Int. Journal of Business Science and Applied Management, Volume 6, Issue 3, 2011

Supply chain risk management: review, classification and

future research directions

Piyush Singhal

Dept. of Mechanical Engineering, GLA University, Mathura, India

17 Km Stone, NH-2 Mathura-Delhi Highway, P.O. Chaumuhan

Mathura-281406, UP, India

Telephone: +919412624713

Email: piyushsinghal2003@gmail.com

Gopal Agarwal

Dept. of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, India

J.L.N. Marg, Jaipur, 302017, Rajasthan, India

Telephone: +91- 141-2529087

Email: agarwal.drg@gmail.com

Murali Lal Mittal

Dept. of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, India

J.L.N. Marg, Jaipur, 302017, Rajasthan, India

Telephone: +91- 141-2529087

Email: mlmittal.mnit@gmail.com

Abstract

In order to be more efficient, firms have adopted strategies such as outsourcing, global partnerships and lean

practices. Although such strategies have tremendous abilities to improve the efficiencies but simultaneously

they make the firms vulnerable to market uncertainties, dependencies and disruptions. Moreover, natural

calamities and manmade crises have also put negative impact on strategic, operational and tactical performance

of supply chains. These factors have triggered the interest of academia and industry to consider the risk issues as

prime concerns. To capture the more fine-grained elements of diversified risk issues related to the supply chain

we employ a multi-layered top town taxonomy to classify and codify the literature and put forward the probable

dimensions for future research. We further study the pool of SCRM literature focusing on coordination, decision

making and sector-wise SCRM implementation issues and derive relevant propositions.

Keywords: supply chain risk management, risk, uncertainty, literature review

Int. Journal of Business Science and Applied Management / Business-and-Management.org

1 INTRODUCTION

Supply Chain Management (SCM) as a discipline has witnessed a tremendous growth during the last two

decades. This growth has been noticed in terms of modelling and analysing various issues arising due to the

development of complex networks amongst different organizations not only within countries but also across the

globe. These issues are mainly related to designing, planning and coordinating the material, information, and

money flows across the supply chains. But owing to increasing dynamism and uncertainty in the business

environment risk issues are becoming key concerns to the organizations. The risks in supply chains arise mainly

due to (i) operational fluctuations such as variability in supply, demand uncertainties, and price variability

(Juttner, 2005; Christopher and Lee, 2004) (ii) natural events such as earthquakes, cyclones, epidemics and (iii)

manmade crises such as terrorist attacks, unethical business practices and economic recessions (Kleindorfer and

Saad, 2005). Further cultural, infrastructural and political differences and the trend towards strategies such as

outsourcing, single-sourcing and lean practices have also made the supply chain vulnerable to risks (Juttner et

al., 2003; Varma et al., 2007; Meixell and Gargeya, 2005).

Effective management of risks is becoming the focal concern of the firms to survive and thrive in a

competitive business environment. Thus the supply chain risk management (SCRM) has emerged as a natural

extension of supply chain management with the prime objective of identifying the potential sources of risks and

suggesting suitable action plans to mitigate them. But developing an effective SCRM program is always a

critical task and requires skills and expertise in multiple areas. Considerable work has been reported in the

SCRM literature dealing with issues with qualitative and quantitative approaches. Several earlier attempts,

however, have also been made by researchers to review the dimensions of risks and their impact on supply chain

functioning. Tang (2006a) reviewed the literature dealing with quantitative models having strategies to manage

the risks at the operational and strategic level by addressing the risk issues of such functional aspects of the

supply chain as demand management, supply management and product management. Vanany et al. (2009)

studied the SCRM literature based on unit of analysis and risk management processes. Rao and Goldsy (2009)

elaborated the taxonomy of risk sources and a categorization scheme. Further to identify the key enablers and

inhibiters of risk management practices Tang and Musa (2010) employed the bibliometric method of citation

and co-citation and also assessed the potential sources of risk to enhance the understanding of the SCRM

literature. Dailun (2004) provided the basic framework of risk management but was more influenced by

financial risk management approaches. Industrial trends and practices that cause risks and business turbulence

are also considered without reviewing their empirical linkages (Narasimhan and Talliri, 2009; Trkman and

McCormak, 2009).

It is observed that the literature on SCRM is growing exponentially with diversified issues, approaches and

purposes but most of the work is still found to be isolated and appears to be fragmented. Most of the earlier

reviews found the missing elements and suggested guidelines to overcome them. However, our review differs in

purpose, as we seek to assess how well the risk spectrum is explored considering the perceptive elements of risk

definitions, categorizations, structural elements of the supply chain and implementation phases of SCRM. To

provide deeper insights we suggest a multi-layered top-down taxonomy including risk factors, elements and

attributes. We further unify the domain of the SCRM literature that consolidates and refines the available

knowledge and practices. We also develop the codification scheme (Appendix), which could help practitioners

not only to use classifications but also for retrieval of information for various quantitative and qualitative

analyses.

The remainder of the paper is organized as follows: Section2 provides a review methodology, outcomes of

preliminary investigations and a description of the taxonomy used in the study. Section3 outlines the qualitative

and quantitative analysis of the literature, employing the proposed taxonomy. Section4 represents the

managerial implications and challenges, focusing on coordination and decision making issues under business

risks and also considering SCRM implementation issues for specific sectors. Section5 includes the closing

remarks, identifies gaps in the research and proposes future research directions.

2 REVIEW METHODOLOGY AND PRELIMINARY FINDINGS

In this review, we focus on the SCRM literature and search the on-line library databases with the key

words: supply chain risk management, uncertainty, risk and business continuity (Figure 1). The search was

further narrowed down by a key focus on the papers addressing the following issues:

 Spectrum of supply chain risks with their significance

 Contribution of various research methodologies to managing the supply chain risks

 Issues primarily related to description and implementation of SCRM

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

This review includes 114 research papers taken from refereed journals published during the last fifteen

years, from 1996 to 2010. The journals included in the review: Computers and Chemical Engineering;

Computers in Industry; European Journal of Operational Research; Expert Systems With Applications;

International Journal of Agile Systems and Management; International Journal of Logistics Research and

Applications; International Journal of Risk Assessment and Management; International Journal of Physical

Distribution and Logistics Management; International Journal of Production Economics; Journal of Operations

Management; Omega (The International Journal of Management Science); Supply Chain Management: An

International Journal; The International Journal of Logistics Management; The Journal of Supply Chain

Management.

Figure1: Review methodology

2.1 Temporal trends in SCRM

In order to view the periodic growth in the area of SCRM, the papers are divided into three time blocks

each of five years duration. Figure 2 shows the number of papers in each period. Some key insights observed are

presented below in Table 1:

 The papers dealing with supply chain risk issues appear in a variety of journals of different tracks

such as management sciences/operational research, business management and systems

engineering, indicating the multidimensionality of risk issues.

 More than 70% of papers included in the review were published during the last five years,

indicating the growing importance of SCRM.

Table 1: Temporal trends of SCRM study

Period 1996-2000 2001-2005 2006 onwards

Trends in

SCRM

study

Risk definitions and

investigation for focal

firm perspectives usually

influenced by financial

risk analysis

Consideration of global risk

issues, Investigation of

operational parameters such as

inventory policies, demand and

supply, Capacity planning

Cross country relationship issues, Issues

related to information sharing and

security, Focus on brand image and

comprehensive supply chain risk

management program, Agility and

resilience issues

Sources

Literature databases

Search words

Supply chain risk management, uncertainty, risk,

business continuity

Implications and challenges

Coordination and decision making under risk and uncertainties

Specific sector-wise SCRM implementation

Taxonomy and analysis based on

Research Approach

Nature of study

Research methods

Key risk issues

Risk definition/ categorization

Structural and stage risks and uncertainties

Implementation issues

Study outcomes

Propositions and identification of gaps for future research

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Figure 2: Publications in groups of five years

2.2 Details of Taxonomy and its relevance to SCRM

The proposed taxonomy and its classification factors have great relevance to describing and understanding

the multi perspectives and complex risk issues. A multi-layered top-down structure is proposed to classify the

SCRM literature and to encapsulate various research perspectives (Figure 3). To analyse the research efforts in

the field of SCRM two criteria are considered: (i) research approach, (ii) exploration of key risk issues. Further

to the research approach point of view, we consider the literature based on the nature of the study (A) and

research methods (B) adopted to address the issues. Under exploration of risk issues we put specific emphasis

on the exploration of supply chain risk elements in terms of risk definition/ classification criteria (C), risk

related to structural elements of the supply chain (D) and issues related to the level of SCRM implementation

(E). Each factor is further classified on the basis of the most discriminating elements followed by identification

of the attributes of each subclass. Referring to the taxonomy, a logical identification code is also assigned to

each factor, element and attribute, which can indicate the logical linkage among them (Appendix). In the next

subsection we will discuss the classification criteria, their finer elements and their relevance to SCRM.

