Content uploaded by Barbara Gaudenzi
Author content
All content in this area was uploaded by Barbara Gaudenzi on Sep 02, 2015
Content may be subject to copyright.
The International Journal of Logistics Management
Managing risks in the supply chain using the AHP method
Barbara Gaudenzi Antonio Borghesi
Article information:
To cite this document:
Barbara Gaudenzi Antonio Borghesi, (2006),"Managing risks in the supply chain using the AHP method",
The International Journal of Logistics Management, Vol. 17 Iss 1 pp. 114 - 136
Permanent link to this document:
http://dx.doi.org/10.1108/09574090610663464
Downloaded on: 02 September 2015, At: 08:35 (PT)
References: this document contains references to 74 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 4811 times since 2006*
Users who downloaded this article also downloaded:
Uta Jüttner, (2005),"Supply chain risk management: Understanding the business requirements from a
practitioner perspective", The International Journal of Logistics Management, Vol. 16 Iss 1 pp. 120-141
http://dx.doi.org/10.1108/09574090510617385
Ila Manuj, John T. Mentzer, (2008),"Global supply chain risk management strategies", International
Journal of Physical Distribution & Logistics Management, Vol. 38 Iss 3 pp. 192-223 http://
dx.doi.org/10.1108/09600030810866986
Shashank Rao, Thomas J. Goldsby, (2009),"Supply chain risks: a review and typology", The International
Journal of Logistics Management, Vol. 20 Iss 1 pp. 97-123 http://dx.doi.org/10.1108/09574090910954864
Access to this document was granted through an Emerald subscription provided by emerald-srm:284336 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Managing risks in the supply
chain using the AHP method
Barbara Gaudenzi and Antonio Borghesi
University of Verona, Faculty of Economics, Via dell’Artigliere n, Verona, Italy
Abstract
Purpose – The aim of the research is to provide a method to evaluate supply chain risks that stand in
the way of the supply chain objectives.
Design/methodology/approach – An analytical hierarchy process model is proposed to identify
supply chain risk factors with a view to improving the objective of customer value. The two phases of
the method are the prioritization of supply chain objectives; and the selection of risk indicators. A case
study is also presented.
Findings – The appreciation of the most critical supply chain risks comes from careful evaluations of
the impacts and a consideration of the cause-effect relationships. The involvement of key managers is
essential. In the case study the two most divergent evaluations were from the logistics manager and
the sales manager.
Research limitations/implications – Further application in various companies and industry
sectors would be helpful to compare different cases and findings.
Practical implications – The model allows for flexibility in using (and the frequent monitoring of) a
panel of indicators by management. The dashboard is composed of only a few indicators and helps in
ensuring a synthesis among different perspectives. For these reasons it gives an useful contribution to
practitioners.
Originality/value – The model seems helpful in creating awareness of supply chain risk. The
involvement of managers from different areas is essential in establishing a thorough consideration of
critical issues and interdependencies in determining a complete risk analysis. The method can support
managers in setting up a priority hierarchy for risk treatment.
Keywords Supply chain management, Risk management, Analytical hierarchy process
Paper type Research paper
Introduction
The increasingly risky environment (Zsidis in, 2003) in which companies now operate is
characterized by a number of risk components, factors, sources, and drivers (Borghesi
and Gaudenzi, 2004). The term “supply chain vulnerability” (Svensson, 2002) has been
used to describe the interdependences and risks that exist among organizations as they
rise to the challenge of “better, faster, cheaper”.
It is worth highlighting two aspects connected to the risk assessment in supply
chains:
(1) risk exists at various levels, inside the company and at the network level; and
(2) the risk evaluation is inherently subjective, becau se each analyst has his or her
own concept of what constitutes a risk and of what is the nature of the upstream
and downstream relationships.
Supply chain risk has been defined as “any risk to the information, material and
product flow from original suppliers to the delivery of the final product” (Christopher
et al., 2003a, b). Risk factors can be considered in terms of:
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0957-4093.htm
IJLM
17,1
114
The International Journal of Logistics
Management
Vol. 17 No. 1, 2006
pp. 114-136
q Emerald Group Publishing Limited
0957-4093
DOI 10.1108/09574090610663464
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
.
what drives the risk;
.
where the risk is; and
.
what the risk is associated with.
Supply chain risks and supply chain risk factors can be identified in various
ways – depending on the perspective adopted. However, supply chain risk assessment
should be linked to the specific objectives of the supply chain which should “guide” the
selection of risk indicators. In the paper we present a model for assess ing risk in supply
chains based on the analytic hierarchy process (AHP). The AHP supports managers in
prioritising the supply chain objectives, identifying risk indicators and assessing the
potential impact of negative events and the cause-effects relationships along the chain.
A case study is used to show the relevance of the method.
Literature review
Supply chain management
The literature defines logistics in various ways (Perret and Jaffeux, 2002; Slack et al.,
2004) including a functional perspective or a process perspective (Srivastava et al.,
1998; Daven port, 1993; Bowersox et al., 2002; Borghesi, 2005), flows and processes
inside and outside the company (Cox and Alderson, 1950; Bucklin, 1966) and the
processes involved in the chain (Stock and Lambert, 2001: Chopra and Meindl, 2004).
The supply chain has been defined as:
... the network of organisations that are linked through upstream and downstream linkages,
in the different processes and activities that produce value in the form of products and
services in the hands of the ultimate customer (Christopher, 1998).
Each organization in the chain has its own internal philosophy and goals, but all
members should share common supply chain objectives with respect to the final
market. In addition, they should all be aware of the nature of the relationships with
other members in the chain (Mentzer et al., 2001). The concept of a supply chain
network structure refers to the “branches an d roots” that need to be managed in the
supply chain tree constituted by the network of customers and suppliers (Lambert,
2001). If the supply chain is a network by its very nature, the concept of supply chain
network structure refers to the horizontal and vertical dimensions of the structure, in
terms of which various partners might form strategic partnerships in achieving
objectives (Gregory an d Rawling, 2003; Otto, 2003).
The main objectives of supply chain management can be categorized as:
.
Customer value and custo mer satisfaction. The organizations within the chain
should share a focus on the end-customer if they are to develop a supply chain
strategy that achieves the highest level of service for the customer (Borghesi,
2001). The organizations can then create value for the customers (and for
themselves) by improving the rate of customer retention – and hence
profitability (Christopher and Peck, 2003). For the purposes of this paper three
critical service elements are the components of the “perfect order-index”.
These are on-time-delivery (the number of deliveries that meet customer
expectations in terms of time), order com pleteness (the number of deliveries that
are complete), and error- and damage-free delivery (the number of deliveries
that have “clean” invoices).
Managing risks
in the supply
chain
115
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
.
Reactivity. To achieve the goal of reac tivity supply chains must focus on time
compression, in order to reduce lead times (Waters, 2003) and achieve pipeline
efficiency (Burton and Boeder, 2003); also in order to adapt to ranges in demand,
supply chains must be flexible enough to cope with the uncertainty of a rapidly
changing environment (Chopra and Meindl, 2004).
Time compression can be used t o improve product availability through lead-time
management to ensure shorter order cycles (Christopher, 1998). It requires a
synchronization of flows and activities inside the organization and throughout the
chain (Towill, 1996).
Risk management
The aim of risk management is the protection of the business from adverse events and
their effects (Borghesi, 1985). T he risk management process consists of four phases:
(1) risk assessment (which can be broken do wn into risk analysis and risk
evaluation);
(2) risk reporting and decision;
(3) risk treatment; and
(4) risk monitoring (AIRMIC, 2002).
Recent approaches to risk assessment suggest the drivers of key risk or risk areas
should be identified – such as information systems, intangible assets, and safety
(Australian Standard, 2003).
According to the financial management perspective, risk managers monitor financial
risks – such as movements in exchang e rates, commodity prices, interest rates, and
stock prices – and control all the activities that affect revenue. At the same time, they
manage the insurable part of risks with an insurance-portfolio (Dickinson, 2001).
The perspective of enterprise risk management (ERM) is a new approach to risk
management (COSO, 2004). Although it has been called a “holistic and enterprise wide”
approach (Barton et al., 2002), and although it attempts to manage both financial risks
and operational/strategic risks, its perspective is actually “corporate governance
based” (Lam, 2003). It is concerned with monitoring and managing risks to provide
reasonable assurance to stakeholders regarding the achievement of company
objectives. Nevertheless, this “integrated” effort adopts a predominantly financial
perspective (Banham, 2003; Blake, 2003) – in that the ERM philosophy takes tools and
methods of managing financial risk and adapts them for non-financial risk (De Loach,
2000).
