-
The International Journal of Logistics ManagementOn uncertainty
in supply chain risk managementJyri Vilko Paavo Ritala Jan
Edelmann
Article information:To cite this document:Jyri Vilko Paavo
Ritala Jan Edelmann , (2014),"On uncertainty in supply chain risk
management", TheInternational Journal of Logistics Management, Vol.
25 Iss 1 pp. 3 - 19Permanent link to this
document:http://dx.doi.org/10.1108/IJLM-10-2012-0126
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III, (2014),"Assessing disaster risks in supply chains", Industrial
Management & DataSystems, Vol. 114 Iss 5 pp. 755-777
http://dx.doi.org/10.1108/IMDS-11-2013-0477Shashank Rao, Thomas J.
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http://dx.doi.org/10.1108/IJLM-10-2012-0126
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On uncertainty in supplychain risk management
Jyri Vilko, Paavo Ritala and Jan EdelmannSchool of Business,
Lappeenranta University of Technology,
Lappeenranta, Finland
Abstract
Purpose The concept of uncertainty is a relevant yet little
understood area within supply chain riskmanagement. Risk is often
associated with uncertainty, but in reality uncertainty is a much
moreelaborate concept and deserves more in-depth scrutiny. To
bridge this gap, the purpose of this paper isto propose a
conceptual framework for assessing the levels and nature of
uncertainty in this context.Design/methodology/approach The aim of
the study is to link established theories of uncertaintyto the
management of risk in supply chains, to gain a holistic
understanding of its levels and nature.The proposed conceptual
model concerns the role of certainty and uncertainty in this
context.Illustrative examples show the applicability of the
model.Findings The study describes in detail a way of analysing the
levels and nature of uncertainty insupply chains. Such analysis
could provide crucial information enabling more efficient and
effectiveimplementation of supply chain risk management.Practical
implications The study enhances understanding of the nature of the
uncertaintiesfaced in supply chains. Thus it should be possible to
improve existing measures and analyses of risk,which could increase
the efficiency and effectiveness of supply chain and logistics
management.Originality/value The proposed conceptual framework of
uncertainty types in the supply chaincontext is novel, and
therefore could enhance understanding of uncertainty and risk in
supply andlogistics management and make it easier to categorise, as
well as initiate further research in the field.
Keywords Uncertainty, Risk, Supply chain risk management,
Conceptual framework
Paper type Conceptual paper
1. IntroductionComplexity, specialisation, and disintegration
are emerging as major challenges interms of risk management in
supply chains, having made them vulnerable to disturbancesfrom both
inside and outside the system. Indeed, many recent events have
shown howvulnerable long and complex supply chains are. Such events
include the well-knownmelamine crisis in Chinese dairy products,
and other food crises; major natural disastersincluding floods,
tsunamis, and earthquakes; industrial and societal disputes across
theglobe, as well as firm and supply chain-specific glitches and
disturbances (Hendricks andSinghal, 2003; Chopra and Sodhi, 2004;
Sheffi, 2005; Narasimhan and Talluri, 2009).This vulnerability has
attracted the attention of many academics in the field of
logisticsand supply management, in which risk-related issues are
increasingly taken into account(Minahan, 2005; Kleindorfer and
Saad, 2005; Sanchez-Rodrigues et al., 2008, 2010; Wagnerand Neshat,
2010; Ghagde et al., 2012). In this context, the quality and
competitivenessof individual companies operations depend on their
ability to identify and mitigate theuncertainties and risks they
encounter. However, although awareness of vulnerability andof risk
management is increasing among academics and practitioners, many
relatedconcepts are still in their infancy. There are thus
insufficient conceptual frameworks and
The current issue and full text archive of this journal is
available atwww.emeraldinsight.com/0957-4093.htm
Received 31 October 2012Revised 2 April 2013
2 September 2013Accepted 27 September 2013
The International Journal of LogisticsManagement
Vol. 25 No. 1, 2014pp. 3-19
r Emerald Group Publishing Limited0957-4093
DOI 10.1108/IJLM-10-2012-0126
This paper is based on a paper presented at the 17th
International Symposium on Logistics(www.isl21.net) held in July
2012 in Cape Town, South Africa.
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empirical findings to provide a clear picture of the phenomenon
of supply chainrisk management ( Juttner, 2005; Manuj and Mentzer,
2008). Accordingly, bothacademic research and practitioner reports
stress its importance as well as the need todevelop different
approaches (Blos et al., 2009; Manuj and Mentzer, 2008; Shaer
andGoedhart, 2009).
In line with the above-mentioned developments, the role of
supply chain riskmanagement has recently been increasingly
emphasised. Indeed, Ju (2005) found that44 per cent of
organisations expected their vulnerabilities to increase within
thenext five years. More recently, the need for risk management has
become evidentfollowing Snells (2010) study showing that 90 per
cent of the respondent companiesfeared supply risks, whereas only
60 per cent felt confident or knowledgeable enoughto cope with
them. In addition, Hendricks and Singhal (2005) found out that
firms thatexperience supply chain disruptions experience on average
40 per cent stock returns.Thus, it is not surprising that there is
a growing interest in supply chain-relateddecisions and the
uncertainty and risk involved (Prater, 2005; Swink and
Zsidisin,2006; Craighead et al., 2007; Hendricks et al., 2009; Hult
et al., 2010). In fact, it hastraditionally been assumed that
supply side risk is similar to or equivalent to demandside risk
(Christopher and Peck, 2004). However, it has been suggested that
supply-siderisks and uncertainty are much more complex issues than
demand-side ones, and that,therefore, managing them needs more
careful attention (see Snyder and Shen, 2006).
Supply chain risk management concerns risk as a situation
entailing exposureto two essential components: an event and the
uncertainty concerning the possibleoutcomes (Holton, 2004; Sheffi,
2005). Thus, risk assessment is based on the likelihoodof the
occurrence of the risk situation, and on the kind of damage it
might entail ifrealised (Mitchell, 1995). The different types of
risk are extensively covered in therelevant literature (Rao and
Goldsby, 2009), but analysis of the concept of risk itself isvery
limited. For instance, the management of risk is commonly discussed
in terms ofvulnerability and uncertainty (Sorensen, 2005), meaning
that the concept is understoodas an occurrence for which the
probability distribution is known. However, we suggestthat in
reality, it is much more common that the probability distributions
cannotbe defined. Thus, in order to enhance understanding of how
risks could be bettermanaged under such conditions, we put forward
a conceptual framework focused onthe role of uncertainty in supply
chain risk management.