Figure 3: Top-town classification approach to SCRM literature

Key risk

issues

Research

approach

SCRM Literature

B C

D E

A.1

A.2 B.1 B.2

---

-- --

B.1.1

-- --

Layer -1

Layer -2

Layer -3

Layer -4

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

Research approach

A. Nature of study

A.1 Positive approach

A.2 Normative approach

B. Research Methods

B.1 Conceptual

B.1.1 Basic Theory

B.1.2 Theory Enhancement/Applied theory

B.1.3 Literature review/ Taxonomy developments

B.2 Empirical methods

B.2.1 Case Studies

B.2.2 Survey based statistical designs

B.2.3 Combined approach

B.3 Analytical

B.3.1 Risk Modelling

B.3.1.1 Modelling Type

B.3.1.1.1 Mathematical

B.3.1.1.2 Simulation

B.3.1.1.3 Multi agent

B.3.1.2 Model settings

B.3.1.2.1 Linear problem settings

B.3.1.2.2 Integer problem settings

B.3.1.2.3 Dynamic problem settings

B.3.1.2.4 Stochastic problem settings

Exploration of risk issues

C. Approach to defining/classifying Supply chain risk

C.1 Related to operational characteristics

C.2 Related to market characteristics

C.3 Related to business characteristics

C.4 Related to product characteristics

C.5 Miscellaneous

D. Risk issues related to structural elements of supply chain

D.1 Supplier(s) to manufacturer(s) relationship issues (Upstream issues)

D.1.1 Coordination and information issues

D.1.2 Supply system design issues

D.1.3 General issues

D.2 Manufacturer to buyer(s) relationship issues (Downstream issues)

D.2.1 Market volatility and demand fluctuations issues

D.2.2 Coordination under demand disruptions

E. Level of implementation of risk management approach

E.1 Risk identification approaches

E.1.1 Common listings

E.1.2 Taxonomy based risk identification

E.1.3 Scenario based

E.1.4 Objective based process mapping

E.2 Risk assessment and quantification approaches

E.2.1 Assessing the risk sources and exposure

E.2.2 Risk characterization

E.3 Risk mitigation approaches

E.3.1 Shaper

E.3.2 Acceptor

E.3.3 Recovery

Int. Journal of Business Science and Applied Management / Business-and-Management.org

2.2.1 Research approach

Nature of study (A)

In the proposed taxonomy the nature of the study (A) is considered to depict the motives of the study. As

we know SCRM is an exponentially growing area of research, the exploration of literature with the nature of

study perspective identifies the way by which the study contributes to the literature. It indicates whether the

study is conducted to describe risk issues and propose solutions with due analysis or, as in some cases, if

researchers prescribe solutions based on their experience and expertise. As the risk perceptions are multi-

dimensional and elusive it will be interesting to explore the nature of the study adopted in the extant literature.

The nature of the study of the papers is analysed using the Malhotra and Grover (1998) scheme by

categorizing them as having a positive research approach (A.1) or a normative research approach (A.2). Papers

that attempt to describe, explain, investigate and predict the current supply chain risk issues and practices with

various perspectives are considered as positive research. On the other hand, approaches that deal with the issues

in a prescriptive manner where the author suggest(s) what an individual should do in a particular risk situation

are termed normative. In normative research the author usually recommends the guiding framework and

suggestions based on their experience and expertise in a particular field.

Research methods (B)

The next important element in the research approach is the research method, which represents the

researcher’s choice to follow the route to address the research objectives. Initially we follow the Wacker (1998)

scheme and categorize the studies as conceptual, analytical and empirical. But owing to the fact that risk

management has largely been adopted by practitioners and researchers from the last decade onwards, we require

more detailed classification schemes to explore the underpinnings of risk management. Moreover, numerous

emerging techniques, methodologies and approaches are involved to address the complex and entwined risk

issues, which require a systematic framework to unify them under a relevant and logical classification scheme.

Focusing on this crucial need for comprehensive classification, we have fine-grained the classification by

categorizing the conceptual study as basic theory (B.1.1), theory enhancement (B.1.2) and literature reviews/

taxonomy developments (B.1.3). Empirical studies are categorized based on the method of data collection and

analysis such as case studies (B.2.1), survey based statistical design (B.2.2) and combination of both (B.2.3).

It is recognized that analytical approaches have been widely developed during the last decade and it is

becoming difficult to discriminate and classify them as they have a number of derived and common elements.

However, attempts are made in this study to classify the efforts of researchers adopting analytical methods. We

found that researchers adopt various approaches to develop the analytical models to assess the risks and their

impacts. We first consider the factor of risk modelling (B.3.1) and further classify this with two elements: model

type (B.3.1.1) and model settings (B.3.1.2). Various model mechanisms are available in the literature: in the risk

management perspective we consider them as mathematical (B.3.1.1.1), simulation based (B.3.1.1.2) and multi-

agent based (B.3.1.1.3). The second critical element of the analytical approach is the problem setting, which

depends upon the nature of the study and scope and domain of the research problem. We consider these as linear

problems setting (B.3.1.2.1), integer problem setting (B.3.1.2.2), dynamic problem setting (B.3.1.2.3) and

stochastic problem setting (B.3.1.2.4).

2.2.2 Exploration of risk issues

Approach to defining/classifying Supply chain risk (C)

The terms ‘risk’ ‘uncertainty’ ‘disruption’ and ‘disaster’ are frequently and interchangeably used in supply

chains to describe the perceptions and interpretations of individuals and organizations. A general interpretation

of risk is influenced by the negative consequences of variation in expected outcomes, their impact and

likelihoods (March and Shapira, 1987). Risk events are also studied with core supply chain activities and

investigated with common business practices. Christopher and Peck (2004) relate the risks with the vulnerability

and likelihood of being lost or damaged. Interruptions to the flow of information, material and finance from the

original supplier to the end user which cause a mismatch between demand and supply are also considered as

risks (Juttner et al., 2003).

In line with the definitions discussed above and to relate the risks with supply chain functional aspects we

categorize the orientation of risk definitions related to operational characteristics (C.1), market characteristics

(C.2), business/strategic characteristics (C.3), product characteristics (C.4) and others (C.5). Table 2 shows the

risk characteristics and features in each of the categories.

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

Table 2: Risk definition criterion and description

Classification

code

Risk definition criterion

Definition

description/Characteristics

Risk issues

C.1 Related to operational

characteristics

Operational features of supply

chain which mismatch demand

and supply or even disrupt the

functioning of supply chain and

interrupt the flow of material,

product and information

Supply disruptions, demand

uncertainties, machine/system

failures, improper planning and

execution, information and

security risks

C.2 Related to market

characteristics

Market fluctuations which

cannot be predicted precisely

and change their nature, impact

and occurrence over time.