The business continuity and crisis management perspective (Mitroff and Anagnos,
2001) attempts to integrate the management of risks that are not insurable – such as
reputation risk or “service-drop” risk (Ogrizek and Guillery, 1999). But its role actually
seems to be confined to the effective implementation of strategies related to traditional
contingency planning (Myers, 1999).
Most risk management approaches appear, in many instances, to be
fragmented – one group buys the insurance, another administers the claims, and
another handles everything related to safety.
Organizational risk management is an extended perspective (Young and Tippins,
2002) whereby strategic management, risk management, and operations management
IJLM
17,1
116
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
are considered as separate, but overlapping, processes. According to this perspective,
risk management “crosses” supply chain management, in order to support them in the
achievement of their own objectives. In this sense, ORM could be called a
“mission-driven process”. Following this approach , risk assessment and measurement
should be based on specific goals, rather than being adap ted from the financial domain
into the business and operational domains.
Supply chain risk management
The present paper treats supply chain risk management as a process that supports the
achievement of supply chain management objectives. In this sense, risk management is
“an integral part of supply chain management” (Christopher, 2004). With respect to the
various supply ch ain goals discussed above, it is helpful if risk is understood as a
multi-faceted phenomenon.
For example, from the financial perspective, the management of supply chain risks
involves the management of cash-flow variations that result from operational
activities; moreover, from the perspective of corporate governance (Meulbroek, 2002),
management should monitor the efficacy and efficiency of supply chain operations to
ensure that the level of risk in these areas is within the company’s global risk tolerance.
From the perspective of business continuity and crisis management, supply chain
risk management is an integrated management approach along the whole chain (Adams
et al., 2002) – with a view to managing “the exposure to serious business disruption,
arising from risks within the supply chain as well as risks external to the supply chain.”
In this sense, the goal of supply chain risk management is “the ability to react quickly to
ensure conti nuity” (Van Hoek, 2003 ; Rowbottom, 2004). From the reputation
management perspective (O’Rourke, 2004), organizations manage and mitigate risk
against events that could affect the company’s image in the perception of stakeholders.
Another perspective is oriented towa rds the goal of reliability (Moore, 2002), and the
achievement of the best trade-off between quality controls (through inspections) and
process self-control. In this context, supply chain risk management is a process that
aims to reduce all the deviatio ns from the normal or expected (Svensson, 2002), often
utilising the Six Sigma approach and tools (Eckes, 2001).
Other approaches to supply chain risk management involve the management of
risks affecting:
.
specific supply-chain “levels” such as the physical, financial, informational, and
innovation levels (Cavinato, 2004);
.
particular systems inside and outside the chain, such as the information system
(Finch, 2004);
.
or specific project (Halman and Keizer, 1994) when the aim is to identify and
manage risks that threaten the project’s success (Ramgopal, 2003).
In that case risk factors could be considered to be “causes of the project failure”
(Spekman and Davis, 2004) and any risk assessment and analysis will be oriented
towards this understanding.
From this brief review it would seem, therefore, that supply chain risk management
may be summarized as “... the identification and management of risk for the supply
chain through a co-ordinated approach amongst supply chain members” (Ju
¨
ttner et al.,
2003) in order to support the supply chain in the achievement of its objecti ves.
Managing risks
in the supply
chain
117
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Supply chain risk measurement
For the purposes of this paper, a supply chain risk measurement system is seen as a
sub-set of supply chain performance measurement.
Two principles from the performance measurement philosophy should be imported
into the supply chain risk mea surement discipline. First, the mea surement system
should be linked to the specific objectives of the chain so that the measures used can be
focused on their achievement (Neely et al., 2002). Secondly, the measurement system
requires that all members agree on processes, objectives and measures across the
supply chain – for example, using the approach of the Supply Chain Operations
Reference model – SCOR – (Anonymous, 2001), and by the Supply Chain Integrated
Management Analysis Method – SCIMA M – (Signori, 2001).
In that sense it is particularly important to engage the support of managers who
work close to the activities an d processes under consideration (Demchak, 1996). A set
of tools and techniques has been proposed to assist in the evaluation of supply chain
risk (Christopher et al., 2003a, b). In particular, the Delphi method (Miccolis and Shah,
2000) is worthy of mention – as are “brainstorming” scenario planning, critical-path
analysis, and root-cause analysis.
With respect to risk assessment in supply chains the prioritization of supply chain
objectives is essential to identifying the risks which could affect the achievement of
those objectives. The AHP seems in that sense particularly useful (Saaty, 1990, 1994).
AHP is one of the multivariate analysis techniques that help to reduce the randomness of
subjective evaluations. Its goal is to establish the “trade-off” required in complex
decision-making situations, such as consideration of different objectives based on
different criteria (Goodwin and Wright, 1998). By involving AHP, management
can define a decision problem and break it down into a multi-level sequence of decision
attributes. Then, the decision elements can be compared with each other and weights
assigned in order to define whic h are the priorities in the decision process (Zahedi , 1986).
We used the AHP in our case study for creating a prioritization among supply chain
objectives (Millet and Ewdley, 2002) and to assist decision makers who have identified
alternative courses of action to:
.
set up a decision hierarchy;
.
make pairwise comparisons of attributes and alternatives;transform the
comparisons into weights and scores for different optio ns; and
.
perform sensitivity analysis or qualitative analysis.
The AHP method can support managers in a broad range of decisions and complex
problems – including supplier-selection decisions, facility-location decisions,
forecasting, risks and opportunities modelling, choice of technology, plan and
product design, and so on (Fariborz et al., 1989).
With respect to the risk measures, as in the field of performance measurement, the
ratios are usually considered from the perspective of a focal company – with external
members having their own governance structure and culture (and thus their own
targets). In general, a lack of confidence among members of the chain can limit
informational transparency. In terms of measurement, there is, therefore, potential for a
lack of consistency in the data providing the measures. This discussion reveals the
importance, from a performance and risk perspective, of carefully selecting both
the partners in the supply chain and the shared measurement system.
IJLM
17,1
118
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Looking at the body of the literature, there are several approaches to risk analysis
(Harland et al., 2003) in the field of supply chain risk measurement. A recent complete
assessment approach identified supply chain risk sources and factors and provided
directions to mitigate the effects of such risks along the supply chain (Ju
¨
ttner et al.,
2003). In addition, risks have been described in both qualitative and quantitative terms
(Christopher et al., 2003a, b), and have been evaluated by techniques that assign to
certain indicators potential impa cts and hence importance (Zsidisin, 2004). The
quantitative evaluation of supply chain risk can be supported by statistical analysis.
The Six Sigma method, for example, investigates all the events that give rise to
variations in process performance (Eckes, 2001). Another method – the failure modes
and effect analysis method – focuses on potential failures in order to assess, prevent,
and eliminate them as early as possible (Sinha et al., 2004). In a relatively stable and
predictable environment a suite of statistical controls can be used in the management
of supply chain risks (Aichlmayr, 2001). In a complex environment, statistical control
tools can still be helpful (Christopher and Rutherford, 2004). However, according to the
“lean-agile” paradigm (Towill and Christopher, 2003) these should be supported by
other tools. Nonetheless, a wider panel of approaches and points of view in supply
chain risk measurement would appear to be required – for two further reasons.
First, in complex networks in which risk monitoring is a difficult challenge
(Braithwaite and Hall, 1999) it is worth looking for a transversal data-base of
information (Harland et al., 2003). Secondly, from an operational and business-based
risk perspective, statistical risk-modelling tools have been identified as being
“imperfect” – because they could expose managers to risks they were trying to avoid
by using the models. Decision-making could thus become risky if they are based on a
one-sided or narrow information base. It has been observed that:
... better tools available are structural models of risks that capture cause-and-effect
relationships between risk factors and outcomes (Miccolis and Shah, 2001).
The measurement of damage and defect risks is helpful in achieving the objective
of shrinkage reduction (Chapman et al., 2001) – by controlling the average severity
and probability of losses. The physical risk can be further measured in terms of
the potential damages that could be inflicted upon goods in physical spaces (such
as warehouses and production lines). These measures, called “physical risk
indexes” are derived from inspection procedures by exte rnal experts.