Supporting our argument and illustrating the research gap, some
authors havecriticised the fact that the literature on supply chain
risk management does not alwaysclearly distinguish between risk and
uncertainty, which makes the definitions quitevague (Tang and
Nurmaya Musa, 2010). Further, it has been suggested that
merelyrecognising uncertainty is not enough, but researchers and
practitioners need morerigorous frameworks breaking uncertainty
down into more detailed elements (Prater,2005). Indeed, earlier
studies have reported that the concepts of risk and uncertaintyare
less understood and developed in this context than in other
disciplines (Khan andBurnes, 2007). Given that the two concepts are
among the most essential in supplychain risk management, clarity is
of the essence. The aim in this paper, therefore,is to enhance
understanding of these concepts by illustrating the levels of
uncertaintyin supply chain risk management. In so doing, we hope to
define the nature ofuncertainty so as to facilitate a more
comprehensive and valuable consideration of howthe concept should
be approached in future studies in the field.
In the next section, we introduce the theoretical background of
supply chain riskmanagement and uncertainty. We then develop our
conceptual model of different types
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of certainty/uncertainty and their implications for the analysis
and management ofsupply chain risk. In building the framework, we
utilise an integrative literature review,aiming to synthesise and
integrate existing literature in the quest of generatingnew
frameworks and perspectives (Torraco, 2005). We mainly build on the
existingsupply chain risk management literature and combine it with
the insights from economictheory on certainty vs uncertainty
(Simon, 1957; Langlois, 1984; Dosi and Egidi, 1991).Illustrative
examples of each type of uncertainty follow, which cover events
relatedto the focal actor, the supply chain, and the external
environment. The study ends withdiscussion of the theoretical and
practical implications and further research directions.
2. Theoretical background2.1 Supply chain risk managementThe
supply chain comprises a series of activities and organisations
through whichmaterial and information move on their way to the
final customer. Peck (2005) describessupply chain vulnerability in
this context as exposure to serious disturbance arising fromrisks
within and external to the chain. According to Waters (2007),
vulnerability reflectsthe susceptibility of a supply chain to
disruption, and is a consequence of risks in it.Ju (2005) further
refers to supply chain vulnerability as the propensity of risk
sources anddrivers to outweigh risk-mitigating strategies, thus
causing adverse consequences in thechain and jeopardising its
ability to effectively serve the end customer market. Supplychain
risk management, in turn, is a function that aims to identify the
potential sources ofrisk, and to implement appropriate actions to
avoid or contain supply chain vulnerability(Narasimhan and Talluri,
2009; see also Ghagde et al., 2012).
Supply chain risk is commonly portrayed as a threat that
something mighthappen to disrupt normal activities and that would
stop things happening as planned(Waters, 2007). Most of the
literature defines risk as purely negative and leading toundesired
results or consequences (Harland et al., 2003; Manuj and Mentzer,
2008).A standard formula for the quantitative definition of supply
chain risk is (Mitchell, 1995):
Risk PLoss ILoss
where risk is defined as the probability ( P ) of loss and its
significance (I).Hetland (2003) and Diekmann et al. (1988) view
risk as an implicitly uncertain
phenomenon. It should be noted that there are differences
between the two conceptsof risk and uncertainty, however. Waters
(2007) explains the difference, suggesting thata risk occurs
because there is a certain type of uncertainty about the future. In
thistraditional risk management context, uncertainty means that
unexpected events mayoccur, but they can be quantified and
therefore managed.
However, as a concept, uncertainty reaches far beyond the
traditional conceptionof risk, and thus it deserves more elaborate
scrutiny. Van der Vorst and Beulens (2002,p. 413) define supply
chain uncertainty as decision making situations in the supplychain
in which the decision maker does not know definitely what to decide
as he isindistinct about the objectives; lacks information about
its environment or the supplychain; lacks information processing
capacity; is unable to accurately predict the impactof possible
control actions on supply chain behaviour; or, lacks effective
controlactions. The sources of uncertainty include quantity,
quality, and time (Van der Vorstand Beulens, 2002).
Davis (1993) was among the first scholars to explicitly consider
uncertainty as astrategic issue for supply chains. However, in his
investigations, the sources were
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limited to suppliers, manufacturing, and customers, and the
effects to theperformance of the supply chain. Later, building on
the work of Davis (1993),Mason-Jones and Towill, 1998 developed an
uncertainty circle model, adding controlsystems as one more source
and offering a wider perspective on the supply anddemand sides of
the chain; this was then further complemented by Geary et al.
(2002)and Sanchez-Rodrigues et al. (2008).
It appears from these studies that uncertainty has been taken
into account in supplychain risk management in various ways and in
different contexts, but the literature stilllacks frameworks and
consensus in terms of the role of uncertainty.
2.2 Uncertainty and riskKnights (1921) distinction between
certainty, risk, and uncertainty could beregarded as the best-known
and most frequently used typology of uncertainty inrisk management.
In his definition of risk, he coined the terms measurable
uncertainty(quantitative) and unmeasurable uncertainty
(non-quantitative), when only partialknowledge of outcomes such as
beliefs and opinions is available. The measurable,quantifiable
perspective portrays a type of basic uncertainty that can be
managedusing objective measures, whereas Knightian uncertainty
refers to immeasurablerisks that cannot be calculated. We adopt
this intuition as our starting point indeveloping a framework for
uncertainty in supply chain risk management.
In this context, Trkman and McCormack (2009) classify
uncertainty in two categories,endogenous and exogenous, depending
on whether they derive from within or outsidethe supply chain.
Juttner et al. (2003) also suggest including external uncertainty
inother sources of uncertainty in the chain. The
exogenous-endogenous distinction initself, however, is too vague to
make sense of how uncertainty really affects decisionsrelated to
supply chain risk management: it describes the source of
uncertainty, whichis relevant as such, but it does not describe the
type of uncertainty. Thus, we proposethat uncertainty, especially
in the context of supply chain risk management, couldbe examined
through the lenses of substantive and procedural uncertainty,
asexplained below.
In line with Simons (1957) concept of rationality, Dosi and
Egidi (1991, pp. 145-146)introduce the notions of substantive and
procedural uncertainty. Substantiveuncertainty derives from the
incompleteness of the information set and is relatedto a lack of
information about environmental events and all the informationwhich
would be necessary to make decisions with certain outcomes. A
similar notionhas been discussed in the project management context
by De Meyer et al. (2002), whodiscuss unforeseen uncertainty, which
refers to the lack of ability to predict factors thatinfluence
project-related risks.
Procedural uncertainty arises from the inability of the agents
to recognise andinterpret the relevant information, even when
available. It concerns the competencegap in problem-solving and
limitations on the computational and cognitivecapabilities of the
agents to pursue unambiguously their objectives, given the
availableinformation. Uncertainty in a supply chain could be
classified as illustratedin Figure 1.
The main components used to distinguish between types of
uncertainty include thefollowing:
(1) the knowledge level of the decision maker related to the
problem under eachtype of uncertainty;
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(2) the decision-makers knowledge of the possible actions in
which to engage;
(3) the decision-makers knowledge of possible states of the
world;
(4) the decision-makers knowledge of the consequences resulting
from the interactionbetween actions and states of the world;
and
(5) the decision-makers subjective or objective knowledge of the
probabilities ofthe occurrence of possible states of the world.
