained from the Coface Group were also
used to assess the level of country risk demonstrated
by Apple Inc.’s key suppliers. Established in 1946,
Coface offers risk prevention, monitoring and protec-
tion services to companies of all sizes and national-
ities, and in all sectors. The organization develops a
‘country risk assessment map’each quarter for 160
nations on the basis of macroeconomic, financial and
political data. Coface employs a eight-level ranking
system for countries in ascending order of risk: A1,
A2, A3, A4, B, C, D, and E. The ranking system is
illustrated in Table 2. Additionally, Coface data were
used to assess business climate risk. This external risk
factor is generated by the firm via the quarterly ana-
lysis of macroeconomic, financial and political data for
Table 1. Supplier profiles.
Supplier Location Rank (Cost to Apple)
Hon Hai Precision Industry Co. Taiwan 1
Pegatron Corp Taiwan 2
Quanta Computer Inc. Taiwan 3
Samsung Electronics S. Korea 4
LG Display Co., Ltd S. Korea 5
Sharp Corp./Japan Japan 6
Compal Electronics Taiwan 7
Taiwan Semiconductor Taiwan 8
Jabil Circuit Inc. USA 9
Schneider Electronics Germany 10
GungHo Online Entertainment Japan 11
Japan Display Inc. Japan 12
Table 2. Coface ranking system for country and business climate risk.
A1 A2 A3 A4 B C D E
Very Low Low Quite Acceptable Acceptable Significant High Very High Extreme
SUPPLY CHAIN FORUM: AN INTERNATIONAL JOURNAL 181
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160 countries. Business climate risk country rankings
are constructed using the same system illustrated in
Table 2.
After the upheaval that followed the First World
War, the Belgian government had the desire to create
a public body that would insure political risks and
revive exports. In 1921, the Belgian Ministry of
Economic Affairs set up the Delcredere Committee
to insure the political risks of exports. The
Delcredere Commitee was conceived as a temporary
entity but was maintained as crises emerged, such as
the 1929 Wall Street crash. In 1939, the government
decided to turn it into a permanent body, making it a
financially and administratively autonomous institu-
tion that is guaranteed by the government.
Delcredere was the source used to obtain commercial
risk data for Apple Inc.’s key suppliers. The institu-
tion’s commercial risk assessment model is composed
of three types of indicators: (1) economic and financial
indicators affecting all companies in a country due to
their impact on corporate results and balance sheets;
(2) indicators reflecting the country’s payment experi-
ence for commercial risk; and (3) indicators character-
izing the institutional context in which local
companies operate, such as political factors, transi-
tions occurring in the economy, etc. Delcredere uti-
lizes an ABC categorization scheme for assessing the
level of commercial risk associated with a given coun-
try. Category A includes countries presenting a low
commercial risk, Category B contains those countries
for which the corresponding commercial risk is
deemed as ‘normal’and Category C comprises the
countries presenting a high commercial risk. The sys-
temic commercial risk categorization for each country
is updated at least twice a year and is subject to
intermediary reviews, if necessary. Data obtained
from Delcredere were used to determine the level of
commercial risk associated with Apple Inc.’s key
suppliers.
The Logistics Performance Index (LPI) is a compre-
hensive measure of the efficiency of international
supply chains developed by the World Bank. Its first
version was published in 2007, and it has since been
updated every 2 years. The LPI is the weighted aver-
age of the country scores on six key dimensions: (1)
efficiency of the clearance process (i.e., speed, simpli-
city and predictability of formalities) by border control
agencies, including customs; (2) quality of trade and
transport related infrastructure (e.g., ports, railroads,
roads, information technology); (3) ease of arranging
competitively priced shipments; (4) competence and
quality of logistics services (e.g., transport operators,
customs brokers); (5) ability to track and trace con-
signments; and (6) timeliness of shipments in reach-
ing destination within the scheduled or expected
delivery time. It demonstrates comparative supply
chain performance using a 5-point scale ranging
from the lowest (1) to highest (5) score. The LPI data
were used to assess the level of logistics risk asso-
ciated with Apple Inc.’s key suppliers.
