Research noteAssessing and managing risks using the Supply.docx

Research note
Assessing and managing risks using the Supply
Chain Risk Management Process (SCRMP)
Rao Tummala
Computer Information Systems Department, College of
Business, Eastern Michigan University, Ypsilanti, Michigan,
USA, and
Tobias Schoenherr
Department of Supply Chain Management, The Eli Broad
Graduate School of Management, Michigan State University,
East Lansing,
Michigan, USA
Abstract
Purpose – The purpose of this paper is to propose a
comprehensive and coherent approach for managing risks in
supply chains.
Design/methodology/approach – Building on Tummala et al.’s
Risk Management Process (RMP), this paper develops a
structured and ready-to-use
approach for managers to assess and manage risks in supply
chains.
Findings – Supply chain risks can be managed more effectively
when applying the Supply Chain Risk Management Process
(SCRMP). The structured
approach can be divided into the phases of risk identification,
risk measurement and risk assessment; risk evaluation, and risk

mitigation and
contingency plans; and risk control and monitoring via data
management systems. Specific techniques for conducting this
process are suggested.
Originality/value – While supply chain risk management is an
emerging and important topic in our dynamic and
interconnected world, conceptual
frameworks providing a clear meaning and normative guidance
are scarce (Manuj and Mentzer, 2008). This paper presents such
a framework, offering
structure and decision support for managers.
Keywords Supply chain management, Risk management process,
Supply chain risk, Risk management
Paper type Research paper
1. Supply chain risk management
At a time when global competition is intensifying and supply
chains are becoming longer and more complex, the likelihood
of not achieving the desired supply chain (SC) performance
increases, mainly due to the risk of SC failures. It is therefore
essential that companies plan for disruptions and develop
contingency plans as they design or redesign their supply
chains. Firms need to understand supply chain
interdependencies, identify potential risk factors, their
likelihood, consequences and severities. Risk management

action plans can then be developed to preferably avoid the
identified risks, or if not possible, at least mitigate, contain
and control them. The risk involved in supply chains, as well
as the impact severity of supply chain failures, has been
demonstrated recently by the recalls and subsequent lawsuits
for toy cars (Story, 2007) and pet food (FDA, 2008). While
risk may be associated with unacceptable products delivered
from upstream, it can also involve risks associated with the
environment, such as the impact of hurricanes Katrina and
Rita (Devlin, 2005), or the current hijackings and robberies of
vessels by pirates off the coast of Somalia (Peats, 2008).
The purpose of this paper is to introduce a structured and
systematic approach to enumerate SC risks, and to assess
their severity and likelihood, so that risk mitigation plans can
be developed and implemented. As such, this paper makes an
important contribution to the area of supply chain risk
management, and highlights an approach to manage these
risks. It continues the tradition of recent academic research

and industry reports, which have stressed the importance of
supply chain risk management, as well as the development of
approaches for its management (e.g. Blos et al., 2009; Manuj
and Mentzer, 2008; Shaer and Goedhart, 2009).
Risk can be defined as a “combination of probability or
frequency of occurrence of a defined hazard and magnitude of
the occurrence” (BS 4778, 1991). Building on several authors
that have defined supply chain risk (e.g. Choi and Krause,
2006; Zsidisin et al., 2000, 2004), we conceptualize supply
chain risk as an event that adversely affects supply chain
operations and hence its desired performance measures, such
as chain-wide service levels and responsiveness, as well as
cost. Regardless of the area of interest, risk is associated with
an undesirable loss, i.e. an unwanted negative consequence,
and uncertainty. Table I presents an illustrative list of supply
The current issue and full text archive of this journal is
available at
www.emeraldinsight.com/1359-8546.htm
Supply Chain Management: An International Journal

16/6 (2011) 474–483
q Emerald Group Publishing Limited [ISSN 1359-8546]
[DOI 10.1108/13598541111171165]
The authors are grateful to Guest Editor Dr Charlene Xie and
two
anonymous reviewers for the valuable feedback and comments
received on
earlier versions of this paper.
474
chain risks, compiled from various prior studies, most notably
Chopra and Sodhi (2004) and Schoenherr et al. (2008).
Even though the assessment and management of risk in
supply chains is more of a recent phenomenon, studies exist
that explored risk management approaches from a variety of
angles (e.g. Charette, 1989; Hayes et al., 1986; Lowrance,
1976; Rowe, 1977; Starr and Whipple, 1980). Building on
these studies, Tummala et al. (1994), by following Raiffa
(1982) and Hertz and Thomas (1983), developed a
structured Risk Management Process (RMP) consisting of
the five phases risk identification, risk measurement, risk
assessment, risk evaluation, and risk control and monitoring.
This RMP framework has been successfully applied to
identify potential risk factors and to assess their likelihood of
occurrence. In addition, the seriousness of associated
consequences can be identified, and appropriate risk

mitigating strategies can be developed (Burchett and
Tummala, 1998). While the RMP has proven to be useful
when applied to such individual project decisions, for example
the risk involved in an extra high voltage transmission line
project (Tummala and Burchett, 1999), it has yet to be
applied to the much broader context of the supply chain.
Additional risk management approaches are included in the
works of, Blos et al. (2009), De Waart (2006), Kilgore (2004),
Kleindorfer and Saad (2005), Kleindorfer and Van
Wassenhove (2004), Manuj and Mentzer (2008), Sinha et al.
(2004) and Zsidisin and Ellram (2003).
However the process may look like, techniques need to be
in place for assessing the likelihood of occurrence of identified
risk factors, as well as the seriousness of associated
consequences. The present paper is based on and extends
above studies, primarily the work by Tummala and colleagues
(Tummala et al., 1994; Tummala and Mak, 2001), but also
research conducted by Ellegaard (2008), Finch (2004),
Manuj and Mentzer (2008), Schoenherr et al. (2008), and
proposes an approach consisting of a modified RMP to
identify, assess and manage supply chain risks. This modified
approach is referred to as the supply chain risk management
process (SCRMP). Techniques mentioned by Tummala and
colleagues (Tummala et al., 1994; Tummala and Mak, 2001),
as well as others, will be highlighted in subsequent sections
within the context of supply chain risk assessment. Overall,
the paper presents a conceptual framework and approach for
effective and efficient management of risks in supply chains,
and attempts to reduce to the current lack of conceptual
frameworks in SC risk management (Manuj and Mentzer,

2008). While this work is a primary extension of Tummala
and colleagues’ (Tummala et al., 1994; Tummala and Mak,
2001) RMP, its application to supply chain management and
supply chain risks is novel and provides significant insight into
the management of such risks. The paper follows the tradition
of risk management within the supply chain (e.g. Harland
et al., 2003; Hauser, 2003; Paulsson, 2004).
2. The Supply Chain Risk Management Process
(SCRMP)
The complete SCRMP is depicted in Figure 1. While the
focus of this paper is on a detailed description of the three
phases, the other components, such as drivers, risk categories,
supplier/logistics evaluation criteria and performance
measures should not be neglected. Risk identification, risk
measurement and risk assessment comprise Phase I of the
Table I Supply chain risk categories and their triggers
Risk category Risk triggers
Demand risks Order fulfillment errors
Inaccurate forecasts due to longer lead times,
product variety, swing demands, seasonality, short
life cycles, and small customer base
Information distortion due to sales promotions and
incentives, lack of SC visibility, and exaggeration of

demand during product shortage
Delay risks Excessive handling due to border crossings or
change
in transportation mode
Port capacity and congestion
Custom clearances at ports
Transportation breakdowns
Disruption risks Natural disasters
Terrorism and wars
Labor disputes
Single source of supply
Capacity and responsiveness of alternate suppliers
Inventory risks Costs of holding inventories
Demand and supply uncertainty
Rate of product obsolescence
Supplier fulfillment
Manufacturing Poor quality (ANSI or other compliance
standards)
(process) Lower process yields

breakdown risks Higher product cost
Design changes
Physical plant Lack of capacity flexibility
(capacity) risks Cost of capacity
Supply
(procurement)
Quality of service, including responsiveness and
delivery performance
risks Supplier fulfillment errors
Selection of wrong partners
High capacity utilization supply source
Inflexibility of supply source
Poor quality or process yield at supply source
Supplier bankruptcy
Rate of exchange
Percentage of a key component or raw material
procured from a single source
System risks Information infrastructure breakdowns

Lack of effective system integration or extensive
system networking
Lack of compatibility in IT platforms among SC
partners
Sovereign risks Regional instability
Communication difficulties
Government regulations
Loss of control
Intellectual property breaches
Transportation Paperwork and scheduling
risks Port strikes
Delay at ports due to port capacity
Late deliveries
Higher costs of transportation
Depends on transportation mode chosen
Assessing and managing risks using the SCRMP
Rao Tummala and Tobias Schoenherr

Volume 16 · Number 6 · 2011 · 474 – 483
475
SCRMP, which will be described in the next section. Input to
this first phase are internal and external drivers, such as those
illustrated in Figure 1.
2.1 Phase I of SCRMP
2.1.1 Risk identification
The first step of the first phase of the SCRMP is risk
identification (Figure 1). Risk identification involves a
comprehensive and structured determination of potential
SC risks associated with the given problem. Understanding
risks, related to such categories as highlighted in Table I, is
critical. These risk categories have also been included in our
overall framework (Figure 1). Rather than attempting to be
exhaustive, this list is illustrative of the multitude of risks that
may be present. Affected areas need to be clearly identified
and consequences need to be understood so that risk
mitigation strategies can be implemented. Care should be
taken since some strategies may adversely affect other risks
(Chopra and Sodhi, 2004). Understanding the variety and

interrelationships of SC risks is therefore important as well.
Such an understanding can be achieved by considering threats
and resources (Crockford, 1986). While threats refer to the
broad range of forces, which could produce adverse results,
resources refer to assets, people or earnings, which could be
affected by the threats. One can start by first enumerating all
possible threats that could produce adverse results for the
performance of the supply chain. Then, for each threat, one
needs to determine the resources of the organization that
could be affected. The following approaches can help in the
identification of potential SC risks: supply chain mapping,
checklists or checksheets, event tree analysis, fault tree
analysis, failure mode and effect analysis (FMEA) and
Ishikawa cause and effect analysis (CEA) (see Tummala et al.,
1994).
While it is beyond the scope of this paper to provide a
thorough overview of each of these suggested approaches,
they will be briefly defined and described in the following.
Illustrative references are provided to which the interested
reader is referred. First, supply chain mapping is an approach
in which the SC and its flow of goods, information and money
is visually depicted, from upstream suppliers, throughout the