Supply chain risk measures are monitored from both the supply chain perspective
and the top-management “control” perspective (Kirk, 1999). In the supply chain
approach, the objectives are considered in terms of the organization’s goals with
respect to the final market. From this perspective, the risk evaluation, in terms of
weights and importance of indicators, should be guided by an awareness of the nature
and importance of the market objectives.
Research design, methodology, and model
For the purposes of the present study, the supply chain was broken down to five
areas – involving the flows and processes of the chain both inside and outside the focal
company. Those areas were:
.
transport/distribution;
.
manufacturing;
Managing risks
in the supply
chain
119
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
.
order cycle;
.
warehousing; and
.
procurement.
The major supply-chain objective in the model was the creation of customer value.
This was driven by the “perfect-order index” – the critical service elements of which
represented the sub-ob jectives of the supply chain. These sub-objectives were:
.
on-time delivery;
.
order completeness;
.
order correctness; and
.
damage-free and defect-free delivery.
Risk indicators were identified in each area particularly with a view to achieving the
objective of “perfect order” improvement (Figure 1). Each area was affected by
different risk factors, depending on the sub-objectives.
The aim of the model was to provide a method to identify a panel of risk indicators
that could be applied at various levels of the chain (at different production or selling
phases). The two phases of the method are:
(1) the prioritization of supply chain objectives using the AHP method; and
(2) the selection and evaluation of risk factors and ratios.
The AHP method has been useful moreover in setting up a priority hierarchy for risk
treatment. That prioritization in managi ng risks depends on the importance of the
objectives they affect. That importance could be initially defined using the AHP
method.
The theoretical model was tested in an organization that sells (and partly
co-designs) medical equipment for doctors – as individual professionals and as
healthcare firms. The company’s supply chain had to ensure a high service level
(required by the customers) and had to work with sma ll and fragmented orders (which
were of high value with frequent product improvements). The required agility of the
company thus suited the theoretical context of the model. In addition, the organization
had many indicators of a low/high value, although their impact was, respectively,
high/low – bec ause of the strong correlation effect among factors. A careful evaluation
of risk factors thus seemed to be especially relevant.
Four phases were involved in defining the panel of theoretical risk indicators.
Figure 1.
Supply chain objectives
and areas: the basis for the
risk assessment
Transport
/ distrib.
Manufact -
uring
Order
cycle
Ware -
housing
Procure -
ment
ON TIME
DELIVERY
ORDER
COMPLETE
DAMAGE/
DEFECT FREE
ORDER
CORRECTNESS
CUSTOMER
VALUE
Transport
/ distrib.
Manufact -
uring
Order
cycle
Ware-
housing
Procure -
ment
Transport
/ distrib.
Manufact -
uring
Order
cycle
Ware -
housing
Procure -
ment
Transport
/ distrib.
Manufact -
uring
Order
cycle
Ware -
housing
Procure -
ment
IJLM
17,1
120
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Phase 1
To prioritize the objectives, the individual eval uations of managers from different
areas were taken into account. The AHP method was used to prioritize objectives, and
to match these prioritized objectives with different perspectives. The following AHP
steps were undertaken:
(1) an assessment of “criticalities” affecting the objectives – in order to assess their
importance;
(2) a quantitative evaluation of the importan ce of each objective, compared with
every other objective; and
(3) an assessment of the weights for the objectives (and a final check).
Each manager could identify risk factors and problems that could affect his
job-objectives. That evaluation helped in defining and prioritizing the role and
importance of the objectives. Each manager expressed a different perspective in that
evaluation, depending on his job focus. As the first step of the AHP method the
managers defined a set of “criticalities” in the achievement of the obje ctives. That
critical points should be used as “drivers” in quantifying the priority of objectives and
potentially, in the next step, as “drivers” in risk eval uation. At the second and third
steps of the AHP method the managers expressed their comparison between objectives.
It means they answered the question: which of the two objectives is more important
and how strongly, using a numerical scale? All the comparisons should be
consequently checked in order to assure the consistency and the coherence of the
evaluation. Setting up a panel of weights for the objectives helped in two decisions:
(1) defining which risks were more serious; and
(2) building the priorities in managing risks.
Phase 2
To assess which risk measures describe the risk of not achieving the objective of the
“perfect” order, three steps were followed.
(1) First, risk factors were identified in each area following different criteria. With
respect to on-time delivery, unforeseen events that afflict the processes, the
nature of the demand, the reliability of the perfect-order cycle, the integration of
suppliers, the concatenation of process phases, and the physical damage index
were all identified. In addition, some ratios that explain “bad performance” were
considered as potential causes of future bad performance (assuming they were
consistent). With respect to order completeness, risk factors were related both to
the potential for errors (thos e that derive from the order cycle), and to the
deliberate delivery of an incomplete order (rather than being late with the full
order). In the latter case, the reason for the non-availability of product should be
considered (such as manufacturing, warehousing, or procurement delays or
errors). Risk factors affecting the order correctness related to errors in both
billing and transport. Risk factors affecting the delivery of damage-free and
defect-free orders depended on the quality of products (and, upstream, of
materials) and on handling and transport activ ities. In this respect, atten tion
should be paid to the level of damage in internal activities (if it is consistent),
and the extent of quality control.
Managing risks
in the supply
chain
121
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
(2) Secondly, only measurable ratios were selected. The theoretical panel should be
adapted to each particular case by the selection of ratios that are objecti ve (not
based on opinions), and relevant (providing appropriate information).
(3) Finally, a panel of indicators that showed the relationships upstream and
downstream along the chain was selected (Table I).
Phase 3
In evaluating the ratios, the knowledge of individual managers assists in
understanding the potential impact of various risk factors. The potential impact of
events, and the cause – effect relationships inside the organization and along the chain,
can thus be evaluated. Different managers have different perspectives, and these
should be reconciled. The Delphi method can be used in those evalua tions – to help
managers in the screening of as many factors as possible.
Phase 4
Finally, in deciding how to represent the data, the risk factors were assessed in terms of
their impact (“high” “medium” or “low”). The dependencies between factors and the
cause-effect relationships can be illustrated with a flow chart (such as an Ishikawa
diagram). The representation of those potential effects and dependencies should be
contextualised in the supply chain – in terms of areas and objectives. In this respect, it
is helpful to construct a matrix that takes into account supply chain areas, objectives,
and risk factors.
Empirical results of the case study
Evaluation of objectives
The study involved four key managers of the focal company described above: the
logistics manager, the warehousing manager, the customer-care director, and the
purchasing manager. The company, which doubled its revenue from 1996 to 2004, sells
to Italian and European dental and medical specialists (52 first-tier clients and their
connected clients) through 110 direct sellers and the company’s marketing offices.
The company aims to deliver orders within 24 hours with a high level of customer
service in terms of technical, maintenance, and innovation services.
In accordance with the AHP method of ascertaining the prior itization of objectives,
the managers were asked to define the crit icalities and risk factors which affect the
supply chain goals. (Figure 2).
Subsequently the managers were asked to rank the importance of various objectives
from their own perspectives. They provided a quantitative prioritization of objectives
comparing the importance of each. Table III shows the preliminary results. No
objective was assessed as being more than three times mor e important than any other
(only one manager described “on-time delivery” as being three times more important
than “damage-free and defect-free”). An example of two different comparisons between
“on time delivery” and “order completeness” made by the sales manager and logistics
manager, is shown in Table II. It highlights the significan ce of the comparisons and
which criticalities they are based on. Finally, Table III shows the results for all the
objectives.
In all the comparisons the two most divergent evaluations were from the logistics
manager and the sales manager, particularly in evaluating “order completeness”.