In 1993, a few individuals decided to take a stance
against corruption in political institutions, and created
Transparency International. The organization, whose
mission is to stop corruption and promote transpar-
ency, accountability and integrity at all levels, and
across all sectors of society, is now present in more
than 100 countries. Their vision is a world in which
government, politics, business, civil society and the
daily lives of people are free of corruption. Each
year, Transparency International constructs a
‘Corruption Perception Index’(CPI) for 168 countries
and territories. The index is based on expert opinion
from around the world. A country or territory’s score
indicates the perceived level of public sector corrup-
tion on a scale of 0 (highly corrupt) to 100 (very
clean). A country’s rank indicates its position relative
to the other countries. The CPI data for 2015 were
used to assess the level of corruption risk associated
with Apple Inc.’s key suppliers.
Data integration and analysis
The supplier external risk profiles for Apple Inc.’s key
suppliers are illustrated in Table 3. These profiles were
constructed using the collected data from the pre-
viously discussed sources. The profiles also reflect
the ranking systems associated with the data source
for a particular risk event. For example, the LPI uses a
5-point scale ranging from the lowest (1) to highest
(5) score. Therefore, the level of logistics risk asso-
ciated with Apple Inc.’s key suppliers was ranked
accordingly. Note that the LPI percentage ranking is
also included in the table. This ranking is a relative
measure of logistics risk based upon the country with
the highest LPI ranking (Germany).
A-priori probabilities for the external risk events
discussed previously were computed using the col-
lected data. This type of probability is calculated by
logically examining the existing information to deter-
mine what outcomes of an event are possible, along
with the chances that an outcome occurs for a parti-
cular event. The a-priori probabilities for the supplier
external risk event variables under examination in this
study are presented in Table 4. The probabilities were
calculated based upon the risk ranking system asso-
ciated with each risk event category. For example, the
risk event labeled ‘Country (1)’is derived from data
obtained from the A.M. Best Rating Services and
places countries into one of five tiers, ranging from
CRT-1, denoting a stable environment with the least
amount of risk, to CRT-5 for countries that pose the
most risk. Thus, the a-priori probability of this risk
event for a Tier 1 country is set at .20, while 1.00 is
the value associated with a Tier 5 country for this
182 A. LOCKAMY III
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event based on the methodologies employed by A.M.
Best. An a-priori probability external risk event profile
for Apple Inc.’s key suppliers is provided in Table 5.
The profiles were developed from the a-priori prob-
abilities for the supplier external risk event variables
under examination contained in Table 4. As illustrated
in Table 4, the probability of an external risk event for
a supplier located in a Tier 1 country is 20%, while
there is a 100% probability of occurrence for a sup-
plier located in a Tier 5 country. The integration of
data pertaining to a supplier’s country, business cli-
mate, commercial, logistics and corruption risks for
the establishment of the a-priori probabilities illu-
strated in Table 5 was used to construct Bayesian
networks for each supplier to determine their prob-
ability of experiencing an external risk event.
A depiction of the Bayesian networks used in this
study is illustrated in Figure 1. Nodes (circles) repre-
sent variables in the Bayesian network. Each node
contains states, or a set of probable values for each
Table 3. Supplier external risk profiles.
Supplier Location Rank (cost to Apple)
Country
risk
a
Country
risk
b
Business
climate
Commercial
risk
Logistics Performance
Index (LPI)
Corruption
Perceptions Index
Hon Hai Precision
Industry Co.
Taiwan 1 2 A3 A2 A 3.70 (83.6%) 62
Pegatron Corp Taiwan 2 2 A3 A2 A 3.70 (83.6%) 62
Quanta Computer Inc. Taiwan 3 2 A3 A2 A 3.70 (83.6%) 62
Samsung Electronics S. Korea 4 2 A3 A2 B 3.72 (84.2%) 56
LG Display Co., Ltd S. Korea 5 2 A3 A2 B 3.72 (84.2%) 56
Sharp Corp./Japan Japan 6 2 A2 A1 B 3.97 (92.1%) 75
Compal Electronics Taiwan 7 2 A3 A2 A 3.70 (83.6%) 62
Taiwan Semiconductor Taiwan 8 2 A3 A2 A 3.70 (83.6%) 62
Jabil Circuit Inc. USA 9 1 A2 A1 B 3.99 (92.8%) 76
Schneider Electronics Germany 10 1 A1 A1 A 4.23 (100.0%) 81
GungHo Online
Entertainment
Japan 11 2 A2 A1 B 3.97 (92.1%) 75
Japan Display Inc. Japan 12 2 A2 A1 B 3.97 (92.1%) 75
a
A.M. Best rating services.
b
Coface country risk assessments.