Price variability, customer

behavior and expectations,

competitor moves, exchange

rates, environmental risks and

disasters

C.3 Related to business/strategic

characteristics

Specific characteristics of

business, sector, their strategies

and environment which cause an

undesired event to happen and

negatively affect the supply

chain performance

adverse effects of strategies such

as outsourcing, single sourcing,

lean manufacturing, improper

supply network design,

forecasting errors, lack of

coordination and information

sharing

C.4 Related to product

characteristics

Features related to the specific

nature of products which make

the supply chain vulnerable to

risk and uncertainties

Short product life cycles,

complexity in product design

and manufacturing, desire for

variety of products, need for

multifunctional products

C.5 Miscellaneous

Various other characteristics can

also be considered which may fit

in the above mentioned category

or can be studied separately

political risks, credibility risks

brand image risk, social risks,

ecological risks etc

Risk issues related to structural elements of the supply chain (D)

Supply chain structures are complex networks of different players (including lower tier suppliers to the end

customer) established with core objectives to minimize the costs, maximize the value and explore new markets

through effectively managed relationships among members (Hallikas et al., 2002; Blackhurst et al., 2007;

Trkman and McCormack, 2009; Tuncel and Alpan, 2010).Though networking is a way to take advantage of

collaboration and partnership amongst various supply chain players, it becomes a source as well as a medium

through which risks are generated and propagated to the entire network.

To capture the structural dimension of the supply chain risks we classify the literature for the perspectives

of upstream (D.1) and downstream (D.2). We also study the literature with a single focal firm point of view but

observe that most of the risk issues related to a single firm are more relevant in a dyadic frame. Therefore we

prefer to analyse the risk issues from a relational point of view in the form of dyads. To provide deeper insights

into the upstream risks we further classify them considering the elements of supply system design: number of

suppliers (single/multiple sourcing), location of suppliers (local/global sourcing) and coordination and

information sharing and thus divide the literature into supply system design (D.1.1) and coordination and

information sharing (D.1.2). Other issues such as supplier behaviour, traits etc. are considered under the general

issues category (D.1.3).

Downstream risks usually relate to the fluctuations in demand, volatile market conditions, customer

behaviour, technological changes and shorter product life cycles. At one end these risks are associated with the

physical distribution and product flow towards the downstream side and on the other hand they are related to

forecasting issues (Szwejczewski, et al., 2008). These risks are usually the outcome of a mismatch between

actual demand and projected demand resulting in a demand and supply mismatch throughout the supply chain.

We focus on two discriminating elements and classify the demand issues as market volatility and demand

fluctuation (D.2.1) and coordination and information sharing (D.2.2).

Level of implementation of risk management approach (E)

Implementation of supply chain risk management is an extremely critical task requiring a sound knowledge

of business functions, market trends and financial and infrastructural status of the organization as well as the

entire supply chain. Implementation of SCRM generally requires three steps given as: identifying the potential

risks to the organization (E.1), assessing the risks and aftermaths (E.2) and adopting suitable risk managing

Int. Journal of Business Science and Applied Management / Business-and-Management.org

strategies (E.3). A hierarchy exists between these phases and the higher phase subsumes the lower phase

(Dailun, 2004).

Risk identification is an important first step in any risk management effort. Numerous approaches have

been proposed to identify the risks in supply chains, classified as: the common listing approach (E.1.1), where

analysis of historical events is utilized to gain insight into future risks; taxonomy based approaches (E1.2),which

provide a consistent framework to elicit and organize risk identification activities related to various business

functions; scenario analysis (E.1.3), in which key risk factors and their effects on supply chain performance are

analysed to develop a risk profile, making it easy to develop contingency plans at the operational level; risk

mapping (E.1.4), with the capability of exposing the vulnerability of supply chains to potential risk before their

occurrence.

Assessing the risks qualitatively or quantitatively is an essential task after the risk identification. When

sufficient past data and expertise is available quantification of risks is meaningful, otherwise qualitative methods

are more appropriate. We categorize the methods as assessing the risk sources (E.2.1) and risk characterization

(E.2.2), with the latter being more rigorous. Assessing the sources and exposure (E.2.1) is effective when

limited past data is available. The sources of risks and exposure are evaluated and subjectively indexed/ranked

based on the assessor’s perspective and experience. Risk characterization (E.2.2) provides a broader framework

for risk assessment, grouping and prioritizing employing analytical models.

Various strategic and operational risk management stances are reported in the literature. We classify them

as the shaper (E3.1), accepter (E3.2) and recovery approach (E3.3). In the shaper approach attempts are made to

shape (reduce the impact and frequency) the uncertainty factors without changing the existing settings of the

supply chain, while in the accepter approach risks are accepted and supply chains are reinvestigated and

redesigned. Recovery strategies mainly support quick recovery mechanisms after severe damage in the supply

chains.

3 ANALYSIS OF RESULTS AND DISCUSSION WITH PROPOSED TAXONOMY

We explore the literature and review the selected papers using the above discussed taxonomy. To develop a

holistic view of SCRM efforts we included studies in practically all key demographical regions including

Europe, Asia and the US. A combination of qualitative and quantitative approaches is adopted to describe the

SCRM issues in the literature. The qualitative contents of the papers are provided in tables showing the issues

discussed in the paper and also the approach adopted to address them. The quantitative exploration is presented

in a table 3 and 2333 showing relative contributions of various classes and sub-classes under particular themes.

Table 3: Contribution of papers as per research approach

Classification

Factor

Sub

Classification %Contribution

Sub

Classification %Contribution

Nature of study

(A)

Positive (A.1) 91

……..

Normative (A.2) 9

Research

Method (B)

Conceptual (B.1 39

Basic Theory (B.1.1)

Theory Enhancement

(B.1.2)

Literature reviews/

taxonomy development

(B.1.3)

Empirical (B.2) 26

Case Studies (B.2.1) 28

Survey based statistical

designs (B.2.2)

Combined approach

(B.2.3)

Analytical (B.3) 35

……..

3.1 Observations on research approach

3.1.1 Nature of study (A)

We first review the papers focusing on the nature of the study and approach adopted. We found that an

ample amount of work has been done but still it seems to be in a nascent state due to the paucity of normative

studies. It is noted that more research initiatives have been taken with a positive approach (91%) than normative

research (Table-3). The low proportion of normative research (9%) exhibits the under-preparedness of research

attempts to proffer precise and specific prescriptions to industries and academia.

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

3.1.2 Research methods (B)

Interestingly we found that even after the decade long period the contribution of conceptual research is

highest, about 39%, followed by empirical (26%) and analytical, 35% (Table-3). This finding suggests that the

field of SCRM is still emerging and requires theoretical support to develop practical frameworks. Analytical

approaches have also made a major contribution to assessing and characterizing the risk issues. But the feeble

acceptance of these models in actual practices point out the need for more empirical studies to explore the

critical underpinning elements and relationships of the risk appetite of firms, their propensity and financial

status.

Conceptual Study (B.1)

To provide the finer details, conceptual papers are further classified and it is observed that during 1996 to

2001 most of the papers focused on theoretical aspects related to risk issues, usually inspired by financial risk

theories. But later on, catastrophic incidents such as the earthquake in Taiwan (2000), which severely damaged

the supply base of the semiconductor industry, the Tsunami in Asia in 2005 that caused losses of more than $17

billion, Hurricane Katrina, which destroyed ports, railways, highways and communication networks and led to a

significant drop in the US economy in 2006;terrorist attacks in the US and many Asian and European countries

and many more motivated the researchers to redefine the risk issues for business continuity and devise

mechanisms for quick recovery after disruptions. Thus agility, resilience and flexibility in supply chains have

become the core agenda for research. This has increased the contribution to the applied theory of SCRM,

dealing with contemporary and upcoming issues (Table-3).

Table-3 shows that theory is enhancing rapidly in the field of SCRM. Researchers are forming deeper

insights and delving into critical SCRM aspects. Analysis also indicates that the field of SCRM is expanding but

the attempts are still very small to review the prevalent literature. Thus more reviews are required to unify the

various research efforts and explore the latent dimensions of risk management to support the global SCRM

efforts significantly. The qualitative description of the issues addressed in papers, their approach and

classification code is provided in Table 4.