This classification distinguishes three types of uncertainty:
parametric and structural(i.e. environment dependent) and
procedural (i.e. decision-maker dependent). We believethis
distinction gives a valuable perspective on uncertainty related to
decision making(Langlois, 1984; Dosi and Egidi, 1991; Kylaheiko,
1995; Kylaheiko et al., 2002).Within each of these categories, the
decision maker has a different amount of knowledgeabout the state
of the world and its events, and therefore also has different kinds
ofresources with which to cope with uncertainty. At the extreme,
uncertainty could alsobe conceptualised as radical, when all pieces
of knowledge are imperfect and there is noknowledge about the
structure or probability of future events (Loasby, 1976;
Kylaheiko,1995).
3. Conceptualising (un)certainty in the supply chain
contextSupply chain risk management is assumed to handle risks
either by proactivelymitigating them or by reactively responding to
them (Chopra and Sodhi, 2004; Tomlin,2006; Ghagde et al., 2012).
The current literature on supply chain risk commonly viewsall
threats disrupting normal activities (i.e. risks) as products of
the impact and theprobability of an event (March and Shapira, 1987;
Manuj and Mentzer, 2008; Vilko andHallikas, 2012), which in reality
may be impossible to measure. Thus, we suggest thatthe nature of
uncertainties plays a crucial role here, since it affects the
visibility and thepossibilities of decision makers within a certain
domain (see De Meyer et al., 2002;
CERTAINTY
RISK
UNCERTAINTY
SUBSTANTIVE PROCEDURAL
PARAMETRIC COMPLEXITYSTRUCTURAL
Related to the outcomesEnvironment-dependent
Related to the decision processDecision-maker dependent
Knightian basic uncertainty
Figure 1.Certainty, risk,
and uncertainty
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Prater, 2005). Thus, if supply chain management is to accomplish
its tasks in boththeory and practice, it has to understand the
concept of uncertainty in its entirety.
Given that the environment cannot normally be fully controlled,
there are alwaysunknown elements facing decision makers. In
general, decision makers in firms need tocope with decision-making
variables that involve variation within a predictable rangeas well
as events that are close to chaos or crisis (Weick, 1993; Pearson
and Clair, 1998;De Meyer et al., 2002). Taking into account all the
influential environmental factors istherefore impossible, and the
information on the basis of which probabilities areformed is more
or less imperfect. In light of the above discussion on various
types ofuncertainty, Table I explains in more detail the range
between (complete) certainty andradical uncertainty and the
implications for supply chain risk management.
Table I describes the amount of knowledge that decision makers
hold about thestate of the world (here: the supply chain) and its
events. The amount of knowledgethus describes the decision makers
ability to cope with uncertainty.
Complete certainty, as illustrated in the first column, is an
example of a hypotheticalworld in which all relevant information is
known to the decision maker. In terms ofsupply chain risks, it
suggests a situation in which all related risks (inside and
outsideof the chain) and their consequences are known. Thus, in
such a situation, there are noactual risks, because all of them can
be mitigated. In reality, this situation is, of
course,impossible.
The second column describes a situation in which the structure
of future events isknown in terms of objective probabilities
concerning the likelihood and impact of theiroccurrence. This
probabilistic certainty is typically regarded as an implicit basis
forthe analysis of supply chain risk and the subsequent management
actions. However,typically the level of knowledge required for
objectively assessing the likelihood andimpact of risk events is
rarely known, which is why various types of uncertainty needto be
addressed as well.
The third column illustrates a situation in which the structure
of the future(or future event) is completely known but the
probability parameters are not.The uncertainty here is environment
dependent. This situation is referred to asparametric uncertainty,
and it includes only subjective beliefs about the probabilities
offuture events and their outcomes (Langlois, 1984; Dosi and Egidi,
1991). In termsof supply chain risk management, potential risk
events are identified, but in terms oflikelihood and impact, they
are difficult to assess objectively the risk analysis is basedon
subjective assessment. Parametric uncertainty allows quantification
of differentaspects of the events; however, the following
categories of uncertainty do not, and theycan only be described
(assessed) qualitatively.
The fourth column indicates environment-dependent structural
uncertainty.Knowledge related to the state of the world in the
future is imperfect, and onlysubjective beliefs can be projected
(Langlois, 1984). In terms of supply chain riskmanagement, this
means that no holistic picture of the supply chain and the
relatedrisk events can be objectively formed. The probabilities of
the identified events are alsodifficult to quantify, and the
interdependence related to the operations of the supplychain is
unclear. Therefore, the analysis cannot objectively assess the risk
eventsor their causality.
The fifth column in the table illustrates procedural
uncertainty, meaning that thedecision maker is constrained by his
or her computational and cognitive capabilities(Dosi and Egidi,
1991) and therefore cannot form a clear picture of the processes or
therisk events, mainly on account of their complexity. Inadequate
cognitive abilities and
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Cer
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Table I.On defining uncertainty
in supply chains
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rtai
nty
(ris
k)
Par
amet
ric
un
cert
ain
tyS
tru
ctu
ral
un
cert
ain
tyP
roce
du
ral
un
cert
ain
tyR
adic
alu
nce
rtai
nty
Imp
lica
tion
sfo
rd
ecis
ion
mak
er(e
.g.
sup
ply
chai
n/
log
isti
csm
anag
er)
Dec
isio
n-m
aker
acts
up
onco
mp
lete
kn
owle
dg
ean
dim
ple
men
tsp
erfe
ctly
effe
ctiv
eac
tiv
itie
s
Dec
isio
n-m
aker
acts
up
onk
now
nev
ent
stru
ctu
res
and
attr
ibu
tes
bas
edon
accu
rate
calc
ula
tion
san
dp
red
icti
ons
Dec
isio
n-m
aker
acts
up
onk
now
nev
ent
stru
ctu
rean
dsu
bje
ctiv
ely
eval
uat
esh
owp
rob
able
and
imp
actf
ul
dif
fere
nt
even
tsar
e
Dec
isio
n-m
aker
acts
up
onsu
bje
ctiv
eas
sess
men
tof
bot
hev
ent
stru
ctu
rean
dp
rob
abil
ity
and
thei
rim
pac
t
Dec
isio
n-m
aker
acts
full
yu
pon
sub
ject
ive
intu
itio
nab
out
futu
reev
ents
and
attr
ibu
tes
Dec
isio
nm
aker
can
not
act
orp
erfo
rmra
tion
ally
Table I.
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imperfect knowledge related to future events severely limit the
decision maker inpursuing set objectives. In the context of supply
chain risk management, this reflects asituation in which the supply
chain has severely limited visibility in terms of itsactivities and
the related risks. Only a fraction of the events and risk can be
identifiedand analysed and then merely subjectively. Further,
structural and parametricuncertainties may be associated with
procedural uncertainty; the sources of uncertaintythat allow some
actors to generate unforeseeable changes are endogenous sources
ofuncertainty for others (Dosi and Egidi, 1991).