Table 4. A-priori probabilities for supplier external risk event variables.
Country risk
a
Risk event level CR1 CR2 CR3 CR4 CR5
Risk probability .200 .400 .600 .800 1.000
Country risk
b
Risk event level A1 A2 A3 A4 B C D E
Risk probability .125 .250 .375 .500 .625 .750 .825 1.000
Business climate risk
Risk event level A1 A2 A3 A4 B C D E
Risk probability .125 .250 .375 .500 .625 .750 .825 1.000
Commercial risk
Risk event level A B C
Risk probability .125 .250 .375
Logistics performance risk
Risk probability .200 .400 .600 .800 1.000
Corruption risk
Risk probability 1.000 .900 .800 .700 .600 .500 .400 .300
.200 .100
a
A.M. Best rating services.
b
Coface country risk assessments.
Table 5. A-priori probability external risk event profiles.
Supplier Country risk
a
Country risk
b
Business climate Commercial risk
Logistics Performance
Index (LPI)
Corruption Perceptions
Index (2015)
Hon Hai Precision Industry Co. .400 .375 .250 .340 .239 .400
Pegatron Corp .400 .375 .250 .340 .239 .400
Quanta Computer Inc. .400 .375 .250 .340 .239 .400
Samsung Electronics .400 .375 .250 .660 .237 .500
LG Display Co., Ltd .400 .375 .250 .660 .237 .500
Sharp Corp./Japan .400 .250 .125 .660 .217 .300
Compal Electronics .400 .375 .250 .340 .239 .400
Taiwan Semiconductor .400 .375 .250 .340 .239 .400
Jabil Circuit Inc. .200 .250 .125 .660 .215 .300
Schneider Electronics .200 .125 .125 .340 .200 .200
GungHo Online Entertainment .400 .250 .125 .660 .217 .300
Japan Display Inc. .400 .250 .125 .660 .217 .300
a
A.M. Best rating services.
b
Coface country risk assessments.
SUPPLY CHAIN FORUM: AN INTERNATIONAL JOURNAL 183
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variable. The values ‘yes’and ‘no’represent the two
states in which the variables can exist in the network
illustrated in Figure 1. Nodes are connected to show
causality with arrows known as ‘edges’which indicate
the direction of influence. When two nodes are joined
by an edge, the causal node is referred to as the
parent of the influenced (child) node. Child nodes
are conditionally dependent upon their parent
nodes. Thus, in Figure 1, the probability of suppliers
experiencing external risks is dependent on the
a-priori probabilities associated with the following
variables: country risk (1); country risk (2); business
climate risk; commercial risk; LPI risk; and corruption
risk.
Results
Bayesian networks were constructed for each of Apple
Inc.’s key suppliers using the a-priori probabilities
provided in Table 5. The results of this analysis are
presented in Table 6. An examination of Table 6
reveals that Apple’s South Korea suppliers (Samsung
Electronics and LG Display Co., Ltd) possess the high-
est probability of supply chain failure due to an exter-
nal risk event, while its German supplier (Schneider
Electronics) has the lowest risk of failure as a
consequence of experiencing such an event. Apple’s
Taiwanese suppliers (Hon Hai Precision Industry Co.;
Pegatron Corporation; Quanta Computer Inc.; Compal
Electronics; and Taiwan Semiconductor) have the sec-
ond highest probability of supply chain failure due to
an external risk event, followed by its Japanese (Sharp
Corp./Japan; GungHo Online Entertainment; and
Japan Display Inc.) and American (Jabil Circuit Inc.)
suppliers.