Table 4: Description of conceptual research methods with risk issues discussed and classification code

Classification

code

Theoretical

Approach

Moves to manage

uncertainties

Description of issues and papers

B.1.1 Fundamental supply

chain and risk issues

Discuss the basic

risk issues

Metrics and performance measurement for risks

(Lawrence et al., 1996; Smeltzer and Sifered, 1998;

Steven and Ronald, 1999; Sislian and Satir, 2000;

Ritchie and Brindley, 2007), Risk management for

practitioner perspectives (Hallikas et al., 2002; Finch,

2004; Yang et al., 2004; Juttner et al., 2003; Ojala and

Hallikas, 2006), Risk definitions and classifications, risk

constructs (Tang, 2006b; Kersten et al.,2007;Berg et al.,

2008; Bailey and Francis, 2008; Trkman and

McCormack, 2009)

B.1.2 Risk management

theory enhancement

Propose theoretical

models and

frameworks to

manage risk issues

Collaboration for responsiveness and customer

satisfaction level (Christopher and Lee,

2004;Christopher and Peck, 2004; Jeng, 2004; Forme et

al., 2007), Intangible issues of supply chain risks,

behavioral aspects of risk ( Ketzenberg et al., 2007; Kim

and Park, 2008; Brun et al., 2006), Strategic and

structural alignment issues (Giunipero and Eltantawy,

2004; Cigolini and Rossi, 2006; Peck, 2006; Khan and

Burnes, 2007; Tapiero and Grando, 2008; Ritchie et al.,

2008; Dani and Ranganathan, 2008), Value and risk

identification and assessment in an advanced planning

and scheduling system (Hung and Sungmin, 2008;

Kenett and Raphaeli, 2008; Neiger et al., 2009,

Szwejczewski, et al., 2008), Disruption risk

management (Norrman and Jansson, 2004; Kleindorfer

and Saad, 2005; Narasimhan and Talleri, 2009; Michael

and Nallan, 2009), Robust strategies for risk mitigation

(Tang, 2006b)

Int. Journal of Business Science and Applied Management / Business-and-Management.org

B.1.3 Literature review and

taxonomies

Classify the risks,

uncertainties and

associated issues to

unify the disjointed

supply chain

management

literature

Classification of quantitative models dealing with risks

and uncertainties(Tang, 2006), Classifications of risks

(Dailun, 2004; Rao and Goldsby, 2009), Classification

of literature considering unit of analysis, research

methods etc as a classification factor (Vanany et al.,

2009),Consideration of intangible and behavioral

aspects of risk issues (Ponomarov and Holcomb, 2009),

Focus on flow risks and developments (Tang and Musa,

2010)

Empirical study (B.2)

We include the papers that used empirical approaches with surveys followed by statistical designs and

structured case studies. Many papers are also noted that have a combination of both methods for quantitative

and qualitative analysis. The empirical approaches have been used to establish the relationships amongst latent

supply chain issues such as short supplies, supplier characteristics, demand variability, erratic behavior of

customers, risk propensity (Blackhurst et al., 2005; Shockley and Ellis, 2006; Bailey and Francis, 2008).These

methods refine the level of understanding of risks, which further helps in taking strategic and operational

decisions (Devraj et al., 2007; Sanders, 2008). It is recognized that survey based statistical designs are the most

adopted approach in empirical studies (52%) to develop the relationship models. But in the SCRM literature

case-studies also have increasing acceptability to develop more specific qualitative and quantitative models.

Table 5 presents a description of the issues and moves to manage the risk in certain empirical papers.

Table 5: Description of empirical research methods with risk issues discussed and classification code

Classification

code

Empirical

Approach

Moves to manage

uncertainties

Description of issues and papers

B.2.1 Case Studies Investigation of

specific cases

Value and risk assessment (Brun et al., 2006; Ojala and

Hallikas, 2006), Perception of risks (Finch, 2004; Zhao

et al., 2008),Managing information flow (Khan and

Greaves, 2008 ; Bailey and Francis, 2008 ; Oke and

Gopalakrishnan, 2009)

B.2.2 Survey based

statistical design

Establish

correlations for

supply chain

performance and

risks

Investigation of outsourcing decisions ( Lambros and

Socrates, 1999), Investigation of the supply risk

construct and integration (Shockley and Ellis,

2006 ;Wagner and Bode, 2006; Harland et al., 2007),

Issues related to practitioner point of view(Juttner,

2005), Agency theory in risk management (Zsidisin and

Ellaram, 2003), Effect of disruption on stock price

performance (Hendricks and Singhal, 2005), Devaraj et

al., 2007; Lee et al., 2007; Haan et al., 2007), Agility

and flexibility in supply chain (Khan andGreaves,

2008;Braunscheidel and Suresh, 2008; Sodhi andTang,

2009)

B.2.3 Combined approach Establishing the

signifiacnt

relationships for

specific cases

Disruptions in supply chains (Blackhurst et al., 2005;

Jiang et al., 2007), Risk and information sharing issues

(Zhou & Benton Jr., 2007 ;Kocabasoglu et al., 2007;

buyer perception of supply risks(Ellis et al.,2010)

Analytical study (B.3)

In order to plan and coordinate in a risk environment, quantification of risk and analytical modelling is

required. Based on the modelling approach we categorize the literature into mathematical (B.3.1.1.1), simulation

(B.3.1.1.2) and agent based methods (B.3.1.1.3) for a variety of settings such as linear (B.3.1.2.1), integer

(B.3.1.2.2), dynamic (B.3.1.2.3) and stochastic (B.3.1.2.4). Table 5 lists the details of the papers and issues

explored using analytical approaches.

The simple analytical approach to quantify and rank the risks is the Analytical Hierarchy Process (AHP)

with linear problem settings in a multi attribute decision model. It reduces the complex decision problem into a

series of one to one comparisons followed by synthesis of results based on a hierarchical structure (Korpela et

al., 2002; Gaudenzi and Borghesi, 2006; Levary, 2008). However, the subjectivity involved in AHP has always

been a matter of concern.

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

Owing to the very nature of the risk, the stochastic models are more accepted in supply chains to model

risk issues, varying from strategic to operational levels (Beamon, 1998). The uncertainty associated with

variables is tackled mainly with three approaches. First, standard distributions are used in which continuous

probability distributions are assigned for decision variables. Second, when continuous distribution is not

feasible, discrete finite scenarios are established considering various combinations of uncertain parameters.

Third, there are fuzzy approaches, where uncertainties in decision parameters are considered as fuzzy numbers

and membership functions (Chen and Lee, 2004; Mele et al., 2007). Underlying complexities and impractical

assumptions limit the utility of mathematical modelling. Moreover, in some cases the explicit relationships

between decision variables are difficult to model. In such situations, simulation techniques provide an

alternative approach to analysing the supply chains by constructing an artificial environment within which the

dynamic behavior of the risks can be assessed. Various risk mitigation strategies and tradeoffs are tested in a

simulated environment with seasonality, level of information sharing, service level, net profit etc as simulation

parameters (Labeau et al., 2000; Jammernegg and Reiner, 2007; Sohn and Lim, 2008; Thomas and David,

2008).

The simulation models also have certain limitations, such as the models can only be run with previously

defined conditions and there are limited capabilities to design the system parameter itself (Swaminathan et al.,

1998; Ohbyung et al., 2007). To overcome these shortcomings, multi-agent approaches, supported by advanced

computational methods, have been introduced. In these approaches the problem is modeled as agent elements

(supplier, manufacturer, distributor etc), control elements (inventory control, scheduling, logistics and

transportation etc) and their interaction protocols (Swaminathan et al., 1998; Mele et al., 2007). These

approaches are better than individual programs as they combine the various autonomous agents/programs in one

platform. Various strategic and operational issues such as collaboration under demand and supply uncertainties,

the role of information sharing, inventory levels, robust and optimal designs are investigated and managerial

inferences are drawn by researchers (Chatzidimitriou et al., 2007; Mele et al., 2007; Ohbyung et al., 2007).