focal firm, to downstream customers. A strategic supply chain
map is a tool to align supply chain strategy with corporate
strategy, and to help firms manage and modify the supply
chain (Gardner and Cooper, 2003). Once every detail of the
supply chain has been mapped, potential risks can be
identified better. Second, checklists or checksheets are
forms to record how often a failure was attributed to a
specific event. These forms are used to standardize data
collection and to create histograms (Chase et al., 2006).
Checklists could for example be used to record late deliveries
from suppliers, which can serve as information to rate their
reliability, i.e. the risk for not delivering on time. Third, event
tree or fault tree analyses are graphical representations of all
possible and subsequent outcomes triggered by an event
(Pate-Cornell, 1984), such as a supply chain failure. While
both types of trees may appear to look the same, there are
important differences, such as the presence of single or
multiple event paths in the diagram (Hollnagel, 2004). One
may for example map out the potential events and responses
that may be triggered by a supply chain failure to then plan for
alternatives. Fourth, failure mode and effect analysis (FMEA)

is a tool to identify “at the design stages potential risks during
the manufacture of a product and during its use by the end
customer” (Karim et al., 2008, p. 3,601). For an introduction
to FMEA please see McDermott et al. (1996). Before
committing to a supply chain one could conduct such an
analysis with this SC to analyze and assess what could go
wrong, as well as how severe the consequences would be. And
fifth, Ishikawa cause and effect analysis involves the
brainstorming and exploration of all possible relationships
between potential causes and failure events. Due to its
structure, CEA diagrams are also sometimes called fishbone
diagrams (Chase et al., 2006). Once a supply chain failure has
been identified, these diagrams could be used to discover the
true root cause of the incident.
2.1.2 Risk measurement
Risk measurement, the second step of the first phase
(Figure 1), involves the determination of the consequences
of all potential SC risks, together with their magnitudes of
impact. Consequences are defined as the manner in which or
the extent to which the threat manifests its effects upon the

resources (Crockford, 1986). Manifestations may include loss
of or damage to assets, loss of income, interruption of service
levels, cost overruns, schedule delays, poor process
performance, liabilities incurred, damage repair costs, or
injuries. Once a checklist, an event tree, a fault tree, an
FMEA, or even an Ishikawa CEA analysis is applied to
identify SC risks, corresponding consequences and their
severity levels can be assessed.
Risks can be classified in terms of four types of undesirable
consequences, with differing characteristics of frequency,
severity and predictability. A popular classification is provided
by Crockford (1986), who characterized consequences into
trivial, small, medium and large. As such, trivial consequences
occur with a very high frequency, have a very low severity, and
a very high predictability. Small consequences have a high
frequency, a low severity, and a reasonable predictability, with
however their occurrence being infrequent. Medium
consequences have a low frequency, a medium severity, and
also a reasonable predictability, with their occurrence being

frequent. Finally, large consequences can be characterized by
a very low frequency, a high severity, and a minimal
predictability. This framework can also be applied to our
context. “Trivial losses” are losses that are expected to occur
in any organization and can be met by normal operating
budgets (Crockford, 1986). “Small losses” may present little
problems, unless their frequency becomes so high that their
aggregate effect approaches that of a single “medium loss”.
Although not preferred, “medium losses” would not cause
the firm serious concern if they happened at regular intervals,
for then their cost could be expressed as an annual amount,
and provisions could be made. A “large loss” presents the
most serious problem. A loss of this kind happens very rarely,
but if it did occur, it could be catastrophic for the firm.
US Military Standard 882C can be used to assess
consequence severities qualitatively as described in Table II
below (Grose, 1987; Military Standard, MIL-STD-882C,
1993). This type of severity assessment is useful when
objective information is not available. Although the

descriptions of consequence severity categories in the
Military Standard are explained in terms of losses to
buildings, environment, people, illness, etc, they can be
adapted to our SC context, as illustrated in the example in
Table II in terms of delivery risk. Risk consequence indices
Volume 16 · Number 6 · 2011 · 474 – 483
476
Figure 1 Supply Chain Risk Management Process (SCRMP)
Volume 16 · Number 6 · 2011 · 474 – 483
477

can then describe the severities, with their descriptions
changed to suit a particular situation. We will use these index
numbers to derive the risk exposure values. Table II also
includes the corresponding HTP codes, which will be used in
a later section to integrate consequence severities with other
risk assessment aspects.
2.1.3 Risk assessment
Risk assessment, the third step of the first phase (Figure 1), is
synonymous with the assessment of uncertainties (Raiffa,
1982), and is concerned with the determination of the
likelihood of each risk factor. Uncertainties can be assessed by
objective information, and probability distributions for
relevant SC risks or consequences can be derived. If,
however, objective information is not available, subjective
information, beliefs and judgment can be used to approximate
distributions. Techniques such as the Delphi method or
expert focus groups can aid in the derivation of probabilities.
Other approaches include parameter estimation, five point

estimation, probability encoding, or Monte Carlo simulation
(see Tummala et al., 1994). Alternatively, probability
categories, as suggested in the US Military Standard 882C
(Grose, 1987; Military Standard, MIL-STD-882C, 1993)
can be applied (Table III). The adapted qualitative
descriptions can be changed to suit a given situation and
supply chain environment; we have adapted them in our
instance to the delivery risk example used above. The
occurrence probability of an event such as hurricane Katrina
could for example be classified as “rare” to “extremely rare”,
whereas the occurrence of a later delivery could be classified
as “often” to “infrequent”. Each risk probability category is
assigned a risk probability index, which will help in finding the
risk exposure values, as explained in a later section. Table III
also includes the corresponding HTP codes, which will be
used in a subsequent section to construct the Hazard Totem
Pole, a tool to integrate various risk characteristics.
2.2 Phase II of SCRMP
Phase II of the SCRMP includes the steps of risk evaluation

and risk mitigation and contingency plans. Both of these steps
drawn on evaluation criteria and performance measures for
suppliers and logistics, as indicated by the boxes on the right
hand side of Figure 1. While it is beyond the scope of the
present paper to discuss these criteria and measures, they are
an important input for the two steps described in the
following.
2.2.1 Risk evaluation
Risk evaluation is the first step in Phase II of the SCRMP
(Figure 1), and involves the sub-steps of risk ranking and risk
acceptance. These two sub-steps are practical particularly
when objective probability assessment is difficult or sufficient
data are not available to derive probabilities. These
components are discussed in the following.
2.2.1.1 Risk ranking. Risk ranking is based on the
determination of risk exposure values for each identified SC
risk, and is defined as
Risk Exposure Value of Risk Factor
¼ Risk Consequence Index £ Risk Probability Index
This equation uses the indices defined in Tables II-III above
(see Tummala and Mak, 2001; Ng et al., 2003). For example,
if the consequence severity of a SC risk is critical and the

corresponding probability category is often, then the risk
exposure value is 3 3 4 5 12. In this fashion we can find the
risk exposure values for each identified risk factor as
illustrated in Table IV.
For simplicity and parsimony, these risk exposure values
can be grouped into classes representing similar ranges of
exposure. For example, risks with values between 16 and 11
could be grouped in the most critical class. These could for
instance include the risk of the shipment being stolen or lost
during transfer, the risk of the only qualified supplier going
out of business, or the risk of the company’s warehouse
burning down. Risks between 10 and 6 could be categorized
in the next-most critical class. Risks in this category could
include the risk of temporary strikes at a supply chain or
logistics partner, delays at customs, or the breakdown of a
Table II Consequence severities and indexes
Consequence severity level Qualitative description
Risk Consequence
Index HTP Code
Catastrophic Plant shut down for more than a month due to lack
of components with

zero safety stock levels 4 A
Critical Slow down of process or plant shut down for one week
due to lack of
components with zero safety stock levels 3 B
Marginal Decreased service levels with depleting safety stocks
2 C
Negligible Service levels not impacted due to sufficient safety
stock levels 1 D
Table III Probability categories and indexes
Risk probability categories
Qualitative description
The identified risk factor could occur on an average of . . .
Probability Index HTP Code
Often . . . once per week 4 J
Infrequent . . . once per month 3 K
Rare . . . once per year 2 L
Extremely rare . . . once per decade 1 M

Volume 16 · Number 6 · 2011 · 474 – 483
478
machine used by a supplier to provide products to the focal
company. Risks between 5 and 1 could then be classified in
the negligible class. These risks could involve late, incomplete
or defective deliveries of suppliers that do not necessarily
threaten the operations of the focal company, due to for
example sufficient safety stock of the supplies or the non-
critical nature of the items. Alternatively, the risk exposure
values may also be used to classify risks based on an 80-20
approach (Pareto analysis), i.e. the 20 percent of the risks
could be identified that are likely responsible for 80 percent of
the supply chain failures, and then these critical risks could be
mitigated.
2.2.1.2 Risk acceptance. Once the SC risks are classified,
acceptable levels of risk must be established. This is the
second sub-step of risk evaluation in Phase II (Figure 1). The
ALARP (as low as reasonably practicable) principle can be

used to classify SC risk as unacceptable, tolerable or
acceptable (Engineering Council, 1994). Cross-functional
teams, including senior management, must be involved, and
all available relevant information should be used in
establishing these criteria. Based on these guidelines the
demarcation between acceptable and unacceptable SC risks
can be defined, as illustrated in Figure 2 (Tummala and Mak,
2001; Ng et al., 2003). As risk-exposure values increase, they
are initially at a value below some level; at this stage risks are
considered to be so small that it is not advisable to spend time
and resources for their control. An example may include late
delivery of pencils to a manufacturing facility – pencils are
not necessarily critical for the proper operation of the plant,
and therefore expending resources to reduce the risk of late
delivery from office products suppliers may not be warranted.
As risks become elevated and their risk-exposure values
increase to unacceptable levels, appropriate response actions
must be taken for their containment. Unacceptable risks
usually have adverse effects on the proper operation of the

firm and can result in the shutdown of the assembly line,
when for example deliveries from an upstream supplier are
not received. The risks for which the risk-exposure values fall
between these two levels may be considered tolerable with no
immediate action required. However, they should be
monitored continuously and further improvement should be
sought if resources are available. Continuing with the example
from above, tolerable risks could be tardy deliveries from
suppliers that do not shut down the assembly line. While
certainly not desired, these late deliveries do not interrupt the
flow of products, but the potential for doing so may be
increased. Contracts developed between customers, suppliers,
logistics providers and manufacturers may aid in the
determination of these acceptability levels. Overall, mapping
risks along their magnitudes, as illustrated in Figure 2, can
provide a useful overview of all risks involved in a particular
supply chain, and can help determine on which risk-
preventive actions should be performed. The triangular