It is important to note that while the South Korea
suppliers pose the largest external risk to Apple’s
supply chain, these suppliers represent approximately
14.5% of the firm’s COGS according to the BSCAD.
However, Hon Hai Precision Industry Co. (Taiwan)
alone represents approximately 55% of Apple’s
COGS expenditures. In contrast, Jabil Circuit Inc. (St.
Petersburg, FL, USA) and Schneider Electronics
(Ratingen, Germany) collectively represent only 6%
of these expenditures.
Conclusions
This study examined external risk factors associated
with Apple Inc.’s key suppliers through the creation of
Bayesian networks. The results of the analysis illu-
strated in Table 6 suggest that Apple’s supply chain
exhibits a moderate risk of experiencing a disruption
due to an external risk event occurrence with a key
supplier. The probability of failure due to such an
event for these suppliers ranges from 20% to 40%,
with an average probability of approximately 33% for
an external risk event occurrence within Apple Inc.’s
supply chain. However, the information contained in
Tables 1 and 6indicate that Apple’s top three suppli-
ers based on the firm’s level of cost exposure to them
are in countries exhibiting the second highest prob-
ability of experiencing an external risk event (Taiwan).
Moreover, Apple Inc. has a total of five key suppliers
in this country. Although the average probability of
failure due to an external risk event for these suppliers
Supplier
External
RiskEvent
Country
Risk
(1)
Business
Climate
Risk
LPI
Risk
Country
Risk
(2)
Commercial
Risk
Corruption
Risk
Figure 1. Bayesian network for supplier external risk assessments.
Table 6. Probability of supplier failure due to an
external risk event.
Supplier Probability of failure (%)
Hon Hai Precision Industry Co. 33.98
Pegatron Corp 33.98
Quanta Computer Inc. 33.98
Samsung Electronics 39.95
LG Display Co., Ltd 39.95
Sharp Corp./Japan 33.78
Compal Electronics 33.98
Taiwan Semiconductor 33.98
Jabil Circuit Inc. 30.03
Schneider Electronics 20.30
GungHo Online Entertainment 33.78
Japan Display Inc. 33.78
Average 33.45
184 A. LOCKAMY III
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is approximately 34%, their probability level is signifi-
cantly higher than Apple’s American (Jabil Circuit Inc)
and German (Schneider Electronics) suppliers, with
probability levels of approximately 30% and 20%,
respectively. This result, coupled with the fact that
the Taiwanese suppliers represent the vast majority
of Apple Inc.’s COGS expenditures as illustrated in the
BSCAD, suggest that the occurrence of an external risk
event with these suppliers would result in major dis-
ruptions to its supply chain. Finally, Tables 1 and 6
suggest that Apple Inc. may have an opportunity to
mitigate its exposure to the types of external risk
examined in this study by reconfiguring its supply
chain towards those suppliers with the lowest prob-
ability of experiencing such an event. As illustrated in
Table 6, Apple’s American (Jabil Circuit Inc) and
German (Schneider Electronics) suppliers exhibit the
lowest probability of an external risk event, but cur-
rently only represent 6% of the firm’s COGS expendi-
tures as reported in the BSCAD. Thus, Apple Inc. may
be able to reduce its external risk exposure by re-
orienting its supply chain activities in the direction
of these suppliers. Furthermore, since Apple is based
in the United States, its additional reliance on an
American firm within its supply chain may provide
the added advantage of enhanced supply chain con-
trol via its familiarity with clearance processes, logis-
tical infrastructure, quality of logistics services and the
timeliness of shipments in the United States.
Managerial implications
The methodology presented in this study can be used
to monitor external risks in supply chain networks by
supply chain professionals. As part of a supply chain
governance agreement, suppliers could be required
to periodically update their external risk probability
profiles for the risk events outlined in Table 3. These
updates could be applied to Bayesian networks to
create new risk profiles for each supplier.
Adjustments to existing risk management strategies,
policies and tactics could then be made to reflect the
current risk realities associated with the supply net-
work. Thus, the methodology can provide a proactive
means of managing all categories of supply chain
risks.