The last column, radical uncertainty, refers to a hypothetical
world in which there istotal imperfection in terms of knowledge
(see Prater, 2005, on chaotic uncertainty forparallel discussion).
Thus, supply chain risk management and analysis are impossiblein
that all knowledge related to each decision-making element is
incomplete, whichdoes not even allow the formation of subjective
beliefs about future events. This type ofuncertainty is unlikely in
any situation, given that subjective beliefs may be based on avery
limited understanding of events and their context.
4. The applicability of the frameworkBy way of a practical
illustration of the application of the developed framework insupply
chain risk management, we follow typical examples of risk through
thedifferent uncertainty categories (Table II). We distinguish
between events related tothe focal actor, the supply chain, and the
external environment (which is a widelyadopted distinction in the
literature, e.g. Trkman and McCormack, 2009).
With regard to events related to the focal actor, we illustrate
the risk of industrialunrest (employee strike affecting the focal
actors activities). This risk is quite easilymitigated on the
left-hand side of the framework, with complete certainty and
probabilisticcertainty, because management can prepare for known
situations in which employeestrikes threaten supply chain and
logistics operations. In contrast, moving towardsmore radical forms
of uncertainty, the related factors and causalities start to become
moreunclear, and their assessment more subjective. Managing risks
is likely to be a muchthornier task in these situations.
Turning to events related to the supply chain, we give the
example of the crash(operational failure/malfunction) of supply
chain information systems, which is anincreasing concern in supply
chain and logistics operations. When there is aprobabilistic
certainty (a typical definition of risk assessment), it is possible
to preparefor the crashes and failures, and the risks can be
sufficiently mitigated. However, on theright-hand side of the
framework, events leading to information system crashes maynot be
understood or recognised, which makes preparing for them very
difficult,if not impossible.
In terms of the external environment, our example relates to a
sudden and impactfuleconomic crisis. In conditions of certainty,
there are no risks, because there is perfectinformation regarding
the economy and related factors. Although this is definitely
notrealistic, probabilistic certainty (i.e. the traditional
risk-assessment category) is not avery likely scenario either, in
the long run. In fact, economic situations may very likelyinvolve
parametric uncertainty issues such as unanticipated fluctuations in
supply anddemand, which may have an impact that can only be
subjectively assessed. Thesesituations may also involve even more
uncertainty that is not fully understood and ishard to define (e.g.
in emerging economies).
These examples show the importance of analysing the nature of
uncertainty insupply chain and logistics operations. In order to
effectively manage the risks involved,
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Cer
tain
tyP
rob
abil
isti
cce
rtai
nty
(ris
k)
Par
amet
ric
un
cert
ain
tyS
tru
ctu
ral
un
cert
ain
tyP
roce
du
ral
un
cert
ain
tyR
adic
alu
nce
rtai
nty
Ev
ents
rela
ted
toth
efo
cal
acto
rIn
du
stri
alu
nre
st(e
mp
loye
est
rik
e)
Com
ple
tely
accu
rate
kn
owle
dg
eab
out
wor
ker
san
du
nio
ns
mot
ivat
ion
s,p
lan
san
dac
tiv
itie
s
Th
ere
lati
ve
pro
bab
ilit
yof
ind
ust
rial
un
rest
isk
now
n,
and
rela
ted
fact
ors
can
be
calc
ula
ted
accu
rate
ly
Th
efa
ctor
sle
adin
gto
ind
ust
rial
un
rest
are
kn
own
,b
ut
thei
rli
kel
ihoo
dan
dim
pac
tar
eu
ncl
ear
and
are
bas
edon
sub
ject
ive
intu
itio
n
Th
efa
ctor
sre
late
dto
ind
ust
rial
un
rest
are
kn
own
,b
ut
thei
ref
fect
sar
eu
ncl
ear
and
are
bas
edon
sub
ject
ive
intu
itio
nat
bes
t
Th
efa
ctor
san
dca
usa
liti
esre
late
dto
ind
ust
rial
un
rest
are
not
full
yu
nd
erst
ood
and
are
har
dto
def
ine
Th
ein
du
stri
alu
nre
stis
not
un
der
stoo
dor
reco
gn
ised
asa
ph
enom
enon
Ev
ents
rela
ted
toth
esu
pp
lych
ain
/n
etw
ork
Su
pp
lych
ain
info
rmat
ion
syst
ems
cras
h(o
per
atio
nfa
ilu
re/
mal
fun
ctio
n)
Com
ple
tely
accu
rate
kn
owle
dg
eab
out
curr
ent
syst
ems
stat
us
and
futu
rech
ang
esin
it
Th
ek
now
nre
lati
ve
pro
bab
ilit
yof
syst
emm
alfu
nct
ion
san
dop
erab
ilit
y
Th
efa
ctor
saf
fect
ing
syst
emop
erab
ilit
yan
dm
alfu
nct
ion
sar
ek
now
n,
bu
tth
eir
lik
elih
ood
and
imp
act
are
un
clea
ran
dar
eb
ased
onsu
bje
ctiv
ein
tuit
ion
Th
efa
ctor
sre
late
dto
chan
ges
insy
stem
oper
abil
ity
and
mal
fun
ctio
ns
can
only
be
asse
ssed
sub
ject
ivel
y
Th
esy
stem
oper
abil
ity
and
mal
fun
ctio
ns
are
reco
gn
ised
,b
ut
nei
ther
the
fact
ors
lead
ing
toth
emn
orth
eir
cau
sali
ties
are
full
yu
nd
erst
ood
,an
dth
eyar
eh
ard
tod
efin
e
Ch
ang
esin
syst
emst
atu
sar
en
otn
otic
edor
reco
gn
ised
Ev
ents
rela
ted
toth
eex
tern
alen
vir
onm
ent
(eco
nom
iccr
isis
)
Com
ple
tely
accu
rate
kn
owle
dg
eab
out
fact
ors
affe
ctin
gec
onom
icen
vir
onm
ent
ofth
esu
pp
lych
ain
/n
etw
ork
and
its
pot
enti
alfl
uct
uat
ion
and
vu
lner
abil
ity
Th
ek
now
nre
lati
ve
pro
bab
ilit
yof
fact
ors
affe
ctin
gth
eb
alan
ceof
the
econ
omic
env
iron
men
t,b
ased
onh
isto
rica
ld
ata
orh
isto
rica
lly
accu
mu
late
dex
per
ien
ce
Th
efa
ctor
saf
fect
ing
econ
omic
env
iron
men
tan
dit
sef
fect
onsu
pp
lych
ain
/net
wor
kar
ek
now
n,
bu
tth
eir
lik
elih
ood
and
imp
act
are
un
clea
ran
dar
eb
ased
onsu
bje
ctiv
ein
tuit
ion
Th
ere
isn
ok
now
led
ge
abou
tth
est
ruct
ure
ofth
eec
onom
icsy
stem
nor
the
cau
sali
ties
lead
ing
top
oten
tial
cris
issi
tuat
ion
sfr
omsu
pp
lych
ain
poi
nt
ofv
iew
Th
em
ech
anis
ms
and
fact
ors
ofth
eec
onom
yan
dec
onom
iccr
isis
are
not
full
yu
nd
erst
ood
and
are
har
dto
def
ine,
and
thei
rli
nk
age
toth
esu
pp
lych
ain
isn
otfu
lly
un
der
stoo
d
Th
eec
onom
icen
vir
onm
ent
isd
eem
edas
com
ple
tely
exte
rnal
and
can
not
be
asse
ssed
oran
aly
sed
atal
l
(con
tinu
ed)
Table II.Illustrative examples ofusing the