shape of Figure 2 implies that most risks will be acceptable
and tolerable, while only few risks will be completely
unacceptable, for which therefore mitigation strategies
should definitely be developed. The next section elaborates
on this aspect.
2.2.2 Risk mitigation and contingency plans
The risk mitigation and contingency plans component, which
is the second step of Phase II (Figure 1), involves the
development of risk response action plans to contain and
control the risks (risk planning). An evaluation technique, the
hazard totem pole (HTP) analysis, already applied by
Tummala and colleagues (Tummala et al., 1994; Tummala
and Mak, 2001), can be very helpful in this regard. This
technique, described next, is repeated here to stress its
applicability also within the supply chain context. It is a useful
technique since it integrates in a coherent fashion risk aspects
discussed in prior sections, specifically risk consequence
severity and probability.
2.2.2.1 Risk planning. Once risks have been identified, their

consequence severity has been assessed, and their probability
determined, risk mitigation action plans can be developed.
Since it is not feasible and practical to develop mitigation and
prevention strategies for every risk identified, risk-planning
begins with the examination of the costs required to
implement each preventive action to contain and manage
the identified SC risks. Supply chain risks can for example be
reduced by buffer inventories, information technologies,
effective relationships with suppliers and downstream
customers, involvement of alternative or multiple suppliers,
risk pooling, and the conduct of “what if’ analyses (Choi,
2007; Choi and Krause, 2006; Chopra and Sodhi, 2004;
Cook, 2007; Mentzer et al., 2006; Stalk, 2006; Swaminathan
and Tomlin, 2007). Findings from AMR Research’s recent
supply chain risk survey indicate that closer collaboration with
trading partners, the passing of cost increases to customers,
Table IV Risk exposure values
Probability
Severity Often (Index 5 4) Infrequent (Index 5 3) Rare (Index 5

2) Extremely rare (Index 5 1)
Catastrophic (Index 5 4) 16 12 8 4
Critical (Index 5 3) 12 9 6 3
Marginal (Index 5 2) 8 6 4 2
Negligible (Index 5 1) 4 3 2 1
Figure 2 Acceptable, tolerable, and unacceptable risks
Volume 16 · Number 6 · 2011 · 474 – 483
479
the use of dual/multi-sourcing strategies and redundant
suppliers, and performance-based contracts with suppliers
and service partners are the most successful methods most
often used to mitigate risks (Tohamy, 2009). These plans are
evaluated and the best course of action is selected. A four-
level cost-category system as shown in Table V (Tummala and
Mak, 2001; Ng et al., 2003) is adopted to facilitate the
selection of the best course of action. Each category is
associated with a cost index and an HTP code. Similar as
above in Tables II-III, specific cost values provided in Table V
can be adapted to the specific supply chain context (they here
refer again to the delivery risk example introduced above),

and are provided here merely for illustrative purposes. Risk
mitigation plans can also be evaluated based on their relative
cost to each other.
2.2.2.2 Hazard Totem Pole (HTP) analysis. The hazard totem
pole analysis provides a method for the systematic evaluation
of SC risks, integrating the risk evaluation aspects of their
severity, probability and cost, as described above in Table II,
Table III and Table V, respectively. The HTP diagram is
designed to combine these three risk dimensions, which
enables the determination of a singular ranking and the
integrated depiction in a single figure. Codes and numerical
values, as introduced above in Table II, Table III and Table V,
are now integrated and used to represent different category
levels.
Based on these three coding levels of severity, probability
and cost, each risk factor is assigned a three-letter code. For
example a risk factor with a code of AJP (or 4, 4, 4) possesses
a consequence severity of “catastrophic”, a probability of
occurrence of “often”, and has an implementation cost to
contain the identified risk factor of less than $1,000. The
corresponding total HTP risk index is then determined as
12ð¼ 4 þ 4 þ 4Þ. Similarly, a risk factor with a code of BJQ
(or 3, 4, 3), having a total risk index of 10, is associated with a
“critical” consequence severity and a likelihood of occurrence
of “often”, involving costs between $1,000 and $10,000 to
implement risk reduction action plans. In this fashion
respective risk codes and risk indices can be assigned to the
identified SC risks. Risks with a higher index number,
determined based on the risk’s severity, probability and
mitigation cost, should be first in line for management
consideration.
With this input the HTP diagram can be constructed
(Figure 3). First, all risks are ordered according to their total

HTP index value from highest to lowest. Second, the
corresponding three-letter risk factor code is added to each
line, to provide more information about the particular risk.
And third, additional columns can be created that denote the
cumulative risk factor count and the cumulative risk control
cost. The pyramidal HTP diagram lists the most significant
risks at the top (sharply pointed for immediate management
attention), and the less significant risks at the bottom (Grose,
1987).
The risk factors at the top of the HTP represent
catastrophic consequences that can be eliminated or
contained for a small amount of money. As we go down the
HTP, the impact of the ranked risk factors diminishes. Since
no firm can afford to eliminate every identified risk, one can
find a level in the HTP below which management accepts the
risks, instead of implementing risk response action plans for
their removal (similar to Figure 2 above, which is a pre-
version to the fully developed HTP here). Alternatively, a firm
may have a certain budget amount available to implement
mitigation strategies. Starting from the top, the firm could
then decide to implement all risk mitigation plans until the
cumulative risk control cost equals or exceeds the budget.
This cumulative cost is the cumulative sum of the risk
prevention costs, which are based on the values in Table V.
With this approach, the most critical risks can be addressed,

while at the same time being constrained by a limited amount
of resources. As a result, risk response actions can be selected
for implementation according to the priority and the available
resources. The cumulative risk factor count at that point
indicates how many risks (irrespective of their severity,
probability and prevention cost) could be eliminated. The
HTP analysis thus represents an effective decision tool for
integrating the severity of the consequence, the probability of
occurrence, and the implementation cost of a risk response
action plan for an identified SC risk.
While the HTP analysis just described can serve as a useful
decision aid, certain limitations must be noted which relate
mostly to assumptions and the subjective nature of the
rankings and evaluations. For example, the implementation
costs for risk mitigation action plans are assumed to be fixed.
However, after the resources have been expended, the risk
may not be completely eliminated; its severity may be merely
lowered, for instance from “catastrophic” to “severe.” Here,
the budget estimated was not sufficient to completely
eliminate the risk. The risk might also emerge in a modified
form, for which the implementation action plan may be not as
effective. The HTP analysis in Figure 3 can therefore only be
a decision aid, and not a tool that makes decisions for the
supply chain manager. It must be realized that almost all

evaluations are subjective, and that assumptions made today
may not be valid tomorrow any more. Modifications to
Figure 3 may therefore be necessary. Nevertheless,
considering these caveats, the suggested approach can help
conceptualize and understand the problem in a more
structured way.
2.3 Phase III of SCRMP
In the last phase of the SCRMP, risk control and monitoring,
one can examine the progress made regarding the
implemented risk response action plans; corrective actions
can be taken if deviations occur in achieving the desired SC
performance. This is Phase III in Figure 1. The process is a
means to determine possible preventive measures and to
provide guidelines for further improvement. Deviation from
desired outcomes, abnormal cases, and SC disruptions are
reported.
Data management systems can aid in this task, for example
by the following modular structure: a catalog of the identified
SC risk factors, consequence severity levels, risk probabilities,
hazard totem pole analysis, government regulations/policies,
Table V Implementation cost categories for risk-response
action-plans
Cost categories Implementation costs

Cost
Index
HTP
Code
Substantial More than $100,000 1 S
High Between $10,000 and $100,000 2 R
Low Between $1,000 and $10,000 3 Q
Trivial Less than $1,000 4 P
Volume 16 · Number 6 · 2011 · 474 – 483
480
tariffs and customs policies, transport schedules, and SC risk
triggers. Related risk information can be stored and updated
as needed. It can be used not only for effective monitoring
and the taking of corrective actions, but also for continuous

improvement of risk assessment and management. While such
a system may be sufficient, there are also a number of
sophisticated supply chain risk management software provides
who offer commercial solutions, also on a Software as a
Service (SaaS) basis, for risk management.
Based on the conduct of these three phases, a supply chain
decision can be reached. However, as is the case with so many
business processes, the exercise does not stop here.
Management must continuously reiterate the SCRMP to
account for any changes having occurred in the environment.
Risk tolerances may also change, as may prevention costs and
severity levels. Therefore, a continuous monitoring and
assessment should be practiced.
3. Conclusion
The proposed supply chain risk management process is a tool
to provide management with useful and strategic information
concerning the SC risk profiles associated with a given
situation. This is in contrast to the traditional approach based

on single point estimates. The SCRMP ensures SC managers
adopt strategic thinking and strategic decision making in
evaluating options to improve supply chain performance. The
analysis can be used not only for evaluating progress but also
for selecting alternative courses of action, based on their
respective SC risk profiles. Ultimately the SCRMP provides
insight into how to make the most appropriate decision.
The SCRMP methodology proposed here is a
comprehensive and coherent approach for managing risks
and uncertainties associated with a given problem. The
SCRMP methodology is practitioner-oriented in evaluating
projects. Supply chain managers can apply it as an audit
framework, in much the same way as the ISO 9000 quality
system, in coping with risks and uncertainties, as well as in
accomplishing the desired supply chain performance. It is
important to recognize though that the approach cannot be
applied blindly. As noted above, the SCRMP is a suggested
aid that can help in making decisions, however, it does not
make the decisions for the supply chain manager. It can

merely serve as a tool to help in decision making. It is then
always the intuitive judgment, tacit knowledge, and the
unique situation that come into play and that must be
considered.
From an academic research perspective, the paper
contributes a conceptual risk assessment framework. As was
noted in Manuj and Mentzer (2008, p. 133), “there is a lack
of conceptual frameworks and empirical findings to provide
clear meaning and normative guidance on the phenomenon of
global supply chain risk management.” While we have
responded to the first observation by the development of the
SCRMP, empirical testing of this model is warranted. Future
research is encouraged to test the SCRMP at a range of
company and to report the findings. Based on the results, the
SCRMP can be refined and modified. Furthermore, different
versions of the SCRMP can be developed depending on the
company’s context and environment, for example of whether
sourcing is done domestically or internationally. Insightful will