IJLM
17,1
122
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Value Impact
Objective Risk area Indicators Description
If
significant
High, med,
low
On time delivery
Transport – distribution Number of not on time deliveries re. carrier
agreement/total deliveries (outsourcing)
The number of not-on time deliveries (not
meeting the agreement with carriers)
represents potential future delays
Number of not on time deliveries re. customers
agreement/total deliveries (no outsourcing)
The number of not-on time deliveries (not
meeting the agreement with clients) represents
potential future delays
Delays due to unforeseen events/total delays The unpredictability of delays means a lack of
control and increases the risk of delays
Number of unplanned machine stoppages/total
machine stoppages
Unscheduled stoppages can affect production
times
(Actual output – planned output)/planned
output
Unpredictable variation of production can
affect production times
(Actual sales- forecasted sales)/forecasted sales Unpredictable demand and forecasting’ errors
(both in positive and in negative) can affect
production times
Number of linked production phases/total
number of production phases
When phases are linked with each other, every
delay can increasingly seriously affect the
entire process
Manufacturing 1 – (No. of monitored manufacturing
phases/total no. of manufacturing phases)
The monitoring of phases helps to avoid
delays and to react rapidly to interruptions
1 – (quantity of monitored materials and
components/total quantity of materials and
components)
The monitoring of materials helps to avoid
delays and to understand the nature of
problems
Promotional orders/total orders Promotional orders are usually affected by
more unpredictable variations and shorter lead
time
1–(Confirmeddeliverywindowsfor
promotions/confirmed delivery windows)
In case of a high level of promotional orders,
it’s difficult to respect particularly restrictive
promotional windows
Damages index of plant (physical indicator) A high level of damages represents a potential
risk of future interruptions
(continued)
Table I.
The panel of risk
indicators
Managing risks
in the supply
chain
123
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Value Impact
Objective Risk area Indicators Description
If
significant
High, med,
low
Late orders/total orders The number of delays caused by order cycle
represents potential future delays
Order cycle 1 – (Computerised order cycle phases/total
order cycle phases)
Information integration helps to avoid delays
and to respond more rapidly; the lack of
integration can be a source of risk
No. of errors in order cycle data input/total
order cycle data input
The number of errors regarding time-tables
can affect delays
1–(Verticaltime/horizontaltime) Specificallyreferringtoon-timedelivery,
safety stock (stock-time related to added-value
times) helps reduce delays
No. of machine stoppages due to lack of
materials/total no. of machine stoppages
The lack of particular materials can cause
stoppages and affect the process time
No. of machine stoppages due to defective
materials/total no. of machine stoppages
Defective materials can cause stoppages and
affect the process time
Ware housing Damaged goods/handled goods Damage levels during handling activities can
cause potential stoppages or defects
Handling time/warehouse processing time Handling times can slow down the process
(this depends on the process structure and
phases relations)
Warehouse damage index (physical indicator) A high level of damages represents a potential
risk of future interruptions
1–(Ontimeorders/totalorders) Supplierdelays(intermsoftime-performance)
can represent a risk trend
1–(Completeordersdelivered/totalorders
delivered)
Incomplete deliveries from suppliers (in terms
of completeness) can represent a trend risk
1–(correctordersdelivered/totalorders
delivered)
Incorrect deliveries from suppliers can
represent a trend risk
Procurement No. of urgent orders/total orders Urgent orders to suppliers are exposed to the
risk of beeing delivered later then normal
orders
Either 1 – (No. of new suppliers/total
suppliers)
Ahighsupplier-turnovercouldhelptoachieve
flexibility and responsiveness
(continued)
Table I.
IJLM
17,1
124
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Value Impact
Objective Risk area Indicators Description
If
significant
High, med,
low
OR (No. of new suppliers/total suppliers) A high supplier-turnover could represent less
collaboration and short-term relations
1–(No.ofelectronicinterchangeddata/total
interchanged data)
A lack of information integration can cause a
lack of visibility
Completeness
Incompleteness
Order cycle No. of order data input errors/total order data
input
Number of errors caused by the order cycle can
affect delivery completeness
Manufacturing No. of incomplete orders due to manufacturing
delays/total incomplete orders
Manufacturing delays can lead to delivery of
incomplete orders
Warehousing No. of incomplete orders due to warehousing
delays/total incomplete orders
Warehousing delays can lead to delivery of
incomplete orders
No. of incomplete orders due to stock-out/total
incomplete orders
Stock outs of products or raw materials can
lead to delivery of incomplete orders
Procurement No. of incomplete orders due to supplier
delays/total incomplete orders
Supplier delays of products or raw materials
can lead to delivery of incomplete orders
Correctness
Incorrect billing
Order cycle No. of orders with billing errors/total orders Billing errors (actual number in the reference
period) can affect order correctness
Orders with data entry errors/total orders Input errors (actual number in the reference
period) can affect order correctness
Incorrectness due to
transport
Transport No. of incorrect deliveries due to carrier
mistakes/total orders
Careless carriers and/or incorrect routes can
affect the delivery integrity
Order cycle No. of orders with destination errors/total
orders
Destination errors caused by the order cycle
can affect order correctness
No. no-correct deliveries due to database
errors/total orders
Errors caused by Information System can
affect order correctness
(continued)
Table I.
Managing risks
in the supply
chain
125
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Value Impact
Objective Risk area Indicators Description
If
significant
High, med,
low
Damages and defects free
Transport Products damaged in transit/total products
delivered
The number of transport-damaged products
delivered to clients can affect delivery integrity
Manufacturing Defective products due to manufacturing/total
products
The number of defective products delivered to
clients (in terms of outbound) can affect
delivery integrity
1–(QualityControlScraprate) Outboundcontrolhelpstoavoiddefective
deliveries (even if requested times should be
carefully monitored)
No. defective packaging/total packaging
dispatched
The level of defective packaging can affect
delivery integrity
Ware housing Obsolete stored products/Total stored
products
The number of obsolete stored products can
affect delivery integrity
Damaged products/total handled products Damage level due to warehousing operations
can affect delivery integrity
Procurement 1 – Monitored inputs in Quality Control/total
warehousing and manufacturing inputs
Inbound control helps to avoid defective
deliveries; the absence of controls presents a
risk
Defective inputs in quality control/total
monitored inputs in quality control
The level of discovered defective inbound
materials can represent a potential
defective-delivery risk
Upstream relations
1–(Supplierretentionrate) Alackofsupplierretentioncanrepresent(or
can cause) a lack of integration
1–(No.ofsuppliersintegratedwithanexplicit
agreement/no. of stable suppliers)
High levels of informal cooperation can
represent a risk of poor service-levels
Upstream integration 1 – (Total fulfilled unplanned orders/total
unplanned orders)
The number of non-fullfilled unplanned orders
can represent a potential lack of upstream
responsiveness
1–(No.ofsupplierswithordervisibility/total
no. of suppliers)
The number of unintegrated suppliers in terms
of visibility can cause a low level of flexibility
(continued)
Table I.
IJLM
17,1
126
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Value Impact
Objective Risk area Indicators Description
If
significant
High, med,
low
jAverage delivery time – optimal delivery
timej/optimal delivery time
This deviation (in positive and negative
values) represents an upstream weakness in
terms of timeliness
Downstream relations
1 – (No. of process data shared with
distribution/process data suitable to be shared)
A lack of visibility with distributors represents
an unused source of integration
1–(Trade-marketingcampaignsco-planned
with distrib./total trade-marketing campaigns)
Alackofsharedt.m.campaignscanrepresent
alackofdownstreamintegration
Downstream integration 1 – (Product innovations shared with
distributors/product innovations suitable to be
shared)
A lack of shared innovations can represent a
lack of dowstream integration
No. of sales renegotiations with
distributors/total negotiations
Ahighlevelofre-negotiationscanrepresenta
lack of downstream integration
jAverage order time – optimal order
timej/optimal order time
This deviation (in positive and negative
values) represents the distance between
capacity and requested service
Table I.
Managing risks
in the supply
chain
127
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
On the left hand side of Figure 3 are shown the results of the prioritization by the AHP
method. On the right hand side of Figure 3 are shown the performances achieved by
the company in terms of the perfect-order components in the period before the model
application. It is apparent that the most-important defined objective, “on-time delivery”
has a poor performance.
From the theoretical panel of potential mea sures, managers in the focal compa ny
selected the 63 percent of ratios that were measurable and significant. The assessment
of the value of the ratios and the evaluation of indicators in terms of potential impact
led to the identification of the critical risk factors and their cause-effect relationships.
In the indicators panel, many ratios represent a low value but a high impact. This is
Figure 2.