developedframework
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tain
tyP
rob
abil
isti
cce
rtai
nty
(ris
k)
Par
amet
ric
un
cert
ain
tyS
tru
ctu
ral
un
cert
ain
tyP
roce
du
ral
un
cert
ain
tyR
adic
alu
nce
rtai
nty
Key
man
ager
ial
acti
vit
ies
No
acti
vit
ies
nee
ded
rela
ted
tosu
pp
lych
ain
risk
man
agem
ent
d
esig
nof
sup
ply
man
agem
ent
isb
ased
onfu
llce
rtai
nty
Cal
cula
te(o
bje
ctiv
ely
)p
rob
abil
itie
sfo
rea
chre
cog
nis
edri
skev
ent
and
imp
lem
ent
effe
ctiv
eri
skm
anag
emen
tac
tiv
itie
sb
ased
onth
ose
Ev
alu
ate
pro
bab
ilit
ies
bas
edon
avai
lab
lein
form
atio
nan
db
est
un
der
stan
din
gfo
rea
chre
cog
nis
edri
skev
ent
and
imp
lem
ent
effe
ctiv
eri
skm
anag
emen
tac
tiv
itie
sb
ased
onth
ose
Ev
alu
ate
pro
bab
ilit
ies
and
risk
situ
atio
nb
ased
onav
aila
ble
info
rmat
ion
and
bes
tu
nd
erst
and
ing
for
each
reco
gn
ised
risk
even
tan
dco
nte
xt
and
imp
lem
ent
effe
ctiv
eri
skm
anag
emen
tac
tiv
itie
sb
ased
onth
ose
Sim
pli
fy,
cate
gor
ise,
and
eval
uat
eri
skev
ents
and
situ
atio
nb
ased
onav
aila
ble
info
rmat
ion
and
exp
erie
nce
,an
dim
ple
men
tef
fect
ive
risk
man
agem
ent
acti
vit
ies
bas
edon
thos
e
No
acti
vit
ies
bas
edon
risk
man
agem
ent
kn
owle
dg
eca
nb
eex
ecu
ted
du
eto
com
ple
teu
nce
rtai
nty
Table II.
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management needs to pay close attention to the certainty and
uncertainty levels inthe parameters, structures, and processes
related to supply chains (as described in thebottom row of Table
II). If the certainty level is low (e.g. procedural uncertainty)
withregard to a major event, there is a need for activities that
enhance it, or the organisationneeds otherwise to accept the
consequences of being exposed to a level of uncertainty.When the
uncertainty increases, the risk management activities should be
directedmore and more towards making sense of the situation and of
risk events themselves,as the level of subjectivity and intuition
rises. Therefore, it is relevant to understandboth the level of
objective information and the understanding used in
subjectivedecision making in the supply chain risk management
context.
5. Discussion and implicationsSupply chains have become very
long and complex, with many parallel physical andinformation flows
to ensure that products are delivered in the right quantities, to
theright place, in a cost-effective manner (Juttner, 2005). At the
same time, they are becomingmore and more vulnerable to serious
disturbances. In line with these developments,supply chain risk
management and the uncertainty related to it are
assumingincreasingly important roles. However, the concept of
uncertainty is fuzzier in the area ofsupply chain risk management
than in many other disciplines, a research gap that hasbeen
identified in some existing studies (Tang and Nurmaya Musa, 2010;
Sorensen,2005). In fact, in a recent systematic literature review
(Ghagde et al., 2012), it wassuggested that further research is
needed to better understand the visibility andtraceability of
risks, which is still an underdeveloped area in the field of supply
chain riskmanagement. Therefore, in order to enhance understanding
on these issues, we proposea conceptual framework and offer
implications illustrating the various levels ofuncertainty in the
supply chain context. We categorise the level of uncertainty in
moredetail than in previous studies, which enables the construction
of a more effective andrealistic risk management strategy with a
better understanding of the level of knowninformation and the
nature of uncertainty related to it.
5.1 Implications for the literature on supply risk
managementCurrently, supply chain risk management tends to assess
risks as if they were based onobjective measures (e.g. Bogataj and
Bogataj, 2007; Yang, 2011), which in this paper werefer to as
probabilistic certainty. However, in reality this objective basis
is rarelythe case. Therefore, the measures derived from such
assessments may well be based onsubjective beliefs, and thus should
be treated as such. This carries major implicationsin terms of risk
management and risk analysis in supply and logistics chainsand
networks.
The current contributions on uncertainty in supply chain risk
management arestill quite scarce, and only a few scholars have
studied the phenomenon. Typically, thepresented frameworks (e.g.
Mason-Jones and Towill, 1998; Wilding, 1998; Prater et al.,2001;
Sanchez-Rodrigues et al., 2008; Van der Vorst and Beulens, 2002)
concentrate onstudying the identification of and controlling the
uncertainty from different parts of thesupply chain process, as
well as studying the effectiveness implications. However,these
studies do not address in detail the nature of uncertainty itself.
Thus, a gap canbe identified in the current body of knowledge, and
this is the gap at which the coreargument of this study aims. There
is, however, some work that comes close to ouraims here. For
example, in the project management context, De Meyer et al.
(2002)underline the need for a better understanding of uncertainty,
and analyse the degree of
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what is known and what can be planned and done based on that.
Similarly, we analysethe uncertainty in the supply chain risk
management domain from the perspectiveof the decision maker, and we
concentrate on the level of knowledge that can be usedfor the basis
of identification, analysis, and management of the uncertain
events.
In building our argument, we have combined various theories of
uncertainty(Knight, 1921; Dosi and Egidi, 1991) in the context of
supply chain risk management.The levels of uncertainty presented in
the developed framework range between completecertainty and
complete (radical) uncertainty. In particular, risk is inherent in
uncertainty(i.e. probabilistic certainty), whereas, in the
literature, uncertainty is commonly seenas inherent in risk. The
view presented here thus differs from that expressed in
themainstream literature in that regard, and it facilitates deeper
exploration of the conceptof uncertainty and its implications.