Table 6: Description of analytical research methods with risk issues discussed and classification code

Classification

code

Analytical

Approach

Moves to manage

uncertainties

Description of issues and papers

B.3.1.1.1/

B.3.1.2.1

Mathematical

(linear

settings)AHP

Evaluating the risk ranks Risk quantification using multi decision criteria

(Korpela et al., 2002; Levary,2008;Teresa et al., 2006)

B.3.1.1.1/

B.3.1.2.2

Mathematical

Stochastic

Models

(probability

distributions

and Scenario

settings)

Quantification of risk using

mean variance analysis

Quantifying the risk and performance attitude (Choi et

al.,2008), Supplier failure risks (Lee, 2008)

B.3.1.1.1/

B.3.1.2.4

Uncertainty quantification

with fuzzy sets

Evaluating the performance of the supply chain using

fuzzy sets for uncertain parameters(Chen and Lee, 2004;

Wang and Shu, 2007); Moghadam et al., 2008; Li and

Kuo, 2008)

B.3.1.1.1/

B.3.1.2.4

Planning under

uncertainties

Mid-term planning models (Gupta and Maranas,2003),

Managing inventory levels and profit margins, strategies

mix to minimize the effect of order variations,

decomposing the problem to profit maximization and

risk minimization objectives (Escudero et al., 1999; Kut

and Zheng, 2003), Integrating risk management and

B2B tools (Aggarwal and Ganeshan, 2007, Risk

assessment in global chains, sourcing decisions under

disruptions (Goh et al., 2007; Stephen et al., 2007;

Boute et al., 2007; Ouyang, 2007; Hong and Sung,

2008;Haisheng et al., 2009, Bogataj and Bagataj, 2006)

B.3.1.1.1/

B.3.1.2.4

Coordination under

uncertainties

Investigating the coordination strategies under

production cost deviation and demand disruptions

(Thomas and Griffin, 1996; Mantrala and Raman, 1999;

Xiao et al., 2007), Quantifying coordinated decisions

(Hsiesh and Cheng, 2008; Demirkan and Cheng, 2008)

B.3.1.1.2/

B.3.1.2.4

Simulation

Planning under

uncertainties

Planning and controlling the inventory and supplier

selection (Moghadam et al., 2008; Jammernegg and

Reiner, 2007), inventory and capacity coordination

(Liston et al., 2007), planning outsourcing, assessing

risks and relations to inventory levels(Thomas and

David, 2008)

Int. Journal of Business Science and Applied Management / Business-and-Management.org

B.3.1.1.2/

B.3.1.2.4

Coordination under

uncertainties

forecasting of demand distortion in case of lack of

information sharing(Meilin and Jingxian,2007;

Carbonneau et al.,2008)

B.3.1.1.2/

B.3.1.2.4

Structuring of network

under uncertainties

Design and restructuring of

production/distribution networks (Mele et al.,2007)

B.3.1.1.2/

B.3.1.2.2

Information policies and

forecasting methods for

risk mitigation

Performance of supply chain with various information

sharing levels (Lau et al.,2004), coordination between

inventory and ordering (Sohn and Lim, 2008)

B.3.1.1.3/

B.3.1.2.4

Multi-agent

systems

Robust mechanism

trading in dynamic and uncertain environments

(Chatzidimitriou et al., 2008)

B.3.1.1.3/

B.3.1.2.2

Collaborations under

uncertainties

Investigation of collaborations for maximum efficiency

under demand and supply uncertainties (Ohbyung et al.,

2008), Decision and implementation of risk

management (Giannaikis and Louis, 2010)

3.2 Observations on exploration on risk issues

Literature is further reviewed to explore the risk issues addressed and contribution to various classification

factors and presented in table 7.

Table 7: Contribution of papers as per risk issues explored

Classification

Factor Sub classification % contribution Sub classification %contribution

Approach to

defining/ classifying

Supply chain risk

(C)

Related to operational

characteristic(C.1)

……...

Related to market

characteristic(C.2)

Related to business

characteristic (C.3)

Related to product

characteristic (C.4)

Miscellaneous

issues (C.5)

Risk issues related

to structural

elements

of supply chain (D)

Supplier(s) to

manufacturer(s) relationship

issues

(Upstream issues) (D.1)

Coordination and information

issues (D.1.1)

Supply system design issues

(D.1.2)

General issues (D.1.3)

Manufacturer to buyer(s)

relationship issues

(Downstream issues) (D.2)

Market volatility and demand

fluctuations issues (D.2.1)

63.5

Coordination under demand

disruptions (D.2.2)

36.5

Level of

implementation of

risk management

approach (E)

Risk identification

approaches (E.1)

…….

Common listings (E.1.1)

Taxonomy based risk

identification (E.1.2)

Scenario based (E.1.3)

Objective based process

mapping (E1.4)

Risk assessment and

quantification

approaches (E.2)

…….

Assessing the risk sources and

exposure (E.2.1)

Risk characterization (E.2.2) 55

Risk mitigation

approaches (E.3)

…….

Shaper (E.3.1)

Acceptor (E.3.2) 45

Recovery (E.3.3)

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

3.2.1 Approach to defining/classifying Supply chain risk (C)

Employing the classification of risk definition criteria, table 7 shows that the operational characteristics

(C.1) (e.g. demand-supply mismatch) are used to a greater extent (31%), followed by the market characteristic

(C.2) (25%). The specific business features, strategies and their effects on the supply chain (C.3) have also been

used in defining the risks (19%). In a business world where customers' expectations regarding products and

services are changing, product centric orientation is a paramount consideration. Various definitions of risks,

focusing on product characteristics (C.4) such as the product life cycle, functional features, variety, and the

technical complexities involved are also gaining acceptance gradually (13%). Apart from this many more

influencing features such as political, legal and financial issues (C.5) have also been used by some authors

(13%). Table 8 provides the qualitative details of issues considered for risk classification in various papers.

Table 8: Details of papers on risk definition criteria with classification code

Classification

Code

Risk issues/sources Papers

C.1 Infrastructural, transport, communication, design,

quality, cost, availability, manufacturability,

health and safety, natural hazards, terrorism and

political instability

Mason-Jones et al. (1998), Zsidisin et al. (2000),

Kersten et al. (2007) Klerndorfer and Saad (2005),

Faisal et al. (2006), Faisal et al. ( 2007), Boin et al.

(2010)

C.2 Changing market conditions, customer

expectations, product yields, quality, process time

Ritchie and Brindley (2007), Wong and Arlbjorn

(2008), Serbanescu ( 2007), Mele et al. (2007)

C.3 Focus on efficiency rather than effectiveness,

globalization of supply chains, trends of

outsourcing, reduction of supplier base, Lack of

trust, Inaccurate information sharing and

asymmetry in power and dependency

Juttner et al. (2003), Finch (2004), Ojala and

Hallikas (2006)

C.4 Product complexity and serviceability Levary (2008), Knemeyer et al.(2009),

Szwejczewski, et al. (2008)

C.5 Operational contingencies, Legal risks, political

risk

Jiang et al. (2009), Manuj and Mentzer (2008)

3.2.2 Issues related to structural elements of the supply chain (D)

It is observed that researchers have focused on the risk issues on both sides of the supply chains but

upstream issues get more attention, as shown in table 7, with a 56% contribution. This suggests that supply

chains are more vulnerable to supply side risks. The downstream issues also make a significant contribution

(44%), which shows that market uncertainties, demand fluctuations and associated risk issues are also well

addressed by researchers. Table 9 shows the details and codes of papers representing upstream and downstream

risk issues.

Upstream issues (D.1)

Upstream risks are associated with procurement and are considered to be threats to supply assurance, the

possibility of improper supplier selection, increased company liabilities and uncertainty in supply lead time

(Smeltzer and Sifered, 1998; Sislian and Satir, 2000; Meixell and Gargeya, 2005). It is observed that about 56%

of the related papers focus on upstream risks. The key issues of supply risks are found to be related to supply

system design (number of suppliers (single/multiple sourcing)), location of suppliers (local/global sourcing) and

supplier’s agility, flexibility, delivery reliability and infrastructural strength and coordination and information

sharing, which we covered in our classification. Analysis of the literature focusing on supply risks shows that

information sharing and coordination issues (D.1.1) have been paid the highest attention (44%) followed by the

supply system design issues (D.1.2) (36%) (Table7).