An example of criticalities
identified using the AHP
method
on time
delivery
Completeness
damage/
defect free
objectives
Criticalities :
‘
setting the
importance of
the objectives
and ‘drivers’ for
evaluating risk
factors
low
intermediate
suppliers'
integration
narrow number of
intermediate
suppliers
lack of
intermediate
suppliers' visibility
lack of transport
providers'
integration
transport
providers'
fragmentation
short life time
products
lack of
outbound
effectiveness
customer
fragmentation
lack of
integration with
final-product
supplier
serious
forecasting
errors
customer
fragmentation
high level of
service
required by
customers
different level of
service required
by different
customers
high level of
service
required by
customers
linked phases in
manufacturing
short lead
times
forecasting errors
for some products
different lead times for
different products
lack of final -
product suppliers'
visibility
stock driven
supply chain
linked phases in
manufacturing
lack of information
transparency between logistics
and marketing
warehousing
and production
interruption
damages in
transport
no transport
solution
alternatives
‘criteria’ for
IJLM
17,1
128
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
due to a correlation effect with the other measures and their likely effect in achieving
the objectives. A consideration of only the value of ratios would, therefore, not be
representative. Some results in terms of calculations are shown in Table IV. The results
from the model in terms of correlations of risk factors are shown in Figure 4.
The managers’ considerations
In their init ial evaluations, managers assessed procurement as being the most risky
area. This was because a lack of integration with supplier s meant that on-time delivery
Order completeness
Intensity of
importance
Explanation Manager’s consideration of
criticalities
On time
delivery
1 (equal
importance)
Two objectives have the same
importance (risk indicators which
affect the two objective will
potentially have the same gravity)
The logistics managers attribute
equal importance to the objectives.
That evaluation is driven by the
following criticality: equal
importance of intermediate
suppliers and final product
suppliers
2 (strong
importance)
“On time delivery” is two times
more important than “order
completeness”
The sales manager attributed a
strong importance to the “on time
delivery” due to the consideration
of the following criticalities: high
level of service required by all
customers; clients have different
expectations: they don’t perceive
the logistics problems but require
punctuality
Table II.
An example of
comparisons between “on
time delivery” and “order
completeness”
On time delivery Completeness Correctness Damage-defect free
On time delivery 1 1-2 0.5-1.5 1.5-3
Completeness 1 0.5-1 1-1.5
Correctness 1 1.5-2
Damage-defect free 1
Table III.
The comparison of
objectives, using the AHP
method
Figure 3.
The objectives’
prioritization (left hand
side) and the perfect order
performance (right hand
side)
on time delivery
completeness
correctness
damage/defect free
logistics manager sales manager
pefect order performance
on time delivery
completeness
correctness
damage/defect free
Managing risks
in the supply
chain
129
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
could not be ensured when orders were urgent or changeable. Managers complained
about missed deliveries, the risk of “stock out” in the processes, and incomplete
deliveries. Because they lacked confidence, managers maintained high quality control
– in order to avoid machine stoppages or defective deliveries to clients. For these
reasons, managers were in the habit of increasing control of inbound materials, and
selecting a small number of suppliers to integrate.
Warehousing, with its short process time, was perceive d to be efficient – taking
account of timeliness as a critical objective for the company. Transport providers were
Some significant results in risk measurement
Risk indicators Ratios
Suppliers’ out-of-time deliveries/on time
deliveries from suppliers
10-25 percent (it depends on each supplier and
products delivered
Suppliers’ turnover 40 percent (it represents short-term relations and
lack of integration)
Incomplete orders due to suppliers delays/total
incomplete orders
50 percent
Warehouse damaged goods/handled goods 20 percent (high value and critical goods)
Unplanned machine stoppages in
warehousing/total stoppages
10 percent
Warehouse damage index 5 percent (all the interruptions)
Unplanned machine stoppages in
production/total stoppages
3 percent
Number of linked production phases/total
number of production phases
50 percent (high impact along the chain)
Unplanned outputs variations/planned outputs 13 percent
Forecasting’s errors/total forecasts 5 percent (high impact along the chain)
Products damaged during transport/products
delivered
15 percent
Number of incorrect deliveries due to carrier
mistakes/total orders
3 percent
Transport delays 7 percent
Table IV.
Results in risk indicators
Figure 4.
The correlations of risk
factors
TRANSPORT
WAREH. DELAYS
DAMAGE
INDEX
WAREH. LINKED UNP. MACHINE
DAM / DEF PHASES STOPPAGES
GOODS
MANUFACT
SUPPLIERS
DELAYIES
DELAYS
TRANSPORT
DELAYS ORDER
COMPLETENESS
ON TIME
DELIVERY
FORECAST'S
DEVIATIONS
DAMAGE
DEFECT
FREE
INTERMEDIATE MATERIALS
FINAL PRODUCTS
high impact
low impact
FINAL PRODUCTS
IJLM
17,1
130
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
considered reasonably efficient and prompt, although the carriers sometimes cause
damages to products. Because of this, managers were in the habit of transferring to
carriers, by contractual agreement, the risk of returned product from the clients and the
cost of reverse logistics.
The model results
With respect to the most important sub-objective, on-time delivery, supplier service
had a significant impact on delays particularly for inbound final products. The impact
of inbound raw materials on stoppages or process delays was less significant. The
impact of delays or damaged handled goods in warehousing was, nevertheless, higher.
These different impacts became apparent from an evaluation of the number of orders
that were missed due to each stoppage or delay, as a result of warehousing or defective
materials. Moreover, the warehousing and manufacturing phases were linked – with a
direct effect in terms of overall impact. Even if the ratios were not high in value, the
impact was very high in terms of missed orders. Further observations revealed that the
high level of inbound quality contro l sometimes slowed down the warehousing process
– with deleterious effects in terms of delays.
In addition, a deviation of actual sales from those forecast, even if only as little as 5
percent, created a high level of risk along the chain – because orders were often urgent
and unpredictable. The impact of the ratio was nevertheless high – thus affecting
relationships upstream.
The transport providers directly caused 7 percent of the delays to clients – as an
average ratio among the three providers. Even allowing for the fact that the objective
of damage-free and defect-free deliveries was not the most critical, the effects in terms
of poor services for key clients are often relevant – especially if damage occurred to
high-value goods destined for important clients. The strategy of a contractual risk
transfer with respect to the return cost thus seems to have been ineffective.
In terms of damages, warehousing also had a critical ratio – in term of obsolete
stored products and damages.
Conclusion of case study
The managers’ initial evaluations and the results of the model are compared in Figure 5,
and represented in the supply-chain areas. An appreciation of the most critical areas
comes from careful evaluations of the impacts and a consideration of the cause-effect
relationships (Figure 4). For example, with respect to forecasting activities, managers
in the focal company initially considered that the low level of deviations between actual
and forecast demand was irrelevant. This actually obscures unsatisfactory time
planning and a lack of visibility downstream. This, in turn, has an adverse effect on
relationships upstream – by a sort of “bullwhip” effect.
Even if the initial risk landscape suggested that the primary need was for the
creation of an integrated program upstream, the improvement of visibility downstream
(and a shorter forecasting horizon) would appear to be more important.
Conclusion and directio ns for further research
The research goal was to develop a model to assess risks in the supply chain and to
involve the AHP method in the definition of decision priorities. From the results of the
case study, the model seems helpful in creating awareness of supply chain risk factors.
Managing risks
in the supply
chain
131
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Figure 5.
The risk landscape by
area
PROCUREMENT
WARE HOUSING
ORDER CYCLE
MANUFACTURING
TRANSPORT
PROCUREMENT
WARE HOUSING
ORDER CYCLE
MANUFACTURING
TRANSPORT
ON TIME ON TIME
COMPLETENESS COMPLETENESS
CORRECTNESS CORRECTNESS
DAMAGE/DEFECT
FREE
DAMAGE/DEFECT
FREE
Managers' perceptions Model's results
high level of risk
medium level of risk high level of risk
IJLM
17,1
132
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
The involvement of managers from different areas is essential in establishing a
thorough consideration of critical issues and interdependencies in determining a
complete risk analysis.
The model allows for flexibility in using (and the frequent monitoring of) a panel of
indicators by management. For this reason the dashboard is composed of only a few
indicators. They should nevertheless be carefully evaluated to ensure tha t synthesis
among different perspectives results in the most critical activities being assessed.
The application of the AHP method seems to be particularly helpful – in order to
support the prioritization of objectives and the overa ll impact analysis.
The model could be developed in two directions. First, it could be oriented towards
the supply chain objective of reactivity – by a consideration of the timeliness
paradigm. Secondly, the model could be extended to more indicators of integration
upstream and downstream within the supply chain.