Our uncertainty framework for supply chain risk management
contributes tothe existing literature in two distinct ways. First,
it illustrates the different levels ofuncertainty and therefore
enhances understanding of its nature when risks areassessed and
managed. This could help researchers in the field to better assess
theavailable information on risks, and on this basis to make
stronger recommendationswith regard to how such risks should be
managed. There is existing literatureaddressing the different
levels of uncertainty in the project management context(e.g. De
Meyer et al., 2002; Ward and Chapman, 2003). In addition, there is
researchfocusing on uncertainty in terms of its sources such as
demand and forecastinguncertainty (Prater et al., 2001), as well as
on the generic influence factors on the level ofuncertainty
(Prater, 2005). Complementing these approaches, our work takes one
stepfurther in explicating the nature of certainty and uncertainty
in the supply managementcontext through the lenses of parametric,
structural, and procedural dimensions.This allows for even more
in-depth inquiry into the nature of the phenomenon, helping tograsp
its dimensions as well as their interrelations. Thus, as the
existing frameworkstreat uncertainty mostly as a one-dimensional
phenomenon and identify its sources andimplications (Mason-Jones
and Towill, 1998; Prater et al., 2001; Sanchez-Rodrigues et
al.,2008), our study can be used to complement these frameworks by
illustrating the natureof different uncertainty types and levels.
Second, the framework carries implications fordecision making under
various levels of uncertainty, which should help decision makersto
design better information and other support systems in and beyond
the contextin question (see e.g. Mansouri et al., 2012). In
particular, in focusing on the type ofknowledge the decision maker
has or lacks, it highlights the potential strong points aswell as
the shortcomings in the decision-making process.
5.2 Practical implicationsIn practical terms, analyses of the
nature of uncertainty could provide crucial informationfor supply
chain risk management and therefore could enable more efficient and
effectiveimplementation. The framework opens up new insights and
could be useful in the riskmitigation process to organisations
attempting to assess their vulnerability to differentexogenous and
endogenous events.
In particular, the better that management understands the nature
of uncertainty,the easier it is to allocate resources related to
supply chain risk more effectively.When uncertainties concerning an
important event are more procedural in nature,more resources should
be allocated to providing more clarification. Despite theinevitable
restrictions on the possibility of reducing uncertainty, resource
allocationand information gathering will ease at least some of it.
An organisation making such
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decisions should examine the cost/benefit implications of
accepting certain levels ofuncertainty or investing in lowering the
level when necessary. In fact, a multidimensionalmanagement
approach for supply risk management has been suggested in order
toeffectively and efficiently handle various types of risks in the
firms supply chain (Chopraand Sodhi, 2004; Kleindorfer and Saad,
2005). Utilising the uncertainty types introducedin this study may
help decision makers in further assessing the best possible
riskmitigation activities and processes.
Finally, the existing practice-oriented models for supply chain
risk managementframeworks, whether they deal with uncertainty,
risk, or vulnerability (see e.g. Waters,2007; Kleindorfer and Saad,
2005; Sheffi, 2005; Ritchie and Brindley, 2007; Manuj andMentzer,
2008) aim to determine the optimal cost/benefit approach in the
managingof the unwanted events. Our framework has the possibility
of extending these byillustrating to managers what is in fact known
(or not known) about the particularevents, thus allowing more
purposeful and precise use of the frameworks according tothe
information available.
5.3 Further research directionsThe illustrated framework linking
theories of uncertainty to the supply chain contextis novel, and
therefore one goal of this study was to act as a catalyst
triggering furtherresearch. Future studies could both improve and
test the argument provided here.Given that the framework is
conceptual, it needs to be empirically tested and validatedby the
academic community in further research endeavours. Such studies
could use itto analyse a certain type of event with a large
dataset, for instance, in which the level ofuncertainty is assessed
against the performance effectiveness of supply chain
riskmanagement. It would also be useful to conduct qualitative
studies exploring varioustypes of processes related to decision
making under different levels of uncertainty.
References
Blos, M.F., Quaddus, M., Wee, H.M. and Watanabe, K. (2009),
Supply chain risk management(SCRM): a case study on the automotive
and electronic industries in Brazil, Supply ChainManagement: An
International Journal, Vol. 14 No. 4, pp. 247-252.
Bogataj, D. and Bogataj, M. (2007), Measuring the supply chain
risk and vulnerability in frequencyspace, International Journal of
Production Economics, Vol. 108 Nos 1-2, pp. 291-301.
Chopra, S. and Sodhi, M.S. (2004), Managing risk to avoid
supply-chain breakdown, MIT SloanManagement Review, Vol. 46 No. 1,
pp. 53-61.
Christopher, M. and Peck, H. (2004), Building the resilient
supply chain, International Journal ofLogistics Management, Vol. 15
No. 2, pp. 1-13.
Craighead, C.W., Blackhurst, J., Rungtusanatham, M.J. and
Handfield, R.B. (2007), The severityof supply chain disruptions:
design characteristics and mitigation capabilities,
DecisionSciences, Vol. 38 No. 1, pp. 131-156.
Davis, T. (1993), Effective supply chain management, Sloan
Management Review, Vol. 34 No. 4,pp. 35-46.
De Meyer, A., Loch, C.H. and Pich, M.T. (2002), Managing project
uncertainty: from variation tochaos, MIT Sloan Management Review,
Vol. 42 No. 2, pp. 60-67.
Diekmann, J.E., Sewester, E.E. and Taher, K. (1988), Risk
Management in Capital Projects,Construction Industry Institute,
Austin, TX.
Dosi, G. and Egidi, M. (1991), Substantive and procedural
uncertainty: an exploration ofeconomic behaviours in changing
environments, Journal of Evolutionary Economics,Vol. 1 No. 2, pp.
145-168.
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http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F13598540910970072&isi=000268619000001http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F13598540910970072&isi=000268619000001http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1540-5915.2007.00151.x&isi=000245228000006http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1540-5915.2007.00151.x&isi=000245228000006http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1007%2FBF01224917http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.ijpe.2006.12.017&isi=000247793900033http://emerald-prod.literatumonline.com/action/showLinks?isi=000224650600011http://emerald-prod.literatumonline.com/action/showLinks?isi=000224650600011http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574090410700275http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574090410700275
-
Geary, S., Childerhouse, P. and Towill, D. (2002), Uncertainty
and the seamless supply chain,Supply Chain Management Review, Vo. 6
No. 4, pp. 52-61.
Ghagde, A., Dani, S. and Kalawsky, R. (2012), Supply chain risk
management: present andfuture scope, International Journal of
Logistics Management, Vol. 23 No. 3, pp. 313-339.
Harland, C., Brenchley, R. and Walker, H. (2003), Risk in supply
networks, Journal ofPurchasing and Supply Management, Vol. 9 No. 2,
pp. 51-62.