then also be the classification of companies into risk profile
groups, based on their application of the SCRMP. What
makes some companies more or less risk averse than others,
and what is the subsequent impact on performance? These
are just some of the questions pressing for answers.
In addition, while the focus of this paper was on a detailed
description of the three phases, the other components of
Figure 1, such as drivers, risk categories, supplier/logistics
evaluation criteria and performance measures should not be
neglected. These issues can impact the level or risk
significantly. Future research is encouraged to investigate
these components in greater detail, and integrate them with
the SCRMP. The cohesive framework presented herein
provides structure and guidance for such further
investigations of supply chain risk management. As such,
Figure 1 stakes out the research landscape of supply chain risk
management. More fine-grained research looking at the
individual phases of the SCRMP is also needed. Right now,
evaluations are based on subjective judgments, and inherently

include some error. Therefore, more quantitative approaches
of risk management are called for. Sensitivity analyses could
for example be conducted by simulating a range of feasible
values and investigating their impact on both cost and risk.
Going even a step deeper, future research should investigate
how data available on company internal systems can be
leveraged to determine these values. Based on the results, an
optimal solution could then ideally be determined.
Figure 3 Hazard Totem Pole (HTP)
Volume 16 · Number 6 · 2011 · 474 – 483
481
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 Chain Management: An International
Journal, Vol. 14 No. 4, pp. 247-52.
BS 4778 (1991), Quality Vocabulary, British Standards
Institute.
Burchett, J.F. and Tummala, V.M.R. (1998), “An application
of the risk management process (RMP) in capital
investment decisions for an EHV transmission line
construction project”, Construction Management and
Economics, Vol. 16 No. 2, pp. 235-44.
Charette, R.N. (1989), Software Engineering Risk Analysis and
Management, McGraw-Hill, New York, NY.
Chase, R.B., Jacobs, F.R. and Aquilano, N.J. (2006),
Operations Management for Competitive Advantage,
McGraw-Hill Irwin, New York, NY.
Choi, T.Y. (2007), “Supplier-supplier relationships: why they
matter”, Supply Chain Management Review, Vol. 11 No. 5,
pp. 51-6.
Choi, T.Y. and Krause, D.R. (2006), “The supply base and its
complexity: implications for transaction costs, risks,
responsiveness, and innovation”, Journal of Operations

Management, Vol. 24 No. 5, pp. 637-52.
Chopra, S. and Sodhi, M.S. (2004), “Managing risk to avoid
supply-chain breakdown”, Sloan Management Review,
Vol. 46 No. 1, pp. 53-61.
Cook, T.A. (2007), Global Sourcing Logistics: How to Manage
Risk and Gain Competitive Advantage in a Worldwide Market
Place, AMACOM, American Management Association,
New York, NY.
Crockford, N. (1986), An Introduction to Risk Management,
2nd ed., Woodhead-Faulkner.
De Waart, D. (2006), “Getting smart about risk
management”, Supply Chain Management Review, Vol. 10
No. 8, pp. 27-34.
Devlin, M. (2005), “Functional matters: Hurricane Katrina
and the supply chain”, ThomasNet, Industrial Market
Trends, available at: http://news.thomasnet.com/IMT/ar
chives/2005/09/functional_matt.html (accessed July 6,
2009).
Ellegaard, C. (2008), “Supply risk management in a small
company perspective”, Supply Chain Management:
An International Journal, Vol. 13 No. 6, pp. 425-34.

Engineering Council (1994), Guidelines and Risk Issues,
Lloyd’s Register, London.
FDA (2008), “Pet foods recall (melamine)/tainted animal
feed”, US Food and Drug Administration, updated
February 6, 2008, available at: www.fda.gov/oc/opacom/ho
ttopics/petfood.html (accessed December 8, 2008).
Finch, P. (2004), “Supply chain risk management”, Supply
Chain Management: An International Journal, Vol. 9 No. 2,
pp. 183-96.
Gardner, J.T. and Cooper, M.C. (2003), “Strategic supply
chain mapping approaches”, Journal of Business Logistics,
Vol. 24 No. 2, pp. 37-64.
Grose, V.L. (1987), Managing Risk: Systematic Loss Prevention
for Executives, Prentice-Hall, Englewood Cliffs, NJ.
Harland, C., Brenchley, R. and Walker, H. (2003), “Risk in
supply networks”, Journal of Purchasing and Supply
Hauser, L.M. (2003), “Risk-adjusted supply chain
management”, Supply Chain Management Review, Vol. 7
No. 6, pp. 64-71.
Hayes, R.W., Perry, J.G., Nompson, P.A. and Willmer, G.
(1986), Risk Management in Engineering Construction,
Implications for Project Managers, Thomas Telford,

Westminster, London.
Hertz, D.B. and Thomas, H. (1983), Risk Analysis and Its
Applications, John Wiley & Sons, Chichester.
Hollnagel, E. (2004), Barriers and Accident Prevention,
Ashgate Publishing, Farnham.
Karim, M.A., Smith, A.J.R. and Halgamuge, S. (2008),
“Empirical relationships between some manufacturing
practices and performance”, International Journal of
Production Research, Vol. 46 No. 13, pp. 3583-613.
Kilgore, J.M. (2004), “Mitigating supply chain risks”, 89th
Annual International Supply Management Conference,
April 2004.
Kleindorfer, P.R. and Saad, G.H. (2005), “Managing
disruption risks supply chains”, Production and Operations
Kleindorfer, P.R. and Van Wassenhove, L.K. (2004), “Risk
management for global supply chains: an overview”,
in Gatignan, H. and Kimberly, J. (Eds), The Alliances on
Globalizing, Cambridge University Press, Cambridge, MA,
Ch. 12.
Lowrance, W.W. (1976), Of Acceptable Risk, Science and the
Determination of Safety, William Kaufmann, Los Altos, CA.
McDermott, R.E., Mikulak, R.J. and Beauregard, M.R.
(1996), The Basics of FMEA, Productivity Inc, Portland,
OR.
Manuj, I. and Mentzer, J.T. (2008), “Global supply chain risk
management”, Journal of Business Logistics, Vol. 29 No. 1,

pp. 133-55.
Mentzer, J.T., Myers, M.B. and Stank, T.P. (2006), Handbook
of Global Supply Chain Management, Sage Publications,
Thousand Oaks, CA.
Military Standard, MIL-STD-882C (1993), System Hazard
Analysis, System Safety Program Requirements, United States
Department of Defense, January 1993, pp. A4-A6.
Ng, M.F., Tummala, V.M.R. and Yam, C.Y. (2003), “A risk
based maintenance management model for toll road/tunnel
operations”, Construction Management and Economics,
Vol. 21 No. 5, pp. 495-510.
Pate-Cornell, M.E. (1984), “Fault tree vs event trees in
reliability analysis”, Risk Analysis, Vol. 4 No. 3, pp. 177-86.
Paulsson, U. (2004), “Supply chain risk management”,
in Brindley, C. (Ed.), Supply Chain Risk, Ashgate
Publishing, Aldershot.
Peats, B. (2008), “How to stop the pirates?”, New Statesman,
December 5, 2008, available at: www.newstatesman.com/a
frica/2008/12/merchant-ships-pirates-piracy (accessed
July 6, 2009).
Raiffa, H. (1982), “Science and policy: their separation and
integration in risk analysis”, The American Statistician,
Vol. 36 Nos 3, Part 2, pp. 225-37.
Rowe, W.D. (1977), An Anatomy of Risk, John Wiley & Sons,
New York, NY.
Schoenherr, T., Tummala, V.M.R. and Harrison, T. (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, Vol. 14 No. 2,
pp. 100-11.
Volume 16 · Number 6 · 2011 · 474 – 483
482
Shaer, S. and Goedhart, J. (2009), “Risk and the consolidated
supply chain: rethinking established best practices”, APICS
Magazine, July/August, pp. 41-3.
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.
Stalk, G. Jr (2006), “Surviving the China riptide”, Supply
Chain Management Review, Vol. 10 No. 4, pp. 19-26.
Starr, C. and Whipple, C. (1980), “Risks of risk decisions”,
Science, Vol. 208 No. 6, pp. 1114-9.
Story, L. (2007), “Lead paint prompts Mattel to recall
967,000 toys”, The New York Times, August 2, 2007,
available at: www.nytimes.com/2007/08/02/business/02toy.
html (accessed December 8, 2008).

Swaminathan, J.M. and Tomlin, B. (2007), “How to avoid
the risk management pitfalls”, Supply Chain Management
Review, Vol. 11 No. 5, pp. 34-42.
Tohamy, N. (2009), “Can Indian parlay its IT services
success into manufacturing outsourcing?”, Supply Chain
Technologies and Services, AMR Research, Boston, MA.
Tummala, V.M.R. and Burchett, J.F. (1999), “Applying a risk
management process to manage cost risk for an EHV
transmission line project”, International Journal of Project
Tummala, V.M.R. and Mak, C.L. (2001), “A risk
management model for improving operation and
maintenance activities in electricity transmission
networks”, Journal of the Operational Research Society,
Vol. 52 No. 2, pp. 125-34.
Tummala, V.M.R., Nkasu, M.M. and Chuah, K.B. (1994),
“A framework for project risk management”, ME Research
Bulletin, Vol. 2, pp. 145-71.
Zsidisin, G.A. and Ellram, L.M. (2003), “An agency theory
investigation of supply risk management”, The Journal of
Supply Chain Management, Vol. 39 No. 3, pp. 15-27.
Zsidisin, G.A., Panelli, A. and Upton, R. (2000), “Purchasing
organization involvement in risk assessments, contingency
plans, and risk management: an exploratory study”, Supply
Chain Management: An International Journal, Vol. 5 No. 4,
pp. 187-97.
Zsidisin, G.A., Ellram, L.M., Carter, J.R. and Cavinato, J.L.
(2004), “An analysis of supply risk assessment techniques”,

International Journal of Physical Distribution & Logistics
Further reading
Tummala, V.M.R. and Lo, C.K. (2004), “Risk management
model for improving electricity supply reliability”,
International Journal of Business & Economics, Vol. 3 No. 1,
pp. 43-55.
About the authors
Rao Tummala is Professor of Operations and Supply Chain
Management in the College of Business, Eastern Michigan
University, Ypsilanti, MI, USA. Professor Tummala is widely
recognized for his scholarly contributions in Project Risk
Management, Quality Management, Supply Chain
Management, Bayesian Decision Theory, and Analytic
Hierarchy Process. Some of the journals in which he has
published papers include Supply Chain Management – An
International Journal, Quality Management Journal, OMEGA –
The International Journal of Management Science, Journal of
Operational Research Society, The Journal of Supply Chain

Management, International Journal of Project Management,
Construction Management and Economics and PRACTIX.
Tobias Schoenherr is Assistant Professor of Supply Chain
Management at the Eli Broad Graduate School of
Management at Michigan State Michigan University, East
Lansing, MI, USA. He holds a PhD in Operations
Management and Decision Sciences from Indiana
University, Bloomington. Dr Schoenherr’s research focuses
on strategic supply chain management, including strategic
sourcing, (global) operations strategy, use of technology in
SCM, and outsourcing. His work has appeared or is
forthcoming in the Journal of Operations Management,
Production and Operations Management, Management Science,
the Journal of Supply Chain Management, the International
Journal of Production Research, the International Journal of
Operations and Production Management, OMEGA – The
Inter national Journal of Management Science, Business
Horizons, the Journal of Purchasing and Supply Management,

and others. For recent publications, please visit: http://broad.
msu.edu/supplychain/faculty/member?id ¼ 748. Tobias
Schoenherr is the corresponding author and can be
contacted at: [email protected]
Volume 16 · Number 6 · 2011 · 474 – 483
483
To purchase reprints of this article please e-mail:
[email protected]
Or visit our web site for further details:
www.emeraldinsight.com/reprints
Reproduced with permission of the copyright owner. Further
reproduction prohibited without permission.
Supply Chain Integration and the SCOR Model
Honggeng Zhou
1
, W. C. Benton, Jr.