Developments in the model should nevertheless be considered only if there is an
effective capability to manage the cause-effect relationships among indicators. The
identification of too ma ny indicators can result in a difficul t evaluation of
interdependences. This, in turn, can result in an inaccurate evaluation and ineffective
decisions.
Finally, further application in various companies and industry sectors would be
helpful in collecting information about different supply chains.
References
Adams, T.J., Austin, S.P., Soprano, R.S. and Stiene, L.M. (2002), “Assessing the transition to
production risk”, Program Manager, September, pp. 10-21.
Aichlmayr, M. (2001), “The future of JIT-time will tell”, Transportation & Distribution, Vol. 42
No. 12, pp. 19-23.
AIRMIC, ALARM, IRM (2002), A Risk Management Standard, available at: www.airmic.com,
www.airmic.com; www.alarm-uk.com, www.theirm.org
Anonymous (2001), Supply Chain Operations Reference-model: Overview of SCOR Version 5.0,
Vol. 0, Supply Chain Council Inc..
Australian Standard (2003), Good Governance Principles, AS 8000-2003.
Banham, R. (2003), “Fear factor”, CFO, June, pp. 1-2.
Barton, T.L., Shenkir, W.G. and Walker, P.L. (2002), Making Enterprise Risk Management Pay
Off, Financial Time Prentice-Hall, New York, NY.
Blake, M.A. (2003), “Taking an holistic approach with enterprise risk management”, Rural
Telecommunications, November/December, pp. 58-62.
Borghesi, A. (1985), La gestione dei rischi di azienda, CEDAM, Torino.
Borghesi, A. (2001), “I processi di SCM, PDM, CRM: la nuova alleanza e la loro centralita
`
nella
funzione di marketing”, Sinergie, Vol. 56, pp. 3-36.
Borghesi, A. (2005), Marketing e Logistica, Giuffre
`
, Genova.
Borghesi, A. and Gaudenzi, B. (2004), “Risk management nella supply chain”, Sinergie, No. 63,
pp. 89-128.
Bowersox, D.J., Closs, D.J. and Cooper, M.B. (2002), Supply Chain Logistics Management,
McGraw-Hill, New York, NY, pp. 282-447.
Braithwaite, A. and Hall, D. (1999), “Risky business? Critical decisions in supply chain
management”, Supply Chain Practice, Vol. 1 No. 2, pp. 44-57.
Managing risks
in the supply
chain
133
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Bucklin, L.P. (1966), A Theory of Distribution Channel Structure, IBER, Berkley.
Burton, T.T. and Boeder, S.M. (2003), The Lean Extended Enterprise, Ross Publishing, New York,
NY, pp. 149-68.
Cavinato, J.L. (2004), “Supply chain logistics risks”, International Journal of Physical Distribution
& Logistics Management, Vol. 34 No. 5, pp. 383-8.
Chapman, P., Harrison, A. and Beck, A. (2001), “Shrinkage: a collaborative approach to reducing
stock loss in the supply chain”, Report for ECR.
Chopra, S. and Meindl, P. (2004), Supply Chain Management, Strategy Planning and Operation,
Pearson Education, New York, NY, pp. 4-26 and 35-36.
Christopher, M. (1998), Logistics & Supply Chain Management: Strategies for Reducing Cost and
Improving Services, 2nd ed., Financial Time Prentice-Hall, New York, NY.
Christopher, M. (2004), “Identifying and managing supply chain vulnerability”, available at:
www.iolt.org.uk
Christopher, M. and Peck, H. (2003), Marketing Logistics, 2nd ed., Butterworth-Heinemann,
Oxford, p. UK.
Christopher, M. and Rutherford, C. (2004), “Creating supply chain resilience through agile six
sigma”, Critical Eye, June-August, pp. 24-8.
Christopher, M. et al. (2003a), Understanding Supply Chain Risk: A Self-Assessment Workbook,
Department for Transport-Cranfield University – Cranfield University.
Christopher, M. et al., (2003b), “Supply chain resilience”, Cranfield Centre for Logistics & Supply
Chain Management, November, appendix 1.
COSO (2004), “Enterprise risk management framework”, Committee of Sponsoring
Organizations of the Treadway Commission, available at: www.erm.coso.org.
Cox, R. and Alderson, W. (1950), Theory in Marketing, Irwin, New York, NY.
Davenport, T.H. (1993), Process Innovation – Reengineering Work through Information
Technology, Harvard Business School Press, Boston, MA.
De Loach, J.W. (2000), “Enterprise-wide risk management: strategies for linking risk and
opportunity”, London Financial Times.
Demchak, C. (1996), “Tailored precision armies in fully networked battlespace: high reliability
organisational dilemmas in the ‘Information age’”, Journal of Contingencies and Crisis
Management, Vol. 4 No. 2, pp. 93-103.
Dickinson, G. (2001), “Enterprise risk management: its origin and conceptual foundation”, The
Geneva Papers on Risk and Insurance, Vol. 26 No. 3, pp. 360-6.
Eckes, G. (2001), Six Sigma Revolution, Wiley, New York, NY, pp. 29-41.
Fariborz, Y.P., Burton, J. and Banerjee, A. (1989), “Application of analytic hierarchy process in
operations management”, International Journal of Production Management, Vol. 10 No. 3,
pp. 5-19.
Finch, P. (2004), “Supply chain risk management”, Supply Chain Management: An International
Journal, Vol. 9 No. 2, pp. 183-96.
Goodwin, P. and Wright, G. (1998), Decision Analysis for Management Judgment, Wiley, New
York, NY.
Gregory, I.C. and Rawling, S.B. (2003), Profit from Time: Speed up Business Improvement by
Implementing Time Compression, Palgrave, New York, NY.
Halman, J.M. and Keizer, J.A. (1994), “Diagnosing risks in product-innovation project”,
International Journal of Project Management, Vol. 12 No. 2, pp. 75-81.
IJLM
17,1
134
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Harland, C., Brenchley, R. and Walker, H. (2003), “Risk in supply networks”, Journal of
Purchasing & Supply Management, Vol. 9 No. 2, pp. 51-62.
Ju
¨
ttner, U., Peck, H. and Christopher, M. (2003), “Supply chain risk management: outlining and
agenda for future research”, International Journal of Logistics: Research and Applications,
Vol. 6 No. 4, pp. 197-210.
Kirk, D.L. (1999), “Enforcement time nears for German Law”, Business Insurance, Vol. 33 No. 36,
pp. 19-20.
Lam, J. (2003), Enterprise Risk Management, Wiley, New York, NY, pp. 43-65.
Lambert, D.M. (2001) in Brewer, A.M. (Ed.), The Supply Chain Management and Logistics
Controversy, Pergamon, New York, NY, pp. 99-126.
Mentzer, J.T. et al. (2001), Supply Chain Management, Sage, Thousand Oaks, CA.
Meulbroek, L. (2002), “The promise and challenge of integrated risk management”, Risk
Management and Insurance Review, Vol. 5 No. 1.
Miccolis, J. and Shah, S. (2000), “Enterprise risk management”, Towers Perrin, www.tillinghast.
com, pp. 34-6.
Miccolis, J. and Shah, S. (2001), “Modeling the reality of risk: the cornerstone of enterprise risk
management”, Expert Commentary, available at: www.irmi.com/Expert/Articles/2001.
Millet, I. and Ewdley, W.C. (2002), “Modelling risk and uncertainty with the analytic hierarchy
process”, Journal of Multicriteria Decision Analysis, Vol. 11 No. 2, pp. 97-107.
Mitroff, I. and Anagnos, G. (2001), Managing Crises Before they Happen, Amacom, New York,
NY.
Moore, K.G. (2002), “Six sigma: driving supply at Ford”, Supply Chain Management Review,
July/August, pp. 38-43.
Myers, K.N. (1999), Manager’s Guide to Contingency Planning for Disasters, Wiley, New York,
NY.
Neely, A., Adams, C. and Kennerley, M. (2002), The Performance Prism, FT Prentice Hall, New
York, NY, pp. 301-2.
O’Rourke, M. (2004), “Protecting your reputation”, Risk Management, April.
Ogrizek, M. and Guillery, J.M. (1999), Communicating in Crisis, Aldine De Gruyter, Paris.