The methodology can also be used by firms to
develop supplier external risk profiles to determine
expenditure risk exposure levels. Firms can then
decide if it is in their best interest to either continue
or terminate a supplier relationship based upon its
risk profile. Supplier external risk profiles can be
used to determine those external risk events which
have the highest probability of occurrence, and the
largest potential cost impact on the supply chain.
Thus, this methodology can assist firms along with
their suppliers in developing comprehensive supplier
risk management programmes designed to minimize
the effects of external and other risk events.
Finally, this methodology can be used as a tool to
assist managers in evaluating current and potential
suppliers. Suppliers who have experienced increases
in external risk events over an extended period of
time may be viewed as ‘at risk’suppliers whose rela-
tionship may require reassessment by the firm. The
reassessment could result in removal from the supply
network. Potential suppliers willing to provide infor-
mation for the generation of their risk profiles may
then become viable candidates for network inclusion.
Limitations
This study provides an examination of external risk
profiles associated with Apple Inc.’s key suppliers
based upon its expenditure exposure level to them.
Therefore, the results are specific to the study sample.
Also, the analysis contained in this study was con-
ducted at the country level, and did not attempt to
examine external risk factors which may be associated
with a particular firm. Thus, a potential limitation to
the use of the methodology presented in this study is
the ability to acquire the necessary data from suppli-
ers needed for the construction of Bayesian networks
to assess the probability of firm-level risk events. For
example, there may be circumstances where some
participants within a supply chain network are reluc-
tant to share firm-level risk profile data with their
customers. Moreover, suppliers must also be willing
to periodically update this data in order to construct
risk profiles that are valid and reliable. Another poten-
tial limitation to the use of Bayesian networks to
model supply chain risks is the proper identification
of risk events and risk categories that can impact a
supply chain. Since there are a number of approaches
available for categorizing supply chain risks, the
inability to incorporate all relevant risks into the
model could limit its effectiveness in representing a
supplier’s true risk profile. Therefore, the data used in
the construction of Bayesian networks must represent
the supplier’s current risk realities within the supply
chain network. Finally, there is currently no universally
acceptable level of supplier or supply chain risk estab-
lished in the research literature. Hence, a limitation of
this study is its inability to establish risk thresholds in
these areas.
Future research
Research studies which explore external as well as
other risk profiles for suppliers and supply networks
in other industries should be examined using
Bayesian networks to determine if industry dynamics
significantly influence supply chain risks. These stu-
dies should also incorporate firm-level data in order to
SUPPLY CHAIN FORUM: AN INTERNATIONAL JOURNAL 185
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gain a better understanding of the differences in risk
levels inherent in firms located in the same country.
Future researchers may also investigate how external
risks can be mitigated within supply chains. For exam-
ple, it may be possible to develop inventory manage-
ment policies, procedures and programmes with a
supplier or supplier group to maintain a sufficient
flow of materials through the supply chain during
and after an external risk event. Future research is
also needed to benchmark risk levels among supply
chains and suppliers within specific industries to
establish guidelines regarding acceptable levels of
risks. Finally, future researchers may choose to
develop studies which solely focus on other cate-
gories of supply chain risks, such as network and
operational, to expand the body of knowledge in
these areas.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes on contributor
Archie Lockamy III, PhD, CFPIM, is the Margaret Gage Bush
Professor of Business and Professor of Department of
Entrepreneurship, Management, & Marketing at Samford
University. Prior to his academic career, Dr Lockamy held
various engineering and managerial positions with DuPont,
Procter and Gamble and TRW. Dr Lockamy has published
research articles in numerous academic journals, and co-
authored the book Reengineering Performance
Measurement: How To Align Systems To Improve Processes,
Products and Profits. Dr Lockamy served on the 1997, 1998,
1999, 2000, 2001 and 2002 Board of Examiners for the
Malcolm Baldrige National Quality Award via appointment
by the United States Department of Commerce. He also
served as Vice President of the Board of Directors of the
American Production and Inventory Control Society (APICS)
Educational and Research Foundation. Dr Lockamy is recog-
nized as a Certified Fellow in Production and Inventory
Management (CFPIM) by APICS, and is certified as an
Academic Jonah by the Avraham Y. Goldratt Institute.
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