2
, David A. Schilling
2
, and Glenn W. Milligan
2
1
University of New Hampshire
2
The Ohio State University
The Supply Chain Operations Reference (SCOR) model has
been widely adopted in many companies. Anecdotal evidence
and tradejournals have reported significant improvements after
firms have adopted the SCOR model. Although practitioners
have been
enthusiastic about implementing and using the SCOR model in
their operations, the SCOR model has not been empirically
validated.
The purpose of this study is to empirically validate the SCOR
model (i.e., test the structure of the SCOR model). Data from
125 North
American manufacturing firms were collected. The results show
that the relationships among the supply chain processes in the
SCOR
model are generally supported. The Plan process has significant
positive influence on the Source, Make, and Deliver processes.
The
Source process has significant positive influence on the Make
process and the Make process has significant positive influence
on the
Deliver process. The Source process mediates the impact of the
Plan process on the Make process and the Make process

mediates the
impact of the Plan process on the Deliver process. The findings
provide managers with empirical evidence that the SCOR model
is in
fact valid.
Keywords: Supply Chain Operations Reference (SCOR) model;
supply chain management; business strategy
INTRODUCTION
The Supply Chain Operations Reference (SCOR) model was
developed by the Supply Chain Council in 1996. The SCOR
model focuses on the supply chain management function
from an operational process perspective and includes cus-
tomer interactions, physical transactions, and market interac-
tions. In the past decade, the SCOR model has been widely
adopted by many companies including Intel, General Electric
(GE), Airbus, DuPont, and IBM. According to the Supply
Chain Council’s (2010) website, ‘‘While remarkably simple, it
[the SCOR model] has proven to be a powerful and robust
tool set for describing, analyzing, and improving the supply
chain.’’ In the literature, several recent studies have reviewed
the SCOR model (Huang et al. 2004, 2005). Many other
studies (McCormack 1998; Lockamy and McCormack 2004;
Supply Chain Council 2010) have attempted to measure the
SCOR model’s impact on business performance. Trade jour-
nals have also reported the benefits of using SCOR model
(Davies 2004; Malin 2006).
To date, the SCOR model has been used by companies
throughout the world. Intel is one of the first major U.S.
corporations to adopt the SCOR model (Supply Chain
Council 2010). In 1999, Intel started its first SCOR project
for its Resellers Product Division. Later, they expanded the
SCOR model implementation to the Systems Manufacturing

Division. Several other SCOR projects were conducted
afterward. The benefits of implementing the SCOR model
included faster cycle times, less inventories, improved visibil-
ity of the supply chain, and access to important customer
information in a timely fashion. GE implemented the SCOR
model in its Transportation Systems unit, which reported
sales of $2.6 billion in year 2001. The use of the SCOR
model streamlined the purchasing process with its suppliers,
which led to shorter purchasing cycle time and lower cost.
Davies (2004) report that since 1999 Philips Lighting has
used the SCOR model in its overall business framework,
which directly resulted in improved customer service and
reduced inventories. In Europe, Degussa (a German chemical
company) used the SCOR model to streamline its newly
merged businesses. Specifically, Degussa set up a team of
cross-functional employees to implement the SCOR project.
After a three-week pilot project, the SCOR team found
opportunities in the existing supply chain processes. It was
reported that the SCOR project was expected to save the
firm millions of euros.
The SCOR model is used not only in manufacturing oper-
ations, but also in service operations. As Malin (2006)
reports, a New York hospital used the SCOR model to
define, measure, and improve supply chains. The first phase
of the project led to 2% reduction in overall drug inventory
the first year. The hospital reported an 8–10% reduction in
excess and obsolete inventory during the next two years.
Meanwhile, the improved visibility and planning generated
21% capacity increase and an 8% increase in demand. The
prep times for key procedures were reduced by as much as
40%, which resulted in reduced labor costs.
Although the SCOR model has been widely practiced by
many companies in different processes of supply chains and

anecdotal evidences have shown the value of adopting the
SCOR model, no large-scale empirical research has been
conducted to systematically examine the relationships among
the supply chain processes as suggested by the SCOR model.
Thus, the purpose of this study is to empirically validate the
SCOR model (i.e., to confirm the structure of the SCOR
model).
The results of this study show that the relationships
among the supply chain processes in the SCOR model are
generally supported. The Plan process has significant positive
Corresponding author:
W. C. Benton, Jr., Department of Management Sciences, Fisher
College of Business, The Ohio State University, 2100 Neil Ave-
nue, Columbus, OH 43210, USA; E-mail: [email protected]
Journal of Business Logistics, 2011, 32(4): 332–344
� Council of Supply Chain Management Professionals
influence on the Source, Make, and Deliver processes. The
Source process has significant positive influence on the Make
process and the Make process has significant positive influ-
ence on the Deliver process. Among the four supply chain
processes, the Plan process has received the least attention
from the implementation firms. The findings from this study
provide practitioners statistical confidence in the implementa-
tion and use of the SCOR model.
In the next section, literature review and research hypothe-
ses will be presented. The theoretical underpinnings for the
research hypotheses are also discussed in the second section.
In the third section, the research methodology and measure-
ment scale development are presented. In the fourth section,
the analysis results are given. The research findings and man-

agerial implications are discussed in the fifth section. Finally,
concluding comments and future research directions are pre-
sented in the concluding section.
LITERATURE REVIEW AND RESEARCH
HYPOTHESES
In this section, we review the literature of the SCOR model.
Based on the literature review, the research hypotheses are
proposed. The literature review provides the theoretical foun-
dation for this research. The theoretical foundation is
reflected in the literature taxonomy given in Table 1.
As the SCOR model is the main framework in this study,
a brief introduction of the SCOR model is necessary. The
SCOR model diagram is given in Figure 1. Level 1 consists
of five supply chain processes: Plan, Source, Make, Deliver,
and Return. As the Return process was not in the first four
versions of the SCOR model and is not as mature as the
other four processes, this study focuses on the other four
processes (Plan, Source, Make, and Deliver), which have
been widely adopted by practitioners. Level 2 of the SCOR
model describes core processes. Level 3 of the SCOR model
specifies the best practices of each process. According to the
definition in the SCOR model, Plan includes the processes
that balance aggregate demand and supply to develop a
course of action which best meets sourcing, production, and
delivery requirements. Source includes the processes that pro-
cure goods and services to meet planned or actual demand.
Make is comprised of the processes that transform product
to a finished state to meet planned or actual demand. Deliv-
ery includes all processes which provide finished goods and
services to meet planned or actual demand (Supply Chain
Council 2010). The following subsections review the litera-
ture of the four processes and develop the research hypothe-

ses.
Plan (planning)
Supply chain planning process uses information from exter-
nal and internal operations to balance aggregate demand
and supply. The SCOR model suggests that the capability to
run ‘‘simulated’’ full stream supply ⁄ demand balancing for
‘‘what–if’’ scenarios is important for supply chain planning.
‘‘What–if’’ analysis helps firms to perform sensitivity analysis
for various possible scenarios. Another important ability is
to get real-time information and rebalance supply chains
using updated information. Information sharing in supply
chains can lead to improved performance (Fawcett et al.
2011). According to Narasimhan and Kim (2001), the use of
information systems can improve supply chain integration.
From the process perspective, it is important to have a desig-
Table 1: Literature review taxonomy
Authors
Supply chain practice
Plan Source Make Deliver
Ahmad and Schroeder (2001) *
Benton and Shin (1998) *
Blackburn (1991) *
Chen and Paulraj (2004) *
Carr and Pearson (1999) *
Choi and Hartley (1996) *
Cua et al. (2001) *
Dong and Xu (2002) * *
Ferrari (2001) * *

Flynn et al. (1999) *
Fullerton and McWatters (2001) *
Fullerton et al. (2003) *
Garcia et al. (2004) * *
Giffi et al. (1990) * *
Goldsby and Stank (2000) *
Gurin (2000) *
Ha et al. (2003) *
Hahn et al. (1983) *
Hausman et al. (2002) * *
Hayes and Wheelwright (1984) * *
Henig and Levin (1992) * *
Hill (1994) * * *
Hines (1996) * *
Kaynak and Hartley (2008) *
Lee et al. (1997) * *
Li et al. (2005) *
Lockamy and McCormack (2004) * * *
MacDuffie et al. (1996) *
Makatsoris and Chang (2004) * *
McKone and Schroeder (2001) *
Nakajima (1988) *
Nair (2006) *
Pande et al. (2000) *
Powell (1995) *
Prahinski and Benton (2004) *
Rungtusanatham et al. (1997) *
Samson and Terziovski (1999) *
Schonberger (1990) *
Shah and Ward (2003) *
Shah and Ward (2007) *
Stalk et al. (1992) * *
Supply Chain Council (2010) * * *
Wemmerlov and Hyer (1989) *
Womack et al. (1990) *