Otto, A. (2003), “Supply chain event management: three perspectives”, International Journal of
Logistics Management, Vol. 14 No. 2, pp. 1-15.
Perret, F.L. and Jaffeux, C. (2002), The Essentials of Logistics Management, EPFL, Paris, pp. 17-49.
Ramgopal, M. (2003), “Project uncertainty management”, Cost Engineering, Vol. 45 No. 12,
pp. 21-4.
Rowbottom, U. (2004), “Managing risk in global supply chains”, Supply Chain Practice, Vol. 6
No. 2, pp. 16-23.
Saaty, T.L. (1990), The Analytic Hierarchy Process, RWS Publications, Shallowater, TX.
Saaty, T.L. (1994), “How to make a decision: the analytic hierarchy process”, Interfaces, Vol. 24
No. 9, pp. 19-43.
Signori, P. (2001), “SCIMAM (Supply Chain Integrated Management Analysis Method): la
valutazione delle potenzialita
`
d’integrazione logistica di una catena estesa di fornitura”,
Sinergie, No. 56, pp. 37-65.
Sinha, P.R., Whitman, L.E. and Malzahn, D. (2004), “Methodology to mitigate supplier risk in an
aerospace supply chain”, Supply Chain management: An International Journal, Vol. 9 No. 2,
pp. 154-68.
Managing risks
in the supply
chain
135
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
Slack, N., Chambers, S. and Johnston, R. (2004), Operations Management, Prentice-Hall, New
York, NY.
Spekman, R.E. and Davis, E.W. (2004), “Risky business: expanding the discussion on risk and the
extended enterprise”, International Journal of Physical Distribution & Logistics
Management, Vol. 34 No. 5, pp. 414-33.
Srivastava, R.K., Shervani, T.A. and Fahey, L. (1998), “Market-based assets and shareholder
value: a framework for analysis”, Journal of Marketing, Vol. 62.
Stock, J.R. and Lambert, D.M. (2001), Strategic Logistics Management, 4th ed., McGraw-Hill,
New York, NY.
Svensson, G. (2002), “A conceptual framework of vulnerability in firm’s inbound and outbound”,
International Journal of Physical Distribution & Logistics Management, Vol. 32 No. 2,
pp. 110-34.
Towill, D.R. (1996), “Time compression and supply chain management – a guided tour”, Supply
Chain Management, Vol. 1 No. 1, pp. 15-27.
Towill, D.R. and Christopher, M. (2003), “The supply chain strategy conundrum: to be lean or
agile or to be lean and agile”, Supply Chain Practice, Vol. 5 No. 2, pp. 30-44.
Van Hoek, R. (2003), “Are you ready? Risk readiness tactics for the supply chain”, Logistics
Research Network, Institute of Logistics and Transport, London.
Waters, D. (2003), Logistics. An Introduction to Supply Chain Management, Palgrave, New York,
NY, pp. 66-70 and 225-350.
Young, P.C. and Tippins, S.C. (2002), Managing Business Risk, Amacom, New York, NY.
Zahedi, F. (1986), “The analytic hierarchy process – a survey of the method and its applications”,
Interfaces, Vol. 16 No. 4, pp. 96-108.
Zsidisin, G.A. (2003), “Managerial perceptions of supply risk”, Journal of Supply Chain
Management, Vol. 39 No. 1, pp. 14-26.
Zsidisin, G.A. (2004), “An analysis of supply risk assessment techniques”, International Journal
of Physical Distribution & Logistics Management, Vol. 34 No. 5, pp. 397-413.
About the authors
Barbara Gaudenzi is Assistant Professor of Economics and Enterprise Management at the
University of Verona, Italy. Barbara Gaudenzi is the corresponding author and can be contacted
at: barbara.gaudenzi@univr.it
Antonio Borghesi is Professor of Economics and Enterprise Management at the University of
Vernona, Italy. E-mail: antonio.borghesi@univr.it
IJLM
17,1
136
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
This article has been cited by:
1. Kunal K. Ganguly, Prabir Bandyopdhyay. 2016. Supply Risk Management Process:. International Journal
of Risk and Contingency Management 3:10.4018/IJRCM.20141001, 17-31. [CrossRef]
2. V.G. Venkatesh, Snehal Rathi, Sriyans Patwa. 2015. Analysis on supply chain risks in Indian apparel retail
chains and proposal of risk prioritization model using Interpretive structural modeling. Journal of Retailing
and Consumer Services 26, 153-167. [CrossRef]
3. William Ho, Tian Zheng, Hakan Yildiz, Srinivas Talluri. 2015. Supply chain risk management: a literature
review. International Journal of Production Research 53, 5031-5069. [CrossRef]
4. Reza Hassanzadeh, Zorica Nedovic-Budic. 2015. Where to go first: prioritization of damaged areas
for allocation of Urban Search and Rescue (USAR) operations (PI-USAR model). Geomatics, Natural
Hazards and Risk 1-30. [CrossRef]
5. Md. Maruf Hossan Chowdhury, Mohammed A. Quaddus. 2015. A multiple objective optimization
based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of
garment industry of Bangladesh☆,☆☆☆This manuscript was processed by Associate Editor B. Lev. Omega
. [CrossRef]
6. Hsin-Pin Fu, Tien-Hsiang Chang, Arthur Lin, Zi-Jun Du, Kuei-Ying Hsu. 2015. Key factors for the
adoption of RFID in the logistics industry in Taiwan. The International Journal of Logistics Management
26:1, 61-81. [Abstract] [Full Text] [PDF]
7. Desheng Wu, David L. Olson, Alexandre Dolgui. 2015. Decision making in enterprise risk management:
A review and introduction to special issue. Omega . [CrossRef]
8. Seyed Hamed MoosaviRad, Sami Kara, Suphunnika Ibbotson. 2014. Impact of international outsourcing
on the value adding of industries. The International Journal of Logistics Management 25:3, 463-486.
[Abstract] [Full Text] [PDF]
9. Kristian Rotaru, Carla Wilkin, Andrzej Ceglowski. 2014. Analysis of SCOR’s approach to supply chain risk
management. International Journal of Operations & Production Management 34:10, 1246-1268. [Abstract]
[Full Text] [PDF]
10. Maria Fischl, Maike Scherrer-Rathje, Thomas Friedli. 2014. Digging deeper into supply risk: a systematic
literature review on price risks. Supply Chain Management: An International Journal 19:5/6, 480-503.
[Abstract] [Full Text] [PDF]
11. Alireza Shokri, David Oglethorpe, Farhad Nabhani. 2014. Evaluating Six Sigma methodology to improve
logistical measures of food distribution SMEs. Journal of Manufacturing Technology Management 25:7,
998-1027. [Abstract] [Full Text] [PDF]
12. David L. Olson, Scott R. Swenseth. 2014. Trade-offs in Supply Chain System Risk Mitigation. Systems
Research and Behavioral Science 31:10.1002/sres.v31.4, 565-579. [CrossRef]
13. Archie Lockamy III. 2014. Assessing disaster risks in supply chains. Industrial Management & Data
Systems 114:5, 755-777. [Abstract] [Full Text] [PDF]
14. Hamed Soleimani, Mirmehdi Seyyed-Esfahani, Govindan Kannan. 2014. Incorporating risk measures in
closed-loop supply chain network design. International Journal of Production Research 52, 1843-1867.
[CrossRef]
15. Kunal Ganguly. 2013. A case study approach for understanding inbound supply risk assessment.
DECISION 40, 85-97. [CrossRef]
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
16. Beauchamp-Akatova Elena, Curran Richard. 2013. From initial risk assessments to system risk
management. Journal of Modelling in Management 8:3, 262-289. [Abstract] [Full Text] [PDF]
17. Susana G. Azevedo, Kannan Govindan, Helena Carvalho, V. Cruz-Machado. 2013. Ecosilient Index to
assess the greenness and resilience of the upstream automotive supply chain. Journal of Cleaner Production
56, 131-146. [CrossRef]
18. Arijit Bhattacharya, John Geraghty, Paul Young, P.J. Byrne. 2013. Design of a resilient shock absorber
for disrupted supply chain networks: a shock-dampening fortification framework for mitigating excursion
events. Production Planning & Control 24, 721-742. [CrossRef]
19. Taih-Cherng Lirn, Rong-Der Wong. 2013. Determinants of grain shippers’ and importers’ freight
transport choice behaviour. Production Planning & Control 24, 575-588. [CrossRef]
20. Rabia Arikan, Metin Dağdeviren, Mustafa Kurt. 2013. A Fuzzy Multi-Attribute Decision Making Model
for Strategic Risk Assessment. International Journal of Computational Intelligence Systems 6, 487-502.