Hendricks, K.B. and Singhal, V.R. (2003), The effect of supply
chain glitches on shareholdervalue, Journal of Operations
Management, Vol. 21 No. 5, pp. 501-522.
Hendricks, K.B. and Singhal, V.R. (2005), An Empirical analysis
of the effect of supply chaindisruptions on long-run stock price
performance and risk of the firm, Production andOperations
Management, Vol. 14 No. 1, pp. 35-52.
Hendricks, K.B., Singhal, V.R. and Zhang, R. (2009), The effect
of operational slack diversification,and vertical relatedness on
the stock market reaction to supply chain disruptions, Journal
ofOperations Management, Vol. 27 No. 3, pp. 233-246.
Hetland, P. (2003), Uncertainty management, in Smith, N. and
Telford, T. (Eds), Appraisal, Riskand Uncertainty, Teldford,
London.
Holton, G.A. (2004), Defining risk, Financial Analysts Journal,
Vol. 60 No. 6, pp. 19-25.
Hult, G.T.M., Craighead, C.W. and Ketchen Jr, D.J. (2010), Risk
uncertainty and supply chaindecisions: a real options perspective,
Decision Sciences Journal, Vol. 33 No. 1, pp. 3-5.
Juttner, U. (2005), Supply chain risk management: understanding
the business requirementsfrom the practitioners perspective,
International Journal of Logistics Management, Vol. 16No. 1, pp.
120-141.
Juttner, U., Peck, H. and Christopher, M. (2003), Supply chain
risk management: outlining anagenda for future research,
International Journal of Logistics Research and Applications,Vol. 6
No. 4, pp. 197-210.
Khan, O. and Burnes, B. (2007), Risk and supply chain
management: creating a researchagenda, International Journal of
Logistics Management, Vol. 18 No. 2, pp. 197-216.
Kleindorfer, P.R. and Saad, G.H. (2005), Managing disruption
risks in supply chains, Productionand Operations Management, Vol.
14 No. 1, pp. 53-68.
Knight, F.H. (1921), Risk, Uncertainty and Profit, Houghton
Mifflin, Boston, MA and New York, NY.
Kylaheiko, K. (1995), Coping with Technology: A Study on
Economic Methodology and StrategicManagement of Technology,
doctoral dissertation, Lappeenranta University of
Technology,Lappeenranta.
Kylaheiko, K., Sandstrom, J. and Virkkunen, V. (2002), Dynamic
capability view in terms of realoptions, International Journal of
Production Economics, Vol. 80 No. 1, pp. 65-83.
Langlois, R.N. (1984), Internal organization in a dynamic
context: some theoretical considerations,in Jussawalla, M. and
Ebenfield, H. (Eds), Communication and Information Economics:
NewPerspectives, Elsevier Science Ltd, North Holland and
Amsterdam.
Loasby, B.J. (1976), Choice, Complexity and Ignorance: An
Enquiry into Economic Theory andPractice of Decision Making,
Cambridge University Press Ess, Cambridge.
Mansouri, S.A., Gallear, D. and Askariazad, M.H. (2012),
Decision support for build-to-ordersupply chain management through
multiobjective optimization, International Journal ofProduction
Economics, Vol. 135 No. 1, pp. 24-36.
Manuj, I. and Mentzer, J.T. (2008), Global supply chain risk
management, Journal of BusinessLogistics, Vol. 29 No. 1, pp.
133-155.
March, J.G. and Shapira, Z. (1987), Managerial perspective on
risk and risk taking,Management Science, Vol. 33 No. 11, pp.
1404-1418.
Mason-Jones, R. and Towill, D.R. (1998), Shrinking the supply
chain uncertainty circle, Control,Vol. 24 No. 7, pp. 17-22.
17
On uncertaintyin supply chain
risk management
Dow
nloa
ded
by I
QR
A U
NIV
ER
SIT
Y A
t 05:
00 2
8 Se
ptem
ber
2014
(PT
)
http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1287%2Fmnsc.33.11.1404&isi=A1987L037600003http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.jom.2008.09.001&isi=000265046400004http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.jom.2008.09.001&isi=000265046400004http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574090510617385http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2FS1478-4092%2803%2900004-9http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2FS1478-4092%2803%2900004-9http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1080%2F13675560310001627016http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.jom.2003.02.003&isi=000187153200001http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.ijpe.2010.11.016&isi=000297402700004http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.ijpe.2010.11.016&isi=000297402700004http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.2469%2Ffaj.v60.n6.2669&isi=000225551100010http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574090710816931http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2FS0925-5273%2802%2900244-X&isi=000179116400008http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1937-5956.2005.tb00008.xhttp://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1937-5956.2005.tb00008.xhttp://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1002%2Fj.2158-1592.2008.tb00072.xhttp://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1002%2Fj.2158-1592.2008.tb00072.xhttp://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1937-5956.2005.tb00009.xhttp://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1937-5956.2005.tb00009.xhttp://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574091211289200
-
Minahan, T. (2005), The Supply Risk Benchmark Report, Aberdeen
Group, Boston, MA.
Mitchell, V.W. (1995), Organisational risk perception and
reduction: a literature review, BritishJournal of Management, Vol.
6 No. 2, pp. 115-133.
Narasimhan, R. and Talluri, S. (2009), Perspectives on risk
management in supply chains,Journal of Operations Management, Vol.
27 No. 2, pp. 114-118.
Pearson, C.M. and Clair, J.A. (1998), Reframing crisis
management, Academy of ManagementReview, Vol. 23 No. 1, pp.
59-76.
Peck, H. (2005), Drivers of supply chain vulnerability: an
integrated framework, InternationalJournal of Physical Distribution
and Logistics Management, Vol. 35 No. 4, pp. 210-232.
Prater, E. (2005), A framework for understanding the interaction
of uncertainty and informationsystems on supply chains,
International Journal of Physical Distribution &
LogisticsManagement, Vol. 35 No. 1, pp. 524-539.
Prater, E., Biehl, M. and Smith, M.A. (2001), International
supply chain agility. Tradeoffsbetween flexibility and uncertainty,
International Journal of Operations & ProductionManagement,
Vol. 21 Nos 5-6, pp. 823-839.
Rao, S. and Goldsby, T.J. (2009), Supply chain risks: a review
and typology, InternationalJournal of Logistics Management, Vol. 20
No. 1, pp. 97-123.
Ritchie, B. and Brindley, C. (2007), Supply chain risk
management and performance: a guidingframework for future
development, International Journal of Operations and
ProductionManagement, Vol. 27 No. 3, pp. 303-322.
Sanchez-Rodrigues, V., Potter, A. and Naim, M. (2010),
Evaluating the causes of uncertainty inlogistics operations,
International Journal of Logistics Management, Vol. 21 No. 1, pp.
45-64.