Supply Chain Integration and SCOR Model 333
nated supply chain planning team. Womack et al. (1990) find
that one primary reason that Japanese automobile firms have
an advantage over traditional U.S. automobile firms is that
they used designated planning teams to coordinate different
functions. Furthermore, the literature suggests that interfunc-
tional coordination within a firm is critical for supply chain
planning because the alignment between the functions is nec-
essary to achieve a firm’s strategic goals (Supply Chain
Council 2010). For example, many studies (Hill 1994; Haus-
man et al. 2002) have found the importance of aligning mar-
keting and manufacturing operations to improve
performance.
Source (buyer–supplier relationship)
Sourcing practice connects manufacturers with suppliers and
is critical for manufacturing firms. The academic literature
and the SCOR model have identified several sourcing
practices as best practices (Carr and Pearson 1999; Chen and
Paulraj 2004; Prahinski and Benton 2004; Li et al. 2005;
Benton 2010). Establishing long-term supplier–buyer rela-
tionship and reducing the supplier base are good sourcing
practices. The role of key suppliers in a supply chain should
be assured through long-term relationship (Treleven 1987;
Benton 2010). Hahn et al. (1983) show that companies’ bene-
fits gained by giving larger volume of business to fewer sup-
pliers using long-term contracts outweigh the costs. Just-in-
time (JIT) delivery from suppliers is also considered a good
sourcing practice. The benefits of JIT delivery have been
widely documented (Benton and Shin 1998; Ahmad and Sch-
roeder 2001; Dong et al., 2001). Furthermore, providing

feedback about suppliers’ performance evaluations is a good
sourcing practice. According to Carr and Pearson (1999),
supplier evaluation systems have a direct positive impact on
buyer–supplier relationship, and an indirect impact on firm
financial performance. More recently, Prahinski and Benton
(2004) studied the role of communication in supply chain
management. They found that executives at buying firms
need to incorporate indirect influence strategy, formality,
and feedback into supplier development programs.
Make (transformation process)
The Make process includes the practices that efficiently
transform raw materials into finished goods to meet supply
chain demand in a timely manner. Both academic literature
Return
Level
Descrip on Schematic Comments
Top Level
(Process
Types)
Level 1 defines the scope and content
for the Supply Chain Operations
Reference-model. Here basis of
competition performance targets are
set.
Source MakeDeliver
Plan
1

#
Configuration
Level (Process
Categories)
A company’s supply chain can be
“configured-to-order” at Level 2 from
core “process categories.” Companies
implement their operations strategy
through the configuration they choose
for their supply chain.
2
Process
Element Level
(Decompose
Processes)
Level 3 defines a company’s ability to
compete successfully in its chosen
markets, and consists of:
• Process element definitions
• Process element information
inputs, and outputs
• Process performance metrics
• Best practices, where
applicable
3

P1.1
Identify, Prioritize, and
Aggregate Supply-Chain
Requirements
P1.2
Identify, Assess, and
Aggregate Supply-Chain
Requirements
P1.3
Balance Production
Resources with Supply-
Chain Requirements
P1.4
Establish and
Communicate
Supply-Chain Plans
Companies implement specific
supply-chain management practices at
this level. Level 4 defines practices to
achieve competitive advantage and to
adapt to changing business conditions.
Implementation
Level (Decompose
Process Elements)
4

Not
in
Scope
Su
p
p
ly
-C
h
ai
n
O
p
er
a
o
n
s
R
ef
er
en
ce
-m
o
d
el
Return

Figure 1: Supply Chain Operations Reference (SCOR) model.
334 H. Zhou et al.
(Shah and Ward 2007; Benton 2011b) and the SCOR model
include four groups of practices for the Make process: JIT
production, total preventive maintenance (TPM), total qual-
ity management (TQM), and human resource management
(HRM). JIT production includes several practices: pull sys-
tem, cellular manufacturing, cycle time reduction, agile man-
ufacturing strategy, and bottleneck removal (Wemmerlov
and Hyer 1989; Blackburn 1991; Powell 1995; MacDuffie
et al. 1996; Benton and Shin 1998; Flynn et al. 1999; Fuller-
ton and McWatters 2001; Fullerton et al. 2003; Benton
2011a). The review of quality management literature has led
to the identification of good quality management practices:
TQM, statistical process control (SPC), continuous improve-
ment program, and six-sigma techniques (Benton 1991; Pow-
ell 1995; Rungtusanatham et al. 1997; Pande et al. 2000; Cua
et al. 2001; Nair 2006; Kaynak and Hartley 2008). TPM is a
manufacturing program that primarily maximizes equipment
effectiveness throughout its entire life (Nakajima 1988; Cua
et al. 2001). Several studies have explored the good practices
of TPM and their positive relationship with business perfor-
mance (Cua et al. 2001). The literature review led to the
identification of the following effective TPM practices: pre-
ventive maintenance; safety improvement program; planning
and scheduling strategies; and maintenance optimization.
The HRM practices emphasize employee team work and
workforce capabilities. Employee team work is important for
improving production, because frontline employees working
as a team can leverage the experience of all employees and
greatly contribute to process and product improvement
(Hayes and Wheelwright 1984). Workforce capability is

another important measurement for workforce management
(Giffi et al. 1990; Schonberger 1990).
Deliver (outbound logistics)
The extant literature and anecdotal evidence show that deliv-
ery has become a critical link in supply chain management
(Gurin 2000; Ha et al. 2003). Goldsby and Stank (2000)
review the world class logistics competencies and capabilities.
One capability is sharing real-time information with supply
chain partners, which increases the real-time visibility of
order tracking. Agility is also an important competence of
world class logistics. Gurin (2000) describes how Ford part-
nered with the United Parcel Service to develop and imple-
ment an Internet-based delivery process, significantly
improving Ford’s delivery performance. An Internet-based
delivery system can significantly enhance the real-time order
tracking capability. Other best delivery practices identified
by the SCOR model include a single contact point for all
order inquiries, order consolidation, and the use of auto-
matic identification. The bar code technology significantly
improves the relationship between suppliers and buyers and
allows some emerging inventory management programs such
as vendor-managed inventory program. Ahmad and Schroe-
der (2001) identify several factors that affect delivery perfor-
mance. The factors include JIT management, quality
management, production instability, and so on. However,
Ahmad and Schroeder (2001) do not use a scale to measure
the good practices in delivery process.
Relationships of the four supply chain processes in the SCOR
model
Both the SCOR model and the literature suggest the relation-
ship among the four supply chain processes as illustrated in

Figure 2. First, effective supply chain planning practices are
expected to influence the implementation of effective sourc-
ing, production, and delivery practices (Lockamy and Mc-
Cormack 2004). The planning process is expected to balance
the aggregate supply chain demand and supply. The ability
to balance demand and supply in real time can enhance a
long-term relationship with suppliers who can better respond
to the demand ⁄ supply changes (Ferrari 2001). It also sup-
ports the implementation of an effective production system,
which includes practices such as JIT, TPM, TQM, and
HRM. For example, without a good planning process, a JIT
production would be impossible. The interfunctional coordi-
nation such as the alignment between marketing and manu-
facturing is important for an effective JIT production.
Effective supply chain planning also drives effective delivery
process. To respond to customer demand changes quickly,
firms need the ability to track the order delivery status in real
time (Makatsoris and Chang 2004). Based on the SCOR
model and the literature, the hypotheses are proposed as fol-
lows.
H1: Plan process positively influences Source process.
H2: Plan process positively influences Make process.
H3: Plan process positively influences Deliver process.
Second, sourcing process positively influences the use of
Make process (St. John and Young 1991; Hines 1996; Ben-
ton 2010). A good long-term relationship with suppliers can
help firms implement JIT production. Without a good JIT
delivery from suppliers, a JIT production system would be
Plan
Source
Make

Deliver
H1
H2
H3
H4
H5
Figure 2: Supply Chain Operations Reference (SCOR)
model.
Source: Supply Chain Operations Reference Model, Supply
Chain Council (2010).
impossible. A good relationship with suppliers also helps
control the quality of the inputs, which helps the use of
TQM program. For example, a major automobile manufac-
turer does not examine the quality of some incoming compo-
nents, because it has a good relationship with its suppliers
and has enough confidence on its supplier’s quality. Finally,
a good delivery from suppliers allows manufacturers to sche-
dule preventive maintenance in an effective way. Therefore,
the following hypothesis is proposed.
H4: Source process positively influences Make process.
Third, the Make process positively influences the delivery

process (Henig and Levin 1992; Garcia et al. 2004). A good
JIT production system produces products in a timely manner
according to customer needs, which is essential to the
implantation of JIT delivery. A good TQM program and
knowledgeable employees are also necessary to facilitate the
use of JIT delivery. In addition, an effective production sys-
tem can help increase the visibility of order tracking
throughout the whole supply chain system. Therefore, the
following hypothesis is proposed.
H5: Make process positively influences Deliver process.
Although H1–H5 are directly from the SCOR model, the
empirical validation of the SCOR model contributes to the
academic literature and provides value to the practitioners.
Taken together, H1, H2, and H4 suggest that Source process
mediates the influence of Plan process on the Make process.
The mediation effect suggests that the Plan process drives
better Make process at least partially because good supply
chain planning practices have positive influence on sourcing
practices. Similarly, H2, H3, and H5 together suggest that
Make process mediates the influence of Plan process on the
Deliver process. Thus, this study will use Sobel tests to
directly examine these two mediation effects.
H6: The influence of Plan process on Make process is medi-
ated by Source process.
H7: The influence of Plan process on Deliver process is
mediated by Make process.
RESEARCH METHOD
Sample
The research objectives were achieved by obtaining responses

from manufacturing professionals holding senior-level posi-
tions. Contact information for qualified informants was iden-
tified with the assistance of the Supply Chain Council (2010).
The surveyed firms include Xerox Corp., Dow Corning
Corp., Owens Corning, Nachi Robotic Systems, Windsor
Mold Inc., and Minntech Corporation. The respondents
were senior executives and held titles such as CEO, Presi-
dent, Vice President, and Director. The average number of
employees in the respondents’ firms was about 5,000. Eight
companies had more than 10,000 employees. The median
annual sales value, as reported by the respondents, was
between $100 million and $500 million. Five companies had
annual sales of more than $5 billion. Four academic experts
and three industry experts were asked to review the survey
instrument (questionnaire) to ensure the relevance and clarity
of the survey instrument. The industry experts who reviewed
the questionnaire also provided insights as to the type of job
titles that may reflect probable knowledge of the SCOR
model. Utilizing this guidance, the sample was selected based
upon job titles and job descriptions available. Employing the
multiple contact strategy as suggested by Dillman (2007), a
total of 745 manufacturing professionals were invited to par-
ticipate in the study.
Four contacts were made with the selected informants.
The purpose of the initial postcard contact was to verify the
accuracy of the mailing address and make the selected
respondents aware of the forthcoming questionnaire. Two
weeks after the initial postcard was mailed, the first round
survey packages were mailed. According to Dillman (2007),
at least two weeks are needed between contacts to allow
enough time for the postcards with wrong addresses to be
returned to us. The survey packages contained three items:
the personalized letter of introduction about the importance
of the study, an eight-page booklet of the survey question-

naire, and a prepaid business reply envelope. The third con-
tact, mailed one week after the first round survey packages,
were reminder postcards. The postcards were used to thank
those who had returned the questionnaire and remind those
who had not returned the questionnaire. Two weeks after
sending the reminder postcards, the second round question-
naires were mailed to the informants who had not replied.
As before, the survey package included: a personalized letter,
the questionnaire, and the prepaid business reply envelope.
Two weeks after the second round questionnaires were
mailed, those companies who had not replied were contacted
by telephone. Several insights were gained from the success-
ful telephone conversation. First, respondents in many of the
companies, the informant forwarded the questionnaires to
others within the company to complete. However, if the
respondent who received the questionnaire could not respond
to certain questions, the respondent would most likely for-
ward the questionnaire to another person who can answer
the questionnaire. It is expected that if the questionnaire was
forwarded, the return rate is greatly reduced. This process
also resulted in significantly longer cycle times (Dillman
2007). Second, many respondents who were interested in the
study could not locate the questionnaire that was sent to
them. Thus, a replacement survey package was sent to them.
Third, we found that it is important to have direct contact
with the executives who had the authority to decide whether
to participate in the study. Finally, many companies could
not participate in the study because of company policies.
Measurement scales
The survey questions and the descriptive statistics for each
measurement scale are in Table 2. The Make process has
four indicators (JIT, TQM, TPM, and HRM). This section
336 H. Zhou et al.