Downstream issues (D.2)

We focus on two discriminating elements and classify the demand issues as market volatility and demand

fluctuation (D.2.1) and coordination and information sharing (D.2.2). Coordination and information sharing

amongst wholesalers, dealers, and retailers and shorter planning horizons are some of the measures suggested in

the literature to manage demand side risks (Gupta and Maranas, 2003; Chen and Lee, 2004; Boute et al., 2007;

Stephan et al., 2007). There have also been proposals to investigate the level of information sharing from a

security point of view and adopt trust based mechanisms under volatile market conditions (Xiao et al., 2007). As

mention in table 7 issues related to demand and order variability have been considered more (63.5%) in the

literature than coordination and information sharing issues (36.5%) to manage downstream risks.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Table 9: Details of papers dealing with the structural element of risks with classification code

Classification

code

Structural position of

supply chain

Description of issues and papers

D.1.1 Up-stream issues Number of suppliers, and location related issues (Teresa et al., 2006; Abbas et

al., 2006; Moghadam et al., 2007; Lee, 2008; Aggarwal and Ganeshan, 2007;

Li-ping Liu et al., 2007; Goankar and Viswanadham, 2004; Haisheng et al.,

2009)

D1.2 Relationship and coordination issues on supply side (Hallikas and Virolainen,

2002; Levary, 2008; Sarkar and Mohapatra, 2009; Ojala and Hallikas, 2006;

Trkman and McCormack, 2009)

D.1.3 Responsibilities and reliability of suppliers (Giunipero, and Eltantawy, 2004;

Jeng , 2004; Thomas and David, 2008)

D.2.1 Down- stream issues Demand variability and market uncertainties (Mantrala and Raman, 1999;

Gupta and Maranas, 2003; Kut and Zheng, 2003; Chen and Lee, 2004; Donk

and Vaart, 2005; Boute et al, 2007; Neureuther and Kenyon, 2008)

D.2.2 Coordination under demand disruptions, Profits and service levels (Xiao et al.,

2007; Meilin and Jingxian, 2007; Chatzidimitriou et al., 2008; Ohbyung et al.,

2007; Sohn and Lim, 2008; Hsieh and Cheng, 2008)

3.2.3 Issues related to implementation of Supply chain risk management (E)

Various levels of SCRM implementation are analysed: identifying and classifying potential risks to the

organization (E.1), assessing the risks and aftermaths (E.2) and adopting suitable risk managing strategies (E.3).

Risk identification (E.1)

The literature reflects various approaches to identifying the risks which we have categorized, as noted

earlier in our taxonomy. Table 7 indicates that scenario based approaches (E.1.3) are most accepted (30%) in the

literature because of their ability to predict the impact of risks. Their accuracy, however, depends on the ability

and vision of the person setting the scenarios. Listing methods (E.1.1) are also common (27%) due to their

simplicity. Objective based mapping (E.1.4) has also been used. It is difficult to prepare an exhaustive mapping

but once completed it provides a very effective and accurate tool to understand the sources and drivers of risk.

This method is gaining acceptance for specific supply chains (23%). Taxonomy based approaches (E.1.2) are

usually influenced by the existing literature and practices to establish detailed and systematic risk classification

schemes. As the risk management practices and related literature is growing and becoming more refined, the

acceptability of this approach is expected to grow.

Risk assessment and quantification approaches (E.2)

As indicated in table 7 risk characterization (E.2.2) is more common (55%) followed by assessing the

sources and risk exposure (E.2.1) (45%). The analytical approaches are not widely accepted firstly, due to their

complexity and the requirement of expertise to implement them and secondly, existing methods are yet not

capable of quantifying the elusive and dynamic nature of risk.

Risk mitigation approaches

Various strategic and operational risk management schemes are classified: the shaper (E3.1), accepter

(E3.2) and recovery approach (E3.3). When past knowledge and experience related to market uncertainties are

available, shaper strategies are found to be better. With this stance efforts are made to avoid the risks by

dropping the risk prone market, customer or supplier. Contractual agreements are also in practice to minimize

the risk intensity. To control the severity of risk, stocking an excess buffer and safety stocks are also a common

phenomenon.

If the risk events are unavoidable, accepter strategies are adopted, in which supply chain visibility and

coordination is improved and supply networks are redesigned, considering risks as a prime concern (Berge et.

al., 2008). A variety of strategies such as supplier selection, number of suppliers, coordination architecture and

level of information sharing, accepting the risks and uncertainties (Moghadam et al.,2007; Mantrala and Raman,

1999; Gupta and Maranas,2003; Boute et al., 2007; Neureuther and Kenyon, 2008) have been suggested in the

literature. After 9/11 (the terrorist attack in the US) and a series of natural disasters, recovery strategies are

increasingly considered by researchers. Continuity management and development of quick recovery plans are

becoming a focal research area. Flexibility, agility, knowledge management, information sharing and

horizontal/vertical integration are the key issues that are investigated form the point of view of recovery

(Norrman and Jansson, 2004; Peck, 2006; Dani and Rangnathan, 2008; Braunscheidel and Suresh, 2008). Table

7 shows that accepter approach is the mostly widely considered one in the literature to design risk management

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

strategies (45%) followed by the recovery approach (40%). Shaper approaches are not as commonly discussed

as the other approaches (15%).

4 MANAGERIAL IMPLICATIONS AND CHALLENGES

The detailed classification scheme is further explored with two very significant factors representing the

challenges to the adoption of SCRM: first, the coordination and decision making in uncertain business

environments and second the implementation issues of SCRM for various sectors.

Figure 3: Linkage of coordination mechanism and effectiveness of risk management strategies

4.1 Coordination and decision making in an uncertain business environment

The literature reflects the fact that coordination strategies are established in supply chains at operational

and strategic levels for synchronization of the inventory, logistics and production, employing information

sharing as a tool for timely, relevant and accurate decision making (Sahin and Robinson, 2002). But in a

changing business environment the paradigm has shifted and organizations are more inclined to integrate and

review coordination strategies to reduce unexpected and undesired events throughout the network for better

management of dependencies (Mele et al., 2007). Thus it will be interesting to study the decision making and

coordination strategies with supply chain risk perspectives. In a competitive business environment coordination

and collaboration is becoming the prime concern but criticality arises to make tradeoffs between the level and

type of coordination and associated risks. A fundamental framework suggesting business integration is

discussed by Kleindorfer and Saad (2005), including the strategies to reduce the impact and frequencies of

disrupting elements. But theoretical treatment limits its application to the initial levels. Forme et al. (2007) have

proposed an improved framework for business collaboration with two models, namely the collaboration

characterization model (CCM) and the collaboration oriented performance model (COP). As far as the COP

model is concerned they consider flexibility, reactivity, quality and lead times as a measure of performance

index with various collaboration levels. They found that at the design and development level collaboration is

high but at the operational level more efforts are required to compete in a demand driven market. Inclusion of

supply, internal and financial risks can make these models more effective and acceptable.

Market volatility, shorter product life, uncertain demand is also considered by researchers while studying

the coordination strategies at the operational and strategic level (Brun et al., 2006). They further assess the level

of information sharing considering the value and risk in the supply chain. Donk and Vaart (2005) also studied

the integration and collaboration issues with their empirical investigation with one supplier and their five buyers

of a different nature. They found that shared resources in supply chains limit the possibility of integration. Their

framework helps to set a level of integration in a particular risk and resource sharing situations. On a similar line

of action Ojala and Hallikas (2006) touched on the investment risks in networking with the help of the case of

two industrial original equipment manufacturers and nine of their suppliers, including from the electronics and

metal sectors. Considering the network structure related risks and focusing on the investment decisions in

networking in a buyer dominated environment they suggest four themes of investment decisions: investment

specificity, investment pace, investment size and the possibility of wrong decisions. They found that the

reliability of information plays a significant role in investment decisions. It is realized that more fine-grained

models are now required to find the hidden complexities of the decision making process and coordination in the

context of business risks and uncertainties.