Sanchez-Rodrigues, V., Stantchev, D., Potter, A., Naim, M. and
Whiteing, A. (2008), Establishinga transport operation focused
uncertainty model for the supply chain, InternationalJournal of
Physical Distribution & Logistics Management, Vol. 38 No. 5,
pp. 388-411.
Shaer, S. and Goedhart, J. (2009), Risk and the consolidated
supply chain: rethinking establishedbest practices, APICS Magazine,
July/August, pp. 41-43.
Sheffi, Y. (2005), The Resilient Enterprise: Overcoming
Vulnerability for Competitive Advantage,MIT Press, Cambridge,
MA.
Simon, H. (1957), Models of Man: Social and Rational;
Mathematical Essays on Rational HumanBehavior in a Social Setting,
John Wiley, New York, NY.
Snell, P. (2010), Beware of the risks, and snap up the
opportunities in 2010, IFPSM ezineHighlights, January 11, cited 20
January 2012, available at: www.ifpsmezine.org/S(accessed 20
January 2012).
Snyder, L.V. and Shen, M. (2006), Supply and demand uncertainty
in multi-echelon supplychains, working paper, available at:
http://coral.ie.lehigh.edu/Blarry/wp-content/pubs/suppdem10.pdf
(accessed 20 January 2012).
Sorensen, L.B. (2005), How risk and uncertainty is used in
supply chain management: a literaturestudy, International Journal
of Integrated Supply Management, Vol. 1 No. 4, pp. 387-409.
Swink, M. and Zsidisin, G. (2006), On the benefits and risks of
focused commitment tosuppliers, International Journal of Production
Research, Vol. 44 No. 20, pp. 4223-4240.
Tang, O. and Nurmaya Musa, S.N. (2010), Identifying risk issues
and research advancements insupply chain risk management,
International Journal of Production Economics, Vol. 133No. 1, pp.
25-34.
Tomlin, B. (2006), On the value of mitigation and contingency
strategies for managing supplychain disruption risks, Management
Science, Vol. 52 No. 5, pp. 639-657.
Torraco, R.J. (2005), Writing integrative literature reviews:
guidelines and examples, HumanResource Development Review, Vol. 4
No. 2, pp. 356-367.
18
IJLM25,1
Dow
nloa
ded
by I
QR
A U
NIV
ER
SIT
Y A
t 05:
00 2
8 Se
ptem
ber
2014
(PT
)
http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.jom.2009.02.001&isi=000264567100002http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1080%2F00207540600575761&isi=000240297500002http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F01443570110390507&isi=000169462700017http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F01443570110390507&isi=000169462700017http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09600030810882807http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09600030810882807http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1016%2Fj.ijpe.2010.06.013&isi=000292942100004http://emerald-prod.literatumonline.com/action/showLinks?isi=000071557500006http://emerald-prod.literatumonline.com/action/showLinks?isi=000071557500006http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574090910954864http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574090910954864http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1287%2Fmnsc.1060.0515&isi=000237580500001http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09600030510599904http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09600030510599904http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F01443570710725563&isi=000246004800004http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F01443570710725563&isi=000246004800004http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1467-8551.1995.tb00089.xhttp://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1111%2Fj.1467-8551.1995.tb00089.xhttp://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1504%2FIJISM.2005.006302http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1177%2F1534484305278283http://emerald-prod.literatumonline.com/action/showLinks?crossref=10.1177%2F1534484305278283http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09600030510615833http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09600030510615833http://emerald-prod.literatumonline.com/action/showLinks?system=10.1108%2F09574091011042179
-
Trkman, P. and McCormack, K. (2009), Supply chain risk in
turbulent environments-aconceptual model for managing supply chain
network risk, International Journal ofProduction Economics, Vol.
119 No. 2, pp. 247-258.
Van der Vorst, J.G.A.J. and Beulens, A.J.M. (2002), Identifying
sources of uncertainty to generatesupply chain redesign strategies,
International Journal of Physical Distribution andLogistics
Management, Vol. 32 No. 6, pp. 409-430.
Vilko, J.P.P. and Hallikas, J.M. (2012), Risk assessment in
multimodal supply chains,International Journal of Production
Economics, Vol. 140 No. 2, pp. 586-595.
Wagner, S.M. and Neshat, N. (2010), Assessing the vulnerability
of supply chains using graphtheory, International Journal of
Production Economics, Vol. 126 No. 1, pp. 121-129.
Ward, S. and Chapman, C. (2003), Transforming project risk
management into project uncertaintymanagement, International
Journal of Project Management, Vol. 21 No. 2, pp. 97-105.
Waters, D. (2007), Supply Chain Risk Management: Vulnerability
and Resilience in Logistics,Kogan Page Limited, London.
Weick, K.E. (1993), The collapse of sensemaking in
organizations: the Mann Gulch disaster,Administrative Science
Quarterly, Vol. 38 No. 4, pp. 628-652.
Wilding, R. (1998) The supply chain complexity triangle:
uncertainty generation in the supplychain, International Journal of
Physical Distribution & Logistics Management, Vol. 28No. 8, pp.
599-616.
Yang, Y.C. (2011), Risk management of Taiwans maritime supply
chain security, SafetySciences, Vol. 49 No. 3, pp. 382-393.
Further reading
Kylaheiko, K. (1998), Making sense of technology: towards a
synthesis between neoclassicaland evolutionary approaches,
International Journal of Production Economics, Vols 56-57Nos 1-3,
pp. 319-332.
About the authors
Dr Jyri Vilko (DSc Econ and Bus Adm, MSc, Tech) is a
Post-Doctoral Researcher in the School ofBusiness at the
Lappeenranta University of Technology, Finland. His recent research
interestsare in the areas of supply chain risk management,
inter-firm relations, operations research andoutsourcing. He has
published on these topics in high-quality academic journals such
asInternational Journal of Production Economics and International
Journal of Shipping andTransport Logistics. He has also been
involved in business practice with regard to these topicsthrough
his research, and in speaker and advisory roles. Previously he
worked as a ProjectManager in the project STOCA studying risk in
the cargo flows of Gulf of Finland in emergencysituations. Dr Jyri
Vilko is the corresponding author and can be contacted at:
[email protected]
Dr Paavo Ritala (DSc Econ and Bus Adm) is a Post-Doctoral
Researcher in the School of Businessand a Research Manager in the
Technology Business Research Center at the Lappeenranta
Universityof Technology. His recent research interests are in the
areas of inter-organisational networks, businessmodels, innovation
and coopetition (collaboration between competing firms). He has
published onthese topics in high-quality academic journals such as
Journal of Product Innovation Management,British Journal of
Management & Technovation. He has also been involved in
business practice withregard to these topics through his research,
and in speaker and advisory roles.
Dr Jan Edelmann (DSc, Econ and Bus Adm) is currently working in
the private sector.His research interests include innovation
management, investment decision-making and strategy.
To purchase reprints of this article please e-mail:
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This article has been cited by:
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http://dx.doi.org/10.1080/00207543.2014.948579