first describes the multiple criteria that are used to validate
the measurement scales. Then, the final results of the scale
analysis are presented.
Scale validity and reliability
The measurement scale development process supports the
validity and reliability of the measurement scales. First,
exploratory factor analysis was performed. Then, confirma-
tory factor analysis (CFA) was performed. The content
validity of the scales was established by the literature. In
addition, both academicians and practicing managers
assessed the survey questionnaire content validity before the
surveys were distributed. Construct validity ensures that the
conceptual constructs are operationalized in the appropriate
way. To ensure construct validity, exploratory factor analysis
with principal component method is used. According to Hair
et al. (1998) and Carmines and Zeller (1979), the factor load-
ings need to be at least .3. Only one factor in each construct
can have an eigenvalue that is larger than 1.00 and the vari-
ance explained by the first factor in each construct is at least
40%. Reliability is defined as the extent to which the mea-
sures can yield same results on other replication studies. The
internal consistency measured by Cronbach’s alpha is used
to measure the construct reliability in this study. The lower
Table 2: Survey questions and descriptive statistics
Survey question Mean SD
To what extent have the following planning practices been
implemented in your company
[1 = not implemented, 7 = extensively implemented]

Plan1. ‘‘What–if’’ analysis has been implemented for supply ⁄
demand balancing 3.41 1.98
Plan2. A change in the demand information instantaneously
‘‘reconfigures’’ the
production and supply plans
3.21 2.18
Plan3. Online visibility of supply chain demand requirements
3.35 2.05
Plan4. The designation of a supply chain planning team 3.65
2.15
Plan5. Both marketing and manufacturing functions are
involved in supply chain
planning process
3.70 2.08
To what extent have the following sourcing practices been
Source1. Long-term relationships with strategic suppliers 5.51
1.52
Source2. Reduction in the number of suppliers 4.69 1.87
Source3. Just-in-time delivery from suppliers 4.29 1.92
Source4. Frequent measurement of suppliers’ performance 4.75
1.83
Source5. Frequent performance feedback to suppliers 4.44 1.94
To what extent have the following production practices been
JIT1. Pull system 3.97 2.11
JIT2. Cellular manufacturing 3.42 2.25
JIT3. Cycle time reduction 4.40 1.96
JIT4. Agile manufacturing strategy 3.10 2.04

JIT5. Bottleneck ⁄ constraint removal 4.02 1.83
TPM1. Preventive maintenance 4.98 1.75
TPM2. Maintenance optimization 4.08 2.00
TPM3. Safety improvement programs 5.57 1.65
TPM4. Planning and scheduling strategies 5.02 1.50
TQM1. Total quality management 4.88 1.84
TQM2. Statistical process control 4.19 2.16
TQM3. Formal continuous improvement program 4.75 2.06
TQM4. Six-sigma techniques 3.36 2.20
HRM1. Self-directed work teams 3.69 1.93
HRM2. We use knowledge, skill, and capabilities as criteria to
select employees 5.14 1.60
HRM3. Direct labor technical capabilities are acknowledged
4.67 1.72
HRM4. Employee cross-training program 4.76 1.51
To what extent have the following delivery practices been
practiced in your company
[1 = not practiced, 7 = extensively practiced]
Deliver1. We have a single point of contact for all order
inquiries 5.12 1.82
Deliver2. We have real-time visibilities of order tracking 4.41
2.17
Deliver3. We consolidate orders by customers, sources, carriers,
etc. 4.59 2.03
Deliver4. We use automatic identification during the delivery
process to track order status 3.26 2.19
limit of .7 is considered acceptable (Nunnally and Bernstein
1994; Hair et al. 1998). The results in Table 3 show that all
factor loadings meet the criterion of larger than .3. The fac-

tor analysis results from Table 3 also show that all con-
structs satisfy the unidimensionality requirement. For all
scales except Deliver process, only one eigenvalue is larger
Table 3: Final results of measurement validation
Scale name Variable name Factor loading Scale statistics
Plan Plan1 .75 Cronbach’s alpha: .80
Largest eigenvalue (variance explained): 2.80 (56%)
Second largest eigenvalue (variance explained): .77 (15%)
Average variance extracted: .46
Reliability, q: .81
Average variance shared, c2: .34
Plan2 .72
Plan3 .74
Plan4 .80
Plan5 .75
Source Source1 .59 Cronbach’s alpha: .76
Reliability, q: .78
Source2 .58
Source3 .66
Source4 .87
Source5 .87
Make
JIT JIT1 .57 Cronbach’s alpha: .82

JIT2 .79
JIT3 .86
JIT4 .77
JIT5 .84
TPM TPM1 .90 Cronbach’s alpha: .89
TPM2 .79
TPM3 .83
TPM4 .77
TQM TQM1 .79 Cronbach’s alpha: .86
TQM2 .85
TQM3 .89
TQM4 .84
HRM HRM1 .68 Cronbach’s alpha: .77
HRM2 .78
HRM3 .88
HRM4 .75
Deliver Deliver1 .68 Cronbach’s alpha: .73
Second largest eigenvalue (variance explained): 1.01 (25%)
Reliability, q: .86

Deliver2 .83
Deliver3 .78
Deliver4 .68
Make JIT .79 Cronbach’s alpha: .86
Reliability, q: .74
TPM .87
TQM .87
HRM .82
Degree of freedom 130
Chi-squared statistics 267
Normed chi-square 2.06
Nonnormed fit index (NNFI) .91
Comparative fit index (CFI) .93
Incremental fit index (IFI) .93
Root mean square error of approximation (RMSEA) .09
All loadings significant at p < .05
338 H. Zhou et al.
than 1.00 and the variance explained by the largest eigen-
value is larger than 40%. For the Deliver process, the second
largest eigenvalue is slightly larger than 1.00. The scree test
suggests that one factor is the most appropriate for this set
of items. Thus, the Deliver process is determined to be unidi-
mensional. For the reliability, Table 3 shows that all scales

have Cronbach’s alpha values of .7 or higher. Thus, it is con-
cluded that all measurement scales are reliable.
After performing the exploratory factor analysis, CFA
was performed to confirm the measurement model of the
structural equation model. As Table 3 shows, reliability rho
scores for all constructs exceed the threshold of .7 (Fornell
and Larcker 1981). For each construct, the average shared
variance is smaller than the average variance extracted.
Moreover, the overall CFA model statistics (comparative fit
index [CFI] = .93, incremental fit index [IFI] = .93, non-
normed fit index [NNFI] = .91, and root mean square error
of approximation [RMSEA] = .09) suggest that the pro-
posed construct structure has a reasonably good fit. It is to
be noted that JIT, TPM, TQM, and HRM do not have the
three CFA-related measures (i.e., average variance extracted,
shared variance, and reliability rho) because they are the
measurement items for the latent variable Make in the CFA
model. For example, JIT value in the CFA model is the
average of the five JIT items (i.e., JIT1, JIT2, JIT3, JIT4,
and JIT5) in Table 3.
As we used a single informant to answer all questions,
potential common method bias is checked. The items com-
prising the scales of planning, sourcing, JIT, TPM, TQM,
HRM, and delivery were not highly similar in content. The
respondents are familiar with the constructs. Harman’s one-
factor test of common method bias (Podsakoff and Organ
1986; Podsakoff et al. 2003; Hochwarter et al. 2004) found
several distinct factors for the variables, which suggested that
common method variance bias was not a problem.
Summary of research methodology
This study used a survey research method. The analysis was
based on 125 useable responses from U.S. manufacturing

firms. The survey followed the standard process suggested by
Dillman (2007) to ensure that a good and representative
sample was obtained. After the sample was obtained, the sta-
tistical analysis has been performed to ensure that the mea-
surement scales are valid and reliable before the
measurement scales have been used in further statistical anal-
ysis such as structural equation model. Other measurement
concerns such as common method bias have been addressed
in this research methodology stage.
ANALYSIS RESULTS
Descriptive statistics
The descriptive statistics in Table 2 show that the mean of
the supply chain planning and JIT practices are relatively
low compared with the practices of the Source, TPM, TQM,
HRM, and Deliver processes. The means of the planning
and JIT practices are 3.46 and 3.78, respectively, while the
means of the Source, TPM, TQM, HRM, and Deliver prac-
tices are 4.74, 4.91, 4.30, 4.57, and 4.34, respectively. For the
five planning practices, all of them are below 4.00. In con-
trast to that, all five sourcing practices have scores above
4.00. In the Make process, it is quite surprising to see that
the mean of the pull system, cellular manufacturing, agile
manufacturing strategy, six-sigma techniques, and self-direc-
ted work teams are below 4.00, since the lean manufacturing
has been introduced to North America for more than
20 years and many studies have reported extensive imple-
mentation of lean practices in North American firms (Powell
1995; Flynn et al. 1999; Shah and Ward 2003). It seems that
the firms are doing well in the TPM area and most aspects
of TQM and HRM. The factor analysis for the four indica-
tors (JIT, TPM, TQM, and HRM) of the Make process sup-
ports the idea of lean manufacturing bundles in Shah and