As suggested in the literature, coordination strategies can be reviewed under the influence of two

managerial decision making environments: centralized and decentralized. In a centralized decision making

environment the focal concern of the managers is to align the marketing and operational management objectives

to improve the relationship between supply chain members (Demirken and Cheng, 2008; Donk and Vaart, 2005;

Hallikas and Virolainen, 2005). Managers are always assertive in order to develop strategic protocols for

coordination among various members for sustainable relationships. The critical challenge faced by the managers

in a centralized decision making environment is that the firm which leads the supply chain and has the power to

take strategic decisions defines the risks with their own perspective and characterizes the risk impacts with their

Coordination

mechanism and

relationshi

Effectiveness of

risk management

strate

ies

Int. Journal of Business Science and Applied Management / Business-and-Management.org

own appetite. It is further argued that they have the tendency to bear minimum risk and transfer it to other

players, resulting in imbalances in the whole supply chain, which strain the entire supply networks.

Decentralized supply chains can be viewed in a different way and considered as an aggregate body of

various discrete entities, where coordination exists, not more than inter-firm or dyadic level. It is observed that

most of the decentralized supply chains suffer from uneven power distribution and conflicting risk perceptions

and attitudes that limit the performance of the individual risk management strategies of various members.

Managers can handle this challenge by addressing three prime issues in centralized as well as decentralized

decision making environments: First, as we discussed above, the risk is multidimensional and multi-perspective

in nature it could be better to identify and define the risk and its elements not only at the firm level but jointly at

the supply chain level, including lower tier suppliers to the end customer. Many times it becomes impractical to

consider long chain analysis in strategic decision making, in this case, at least, dyadic relationships should be

considered for initial listing of risks and their quantification schemes. Second, in cases where the members have

their own risk perceptions and strategic stances and plan to mitigate them, sincere managerial efforts are

essential for strategic alignment of multiple perceptions and incorporation of a common minimum program.

Third, managers should encourage the tendency to share the appropriate risk by linking it with profit sharing and

investment of funds in supply chains. Thus suitable coordination mechanisms, including resource as well as risk

sharing structures and level of control can resolve the issues of centralized and decentralized supply chains

under risk and uncertainties to a large extent.

On reviewing the risk management literature, it has been found that most of the studies dealing with risk

and uncertainties sufficiently cover issues like

demand and supply disruptions, network design and multilevel

inventory studies but the role of coordination mechanisms under diversified risk situations have not been

thoroughly addressed. Supply chain coordination provides the means to understand and analyse the supply chain

as a set of dependencies in the form of physical flow and information flow. Appropriate coordination in the

supply chain can also reduce uncertainty in networks and strengthen the networks to perform better in existing

risks and uncertainties. It is also argued that coping with uncertain situations should be the prime motive of

coordination mechanisms. From the above discussion we conclude that integration and coordination among

supply chain partners is a prerequisite for an effective risk management program and, furthermore, existing

coordination mechanisms should be revisited considering the perceived risks and uncertainties (Figure 15). This

discussion has helped us to synthesize two coupled propositions. These propositions can be examined and

investigated empirically in various business environments.

P-1 Strong relationships and appropriate coordination mechanisms among partners improve the

effectiveness of risk management strategies.

P-2 Existing coordination strategies can be more effective if revisited and revised, considering perceived

risks and uncertainties.

4.2 SCRM for various sectors

We further explore the literature with the theme of implementing SRCM in diversified sectors and the

practical implications. Disparity among supply chain partners, limited visibility and conflicting risk perspectives

are key barriers to the implementation of SCRM at the supply chain level. Further it is argued that common

SCRM strategies cannot be effective for diversified industrial sectors as the notion of risks, challenges, barriers

and facilitators may vary with the nature, size and type of industry (Finch, 2004; Juttner, 2005). To explore this

fact we study the diversified risk issues and preferences of certain industrial sectors.

Managing the supply chains of high-tech industries such as semiconductors, computer hardware and other

electronic components is becoming challenging due to current business trends towards shorter product

lifecycles, ever-changing customer demand, expanding product variety, and globalization. In high-tech

industries, technology is changing rapidly, resulting in higher costs of obsolescence compared to other industries

(Kut and Zheng, 2003; Jeng, 2004). Accurate forecasting, information sharing and integration among the supply

chain players are urgently needed for this sector to manage the market volatility and price variability. Thus a

specific SCRM program including dynamic risk factors will be more effective for such conditions.

Chemical and process industries have different situations. This sector is more vulnerable to safety and

hazard issues; the efforts in this area are primarily focused on reduction of operational risks in the form of

accidents, machine failures and supply disturbance which can propagate throughout the supply chain

(Kleindorfer and Saad, 2005; Donk and Vaart, 2005). An SCRM program focusing on operational features and

safety issues will be more relevant for this sector.

The automobile and machine components sector has been found to be plagued with high costs, reducing

profit margins and accelerating competition. The focus of SCRM strategies is to redesign supply networks

considering specific business risk issues and to investigate trade-offs between efficiency and responsiveness in

the known/anticipated business risks environment (Moghadam et al., 2008; Carbonneau et al., 2008).

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

Diversified product requirements, changing customer tastes, stiff competition and distribution problems are

the marked features of the textile industry (Forme et al., 2007; Brun et al., 2006). For the textile sector SCRM

should strive to develop strategies to optimally manage the production capacity, workforce level and storage

space, considering customer preferences, flexibility, reactivity, quality and lead times as the performance

measure.

Food companies are continuously reviewing their business models in a changing business environment.

Sustainability is becoming a key concern for this sector. Safety hazards such as contamination, biological risks,

genetic risks and natural disasters, distribution and packaging losses, inappropriate contingency plans are the

key challenges of this sector (Hong and Sung, 2008). In this sector SCRM primarily focusing to minimizing the

wastage through proper distribution, storage and packaging in a collaborative environment, but food supply

chain elements are still loosely linked and require more transparent and integrative models.

The above discussion reveals that different industrial sectors have diversified risk issues, priorities and

needs. It could be interesting to explore this proposition empirically and identify the commonalities and

differences among different sectors regarding risk issues to form clusters and develop specific risk mitigation

strategies for clusters.

P-3 A common SRCM program may not be effective for different supply chains and specific SCRM

strategies for specific sectors and industries are required.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Table 10: Risk issues and risk management for specific industries/supply chains

Industry /

Sector type

Risk issues & sources Risk mitigation approach Papers

Electronics components/systems

Semi-conductor Supply and demand

uncertainties

Correlating external demand

to the supply lead time

variability

Kut and Zheng (2003), Jeng

(2004)

Electronic devices Network risks in a buyer

dominated environment

Assessing information risk

& reliability

Ojala and Hallikas (2006)

PC manufacturing Inbound risk identification

&classification

Prototype model based on

lit. reviews

Wu . et al. (2006)

Telecommunication Internal and network risks Trade-off between capacity

and inventory management

Jammernegg and Reiner

(2007)

Electronics Outsourcing risks with

contract costing

outsourcing with control

costing

Liston et al. (2007)

DDR/RAM manufacturer Demand variability with

seasonality multi-generation

products

Combination of forecasting

method and level of

information sharing

Sohn and Lim(2008)

Process

Chemical Accidents and disruptions Integration to reduce impact

and frequency of risks

Kleindorfer and Saad (2005)

Chemical Supply, demand and internal

uncertainties

Risk calculation Li Ping Liu et al. (2007)

Pigments as raw materials Variation in product

specifications and volumes

Suggesting different

integration with various

uncertainty levels

Donk and Vaart (2005)

Textile/Fashion

Fashion products Demand variability with

product variety

Assessment of value and

risks

Brun et al. (2006)

Textile Collaboration Risks under

high demand variability

Developing collaboration

performance indexes such as

key success factors & key

performance factors

Forme et al. (2007)

Textile Demand uncertainties Production loading plans

using uncertainty data

Stephan et al. (2007)

Miscellaneous

Machine tools Supply risks Risk control with optimal

inventories

Moghadam et al. (2005)

Foundries Demand disruptions soft computing methods for

forecasting

Carbonneau et al. (2008)

Agriculture Supply risks, product

decomposition

Improved auction model Hong and Sung (2008)

Automobile spare parts Demand variability Suggesting better

forecasting methods and

inventory management

Li and Kuo (2008)

Food, beverage and meat Demand amplification due

to information mismatch

Collaborative partnership to

manage demand and

information flow

Cigolili and Rossi (2006),

Baily and Francis (2008)

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

5 REVIEW FINDINGS, EXPLORATION OF GAPS AND AVENUES FOR FUTURE RESEARCH

SCRM is an exponentially growing area of research encompassing multidisciplinary and multidimensional

aspects of risks. As the body of SCRM literature involves complex and entwined issues, a systematic taxonomy

could make a great contribution. To delve into the supply chain risk issues we presented a multi-layered top-

down classification scheme. In the first layer we considered the research approach and exploration of risk issues;

in the second layer we examined the nature of the study, research methods, orientation of risk definitions,

structural elements and the level of implementation; in the third layer the key discriminating elements of each

factor were considered and were further categorized into detailed attributes. Apart from this, we have used a

logical codification scheme employing an alphanumeric code which can assist in quantitative and qualitative

analysis. We have further explored the literature with two very important and practical dimensions of the study,

namely coordination and decision making in an uncertain business environment and implementation of SCRM

for various sectors. The outcomes of these analyses have been presented in the form of propositions. In addition

to describing the contributions of the researchers, this study also provided new insights for practical aspects of

SCRM.