[CrossRef]
21. Avinash Samvedi, Vipul Jain, Felix T.S. Chan. 2013. Quantifying risks in a supply chain through
integration of fuzzy AHP and fuzzy TOPSIS. International Journal of Production Research 51, 2433-2442.
[CrossRef]
22. Gunjan Soni, Rambabu Kodali. 2013. A decision framework for assessment of risk associated with global
supply chain. Journal of Modelling in Management 8:1, 25-53. [Abstract] [Full Text] [PDF]
23. Kunal K. Ganguly, Kalyan K. Guin. 2013. A fuzzy AHP approach for inbound supply risk assessment.
Benchmarking: An International Journal 20:1, 129-146. [Abstract] [Full Text] [PDF]
24. Abhijeet Ghadge, Samir Dani, Roy Kalawsky. 2012. Supply chain risk management: present and future
scope. The International Journal of Logistics Management 23:3, 313-339. [Abstract] [Full Text] [PDF]
25. 2012. A Comprehensive Risk Assessment and Evaluation Model:. Transportation Journal 51, 428-451.
[CrossRef]
26. Berman Kayis, Putu Dana Karningsih. 2012. SCRIS. Journal of Manufacturing Technology Management
23:7, 834-852. [Abstract] [Full Text] [PDF]
27. Anna Corinna Cagliano, Alberto De Marco, Sabrina Grimaldi, Carlo Rafele. 2012. An integrated approach
to supply chain risk analysis. Journal of Risk Research 15, 817-840. [CrossRef]
28. Nachiappan Subramanian, Ramakrishnan Ramanathan. 2012. A review of applications of Analytic
Hierarchy Process in operations management. International Journal of Production Economics 138, 215-241.
[CrossRef]
29. Seung-Ok Yoon, Chang-Ho Lee. 2012. A Study on the Conceptual model of Enterprise Risk Management
System. Journal of the Korea Safety Management and Science 14, 113-121. [CrossRef]
30. Archie Lockamy III, Kevin McCormack. 2012. Modeling supplier risks using Bayesian networks.
Industrial Management & Data Systems 112:2, 313-333. [Abstract] [Full Text] [PDF]
31. Guozhong Zheng, Neng Zhu, Zhe Tian, Ying Chen, Binhui Sun. 2012. Application of a trapezoidal fuzzy
AHP method for work safety evaluation and early warning rating of hot and humid environments. Safety
Science 50, 228-239. [CrossRef]
32. Xiaojun Wang, Hing Kai Chan, Rachel W.Y. Yee, Ivan Diaz-Rainey. 2012. A two-stage fuzzy-AHP model
for risk assessment of implementing green initiatives in the fashion supply chain. International Journal of
Production Economics 135, 595-606. [CrossRef]
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
33. ManMohan S. Sodhi, Byung-Gak Son, Christopher S. Tang. 2012. Researchers' Perspectives on Supply
Chain Risk Management. Production and Operations Management 21:10.1111/poms.2011.21.issue-1,
1-13. [CrossRef]
34. Kristian Rotaru, Carla Wilkin, Leonid Churilov, Dina Neiger, Andrzej Ceglowski. 2011. Formalizing
process-based risk with Value-Focused Process Engineering. Information Systems and e-Business
Management 9, 447-474. [CrossRef]
35. David L. Olson, Desheng Wu. 2011. Risk management models for supply chain: a scenario analysis of
outsourcing to China. Supply Chain Management: An International Journal 16:6, 401-408. [Abstract]
[Full Text] [PDF]
36. Brad Hittle, Karen Moustafa Leonard. 2011. Decision making in advance of a supply chain crisis.
Management Decision 49:7, 1182-1193. [Abstract] [Full Text] [PDF]
37. Archie Lockamy III. 2011. Benchmarking supplier risks using Bayesian networks. Benchmarking: An
International Journal 18:3, 409-427. [Abstract] [Full Text] [PDF]
38. Chiara Verbano, Karen Venturini. 2011. Development paths of risk management: approaches, methods
and fields of application. Journal of Risk Research 14, 519-550. [CrossRef]
39. SCOTT C. ELLIS, JEFF SHOCKLEY, RAYMOND M. HENRY. 2011. MAKING SENSE OF
SUPPLY DISRUPTION RISK RESEARCH: A CONCEPTUAL FRAMEWORK GROUNDED IN
ENACTMENT THEORY. Journal of Supply Chain Management 47, 65-96. [CrossRef]
40. Shouming Chen, Tao Jian, Hui Yang. 2011. A Fuzzy AHP Approach for Evaluating Customer Value of
B2C Companies. Journal of Computers 6. . [CrossRef]
41. C. Clifford Defee, Brent Williams, Wesley S. Randall, Rodney Thomas. 2010. An inventory of theory in
logistics and SCM research. The International Journal of Logistics Management 21:3, 404-489. [Abstract]
[Full Text] [PDF]
42. David L. Olson, Desheng Dash Wu. 2010. A review of enterprise risk management in supply chain.
Kybernetes 39:5, 694-706. [Abstract] [Full Text] [PDF]
43. Hans-Christian Pfohl, Holger Köhler, David Thomas. 2010. State of the art in supply chain risk
management research: empirical and conceptual findings and a roadmap for the implementation in
practice. Logistics Research 2, 33-44. [CrossRef]
44. Sandeep Tiwari, P.C. Pandey, H. Singh, P.K. Mishra. 2010. A comparative study of the performance
behaviour of adsorbent materials used for the treatment of Spent Cutting Fluids. International Journal of
Environmental Technology and Management 13, 206. [CrossRef]
45. I. Nyoman Pujawan, Laudine H. Geraldin. 2009. House of risk: a model for proactive supply chain risk
management. Business Process Management Journal 15:6, 953-967. [Abstract] [Full Text] [PDF]
46. Judith M. Whipple, M. Douglas Voss, David J. Closs. 2009. Supply chain security practices in the food
industry. International Journal of Physical Distribution & Logistics Management 39:7, 574-594. [Abstract]
[Full Text] [PDF]
47. Dong‐Young Kim, Vinod Kumar. 2009. A framework for prioritization of intellectual capital indicators
in R&D. Journal of Intellectual Capital 10:2, 277-293. [Abstract] [Full Text] [PDF]
48. Dina Neiger, Kristian Rotaru, Leonid Churilov. 2009. Supply chain risk identification with value-focused
process engineering. Journal of Operations Management 27, 154-168. [CrossRef]
49. Ram Narasimhan, Srinivas Talluri. 2009. Perspectives on risk management in supply chains. Journal of
Operations Management 27, 114-118. [CrossRef]
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
50. Sabine Matook, Rainer Lasch, Rick Tamaschke. 2009. Supplier development with benchmarking as part
of a comprehensive supplier risk management framework. International Journal of Operations & Production
Management 29:3, 241-267. [Abstract] [Full Text] [PDF]
51. Guido J.L. Micheli, Enrico Cagno, Marta Zorzini. 2008. Supply risk management vs supplier selection to
manage the supply risk in the EPC supply chain. Management Research News 31:11, 846-866. [Abstract]
[Full Text] [PDF]
52. Siddharth Varma, Subhash Wadhwa, S.G. Deshmukh. 2008. Evaluating petroleum supply chain
performance. Asia Pacific Journal of Marketing and Logistics 20:3, 343-356. [Abstract] [Full Text] [PDF]
53. Tobias Schoenherr, V.M. Rao Tummala, Thomas P. Harrison. 2008. Assessing supply chain risks with the
analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing
company. Journal of Purchasing and Supply Management 14, 100-111. [CrossRef]
54. Liz Breen. 2008. A Preliminary Examination of Risk in the Pharmaceutical Supply Chain (PSC) in the
National Health Service (NHS). Journal of Service Science and Management 01, 193-199. [CrossRef]
55. Iwan Vanany, Suhaiza Zailani, Nyoman PujawanSupply Chain Risk Management 15-32. [CrossRef]
Downloaded by UNIVERSITA DEGLI STUDI DI VERONA At 08:35 02 September 2015 (PT)