Ward (2003). Regarding the delivery process, the firms are
doing well on all practices except automatic identification. In
sum, the descriptive statistics suggest that firms are doing
well overall in sourcing, delivery, TPM, TQM, and HRM,
the means of which are above 4.00. But the firms are not
doing as well on supply chain planning and JIT production,
the means of which are below 4.00.
Structural equation model
We use the structural equation model method to test the
hypotheses H1–H5 about the relationships among the four
supply chain processes and the results are shown in Figure 3.
The results are summarized in Tables 3 and 4. Then we use
Sobel tests to test the two mediation effects hypothesized in
H6 and H7. The results are shown in Table 5.
Before running the structural equation model, the score
for JIT, TPQ, TQM, and HRM were calculated according to
the average of the items with related factor. Therefore, JIT,
TQM, TPM, and HRM are considered as indicators for
Make construct. A number of fit statistics were used to eval-
uate the models because no single measure was adequate
(Bollen and Long 1993). A normed chi-square below one
indicates that the model is overfitted (Joreskog 1969), while
a value larger than 3.0 (Carmines and McIver 1981) to 5.0
(Wheaton et al. 1977) indicates that a model does not ade-
quately fit the data. The normed chi-square adjusts the sam-
ple discrepancy function by the degree of freedom. Hair
et al. (1998) provide guidelines for interpreting the RMSEA
Table 4: Results of hypotheses tests
Path in the structural
model

Path coefficient
estimate (t-value) Outcome
Plan fi Source (H1) .46* (3.27) Supported
Plan fi Make (H2) .31* (3.35) Supported
Plan fi Deliver (H3) .44* (3.13) Supported
Source fi Make (H4) .63* (3.71) Supported
Make fi Deliver (H5) .38* (2.80) Supported
Note:
*
Significant at p < .05.
as follows: RMSEA < .05, good model fit; .05 <
RMSEA < .10, reasonable model fit; RMSEA > .10, poor
model fit. Hair et al. (1998) also suggest that the model fit is
good if NNFI and CFI are above .9. Both NNFI and CFI
adjust the sample discrepancy function by the degree of free-
dom. The IFI is similar to NFI but it has a correction in the
denominator to decrease the sample size effect (Bollen 1989).
It is desirable to have IFI no less than .9. As shown in the
bottom of Table 3, the fit indices of our model were:
v2 = 267 with df = 130 (i.e., the normed chi-square is 2.06),
NNFI = .91, CFI = .93, IFI = .93, and RMSEA = .09.
All fit statistics fell in the desirable ranges and suggested that
the model had a reasonably good fit. Based on the structural
equation model, the results of the five hypotheses are shown
in Figure 3 and Table 4. According to the t-values in
Table 4, all five hypotheses were supported at the .05 signifi-
cance level. In addition to a good fit of the structural model,

a good structural equation model needs to have a good mea-
surement model (i.e., the path coefficients of all indicators to
the related latent variables are significant at the .05 level).
According to the SEM results, all path coefficients are signif-
icant at the .05 level and the t-values are larger than 2.0.
Mediation effect
To test the two mediation effects, the Sobel tests are used.
For each mediation test, three regressions are required. Take
the mediation effect of Source process as an example (see
Table 5). First, Plan process must have significant influence
on Make process. Second, Plan process must have significant
influence on Source process. Third, the influence of Plan pro-
cess on Make process must change significantly when Source
process is entered into the regression model. Then a Sobel
test is performed to test the significance of the mediation
effect (Venkatraman 1989).
Model 1 in Table 5 shows that the Plan process has a sig-
nificant influence on Make process. The regression coefficient
is .405, which is significant at the 5% level. Model 2 shows
that the Plan process has a significant influence on the
Source process. The coefficient is .392, which is significant at
the 5% level. Model 3 shows that the coefficient of the Plan
process on the Make process is reduced to .212 when Source
process is entered into regression together with the Plan pro-
cess. To test whether this reduction is significant, a Sobel test
is performed. The calculation of the Sobel test statistics is
shown in Table 5. The result shows that the Sobel test statis-
tic is 4.5. The p-value of this Sobel test is smaller than .05.
This means that the Source process significantly mediates the
influence of the Plan process on the Make process. Similar
regression analysis is performed for the mediation effect of
the Make process. The results are summarized in Table 5.

The Sobel test statistic is 3.5. The p-value of this Sobel test
is smaller than .05 as well. Thus, we conclude that the Make
process significantly mediates the influence of the Plan process
on the Deliver process.
Summary of analysis
This analysis section first provides the descriptive statistics of
all measurement items, which gives the readers an overall
picture of the data set. Using the measurement scales vali-
dated in the third section, the structural equation modeling
analysis tests the relationships among the four processes in
Plan
Source
Make
Deliver
H1: γ1=.46*
H2: γ2=.31*
H3: γ3=.44*
H4: β1=.63*
H5: β2=.38*
Note: * Indicates significance at p < .05
Figure 3: Supply Chain Operations Reference (SCOR) model
with results.

Table 5: Mediation test for Source and Make processes
Tests for Source process Tests for Make process
Variable Plan Source Variable Plan Make
Model 1 (dependent variable: Make) .405* (.062) Model 1
(dependent variable: Deliver) .504* (.075)
Model 2 (dependent variable: Source) .392* (.067) Model 2
(dependent variable: Make) .405* (.062)
Model 3 (dependent variable: Make) .212* (.059) .493* (.071)
Model 3 (dependent variable: Deliver) .334* (.103) .419* (.103)
Sobel test statistics is: .493 · .392 ⁄ sqrt (.4932 · .0672 + .3922 ·
.071
2
) = 4.5
Sobel test statistics is: .419 · .405 ⁄ sqrt (.4192 · .0622 + .4052 ·
.103
2
) = 3.5
Notes: The numbers within parentheses are the standard errors
of the coefficients.
*Significant at p < .05.
340 H. Zhou et al.
the SCOR model. The statistics in Tables 3 and 4 generally
support the relationships proposed in the SCOR model.

Finally, regression analysis is used to test the mediation role
of the Make process and the Source process in the SCOR
model.
RESULTS AND DISCUSSION
This study marks the first empirical study that tests the valid-
ity of the relationships among the supply chain processes in
the SCOR model. According to the results in Figure 3 and
Table 4, the relationships of the supply chain processes in
the SCOR model are supported as expected (Supply Chain
Council 2010). The Plan process has significant positive influ-
ence on Source, Make, and Deliver processes. Source process
has significant positive influence on Make process while
Make process has significant positive influence on Deliver
process. The strongest link is from the Source process to the
Make process while the weakest link is from the Plan process
to the Make process.
The relatively weak link from the Plan process to the
Make process reveals some issues in the SCOR model. While
the Make process in the SCOR model does include the
HRM and TPM practices, the Plan process of the SCOR
model does not cover the planning about HRM and TPM
(Supply Chain Council 2010). The Plan process primarily
focuses on sourcing, JIT production, and delivery practices.
In the future, the SCOR model might need to include the
planning activities for HRM (leadership) and TPM to keep
the SCOR model consistent with itself.
The results in Table 5 support the hypotheses that (1)
Source process mediates the influence of Plan process on
Make process, and (2) Make process mediates the influence
of Plan process on Deliver process. The significant mediation
effect suggests that an effective Source process plays a critical
role in the relationship between Plan process and Make pro-

cess and an effective Make process plays a critical role in the
relationship between Plan and Deliver processes. According
to Table 5, the indirect influence that Plan process has on
the Make process through the Source process is
.392 · .493 = .193 (.392 from Model 2 and .493 from Model
3). The direct influence that Plan process has on the Make
process is .212 (from Model 3). The total influence (direct
influence + indirect influence) that Plan process has on the
Make process is .193 + .212 = .405. Table 5 shows that
about 34% (1 ) .334 ⁄ .504 = .34) of the total influence that
Plan process has on the Deliver process is the indirect influ-
ence through the Make process when Make process is
entered into the regression.
To our best knowledge, this is the first study that
empirically tests the relationships among all four supply
chain processes in the SCOR model. Very few studies
(Lockamy and McCormack 2004; Huang et al. 2005) con-
ceptually discussed the SCOR model. To date, this is the
only study that has comprehensively addressed the rela-
tionships among all four supply chain processes. This
study contributes to the literature by providing a holistic
view of the supply chain management from the process
perspective and offers an integrative analysis of the supply
chain processes.
For practitioners, the findings provide rigorous empirical
evidence in support of the SCOR model. The finding gives
practitioners statistical confidence in the implementation and
use of the SCOR model. For example, this study reveals the
firms’ insufficiency in the supply chain planning practices,
although the Plan process is shown to be important for all
other three processes. This study identifies the quantitative
relationships among the four supply chain processes, which
can help firms assess their supply chain strengths and

weaknesses. The descriptive statistics can also help firms to
benchmark themselves with other firms.
CONCLUSION AND FUTURE RESEARCH
This study marks the first empirical effort to examine the
validity of the SCOR model. It has been shown that the rela-
tionships among the supply chain processes in the SCOR
model are generally supported. With data from 125 North
America manufacturing companies, the Plan process has sig-
nificant positive influence on the Source, Make, and Deliver
processes. The Source process has significant positive influ-
ence on the Make process and the Make process has signifi-
cant positive influence on the Deliver process. The Source
process mediates the impact of the Plan process on the Make
process and the Make process mediates the impact of the
Plan process on the Deliver process. Among the four supply
chain processes, it appears that the Plan process has received
the least attention from the firms so far, although it does
have significant influence on all the other three processes.
This study contributes to both academic literature and
practitioners. Several recent studies have addressed the issue
of supply chain integration and governance (Chen et al.
2009a,b; Richey et al. 2010). As Chen et al. (2009b) men-
tioned, the SCOR model is an illustration of the process
approach to supply chain integration. This study provides a
holistic view of supply chain integration from an empirical
survey research methodology perspective. It reveals the
quantitative relationships among the four components of the
SCOR model. Richey et al. (2010) suggested that the supply
chain governance which balances the self-interest and inter-
dependency in supply chains can help improve performance.
Through the Source and Deliver components of the SCOR
model, this study enhances our understanding of the impor-
tance of working with suppliers and customers in supply

Research noteAssessing and managing risks using the Supply.docx

Research noteAssessing and managing risks using the Supply.docx

Recommended

Recommended

More Related Content

Similar to Research noteAssessing and managing risks using the Supply.docx

Similar to Research noteAssessing and managing risks using the Supply.docx (20)

More from verad6

More from verad6 (20)

Recently uploaded

Recently uploaded (20)

Research noteAssessing and managing risks using the Supply.docx