The conclusions of this study have illustrated the importance of adopting a broader view and scope of

coordination strategies in the context of effective implementation of SCRM. It has been argued that

understanding the emerging techniques, including conceptual, analytical and empirical approaches with all the

proposed elements, enable us to tackle better the managerial challenges involved in addressing the risk issues.

This kind of broader view is specifically needed in relation to the kind of managerial challenges faced by a

company operating as a focal firm and having more power in supply chains. As this study has illustrated, it is

not enough to concentrate on developing and sharpening the risk mitigation strategies focusing on one side of

the supply chain and practices. Rather, the company needs to understand and try to influence the entire supply

chain, or more importantly, the nature and progression of the flows across the various interfaces. The

broadening of the scope of SCRM from a company’s internal processes towards the inclusion of external issues

is thus an important managerial challenge.

The review reveals various insights and gaps in the SCRM literature. On comparison of the nature of the

study it is observed that even though the literature has a plethora of work the contribution of prescriptive studies

is significantly lower, which justifies the need for more focused and specific studies, acceptable to industry. We

noticed that the contribution of conceptual studies to SCRM has been higher than that of empirical and

analytical studies. This finding highlights the fact that, as risk management studies are still in a nascent state,

conceptual and theoretical up-grading is still essential to improve the level of understanding of complex risk

issues to provide the strands of effective empirical and analytical studies. It has also been noted that SCRM is

accepted in multiple research fields and the literature reflects a huge variety of works with diversified themes,

issues and approach. The literature reports very few reviews covering the width and depth of the field.

Moreover, as we found that the area is still emerging, more reviews are needed encompassing the changing

trends in methodology, approach and finer elements of risk issues with various perspectives. Thus attempts have

been made in this study to cover the prevalent literature dealing with current research methods to address the

risk issues.

The analysis of orientation of risk definitions suggests that operational aspects related to the demand supply

mismatch and interruption of information, funds or material flow are the most utilized factors to define and

classify risks. Market orientation factors such as customer expectations, market fluctuations, price variability,

competitor moves etc. are also found to be significant to characterize the risk issues. Strategic decision elements

such as outsourcing, single sourcing, degree of leanness in manufacturing, level and type of coordination and

information sharing etc. are also issues of concern but are still not addressed as much as the operational

elements. Moreover, product features such as life cycle, functionality and complexity in design have not been

adequately explored to define the risk characteristics. Thus, including product and strategic perspectives to

define the risks could improve the effectiveness of risk management mechanisms.

On exploration of the structural dimensions of the supply chain it was observed that researchers emphasize

supply side risks more than the demand side. The optimal number of suppliers, delivery reliability, optimal size

of deliveries, relationships and coordination are the key elements that influence the risk management strategies,

but in a changing scenario customer related elements such as demand fluctuations and customer behaviour

should also be included to improve the agility and responsiveness of the supply chain. The implementation of a

risk management program shows that scenario based methods are more common due to their comprehensiveness

to identify the risks, followed by listing methods due to their simplicity. Risk characterization techniques were

found to be more accepted but are still not effective to quantify the elusive and dynamic nature of risks. Further,

on investigation of risk management strategic stances we found that the acceptor stance with redesign of supply

networks is more common than hitting the cause of risk and reshaping the uncertainty sources. After a series of

natural and manmade disruptive events recovery strategies are also being developed with the prime notion of

robustness and resilience.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

It has been noted that empirical studies primarily analyse the supply chain, investigating the impact of

various risk factors on performance determinants, information sharing, collaboration, and e-business practices.

The implications of strategic moves such as outsourcing and lean practices have also been investigated with

specific case studies and survey based statistical analysis. However, as we know the risk issues have strong

perceptive elements and human and organizational behaviour plays a decisive role in managing the risk

situations, behavioural elements such as human/organization risk propensity can be integrated with the

conventional risks models to get more realistic solutions. Moreover, the role of various personality traits,

context and experience can also be incorporated in risk management models. Thus empirical studies

investigating behavioural, technical as well as commercial aspects and their role in decision making will be

more relevant to develop better risk management models.

The literature reflects the dramatic growth in mathematical modelling to analyse the risk issues. Initially

the problems were addressed with linear models but later on stochastic modelling, and multi-agent approaches

have been employed more to analyse the risk issues under simulated environments using artificial intelligence

tools. To deal with supply chain risk issues these models require further improvements. The literature reports

various mathematical models developed to assist planning under uncertainties with number of impractical

assumptions such as known probability distributions and linearization in relationships, which reduce the

acceptability of the model for real life situations. Thus inclusion of deeper risk issues can improve the

effectiveness of mathematical models to a large extent.

It is also necessary to develop coordination strategies considering the actual conditions such as non-ideal

members and heterogeneous risk sharing attitudes. Many times managers have to analyse trade-offs considering

the factors which contradict each other such as redundancy and efficiency. Methods and mechanisms are still

required to analyse these trade-offs in a dynamic business environment with a risk perspective.

We have unified the study and analysed it for coordination strategies under different decision making

environments and implementation issues of SCRM for various sectors. The coordination strategies have been

studied with two decision making scenarios namely centralized and decentralized systems. In a centralized

decision making environment the level of coordination and information sharing among various players is found

to be better but it is also observed that the firms leading the supply chain have the tendency to transfer the risks

to smaller players. However, in a decentralized decision making environment, coordination is found only at the

inter-firm level, which causes conflicting risk perceptions and practices to manage them. Based on the

discussion it can be said that coordination among various partners and appropriate level of information sharing

is essential to improve the overall effectiveness of risk management strategies. Study further reflects the fact that

different industries and sectors have different business environments, opportunities and limitations thus a

common risk management framework may not be effective, that causes the need for specific SCRMs for

diversified industries and sectors.

Thus by employing a detailed taxonomy we have investigated the prevalent SCRM literature focusing on

the research methods adopted and exploration of the risk issues from definition to implementation phases and

specific industry needs and we believe that the trend of growing interest in the field of SCRM will continue and

new avenues will open from the strategic to the operation level with inclusion of new developments in

technology, computing techniques and managerial concerns to effectively manage the risk issues.

Piyush Singhal, Gopal Agarwal, Murali Lal Mittal

APPENDIX: A LIST OF PAPERS WITH CLASSIFICATION CODE

Papers/articles Classification Code Papers/articles Classification Code

Lawrence et al.,1996 A(1)B(1.1)C(5)E(3.1) Li-ping Liu et al., 2007 A(2)B(1.2)D(1.1)

Thomas and Griffin, 1996 A(1)B(.3.1.1.1/B.3.1.2.4)D(

1.2)

Liston et al., 2007 A(1)B(.3.1.1.2/B.3.1.2.4)

D(2.2)

Mason-Jones et al., 1998 A(1)B(1.1)C(1) Mele et al., 2007 A(1)B(.3.1.1.2/B.3.1.2.4)

C(2)D(2.2)

Smeltzer and Sifered, 1998 A(1)B(1.1)C91)D(1.2)E(1.1) Meilin and Jingxian,

2007

A(1)B(.3.1.1.2/B.3.1.2.4)

D(2.2)

Escudero et al., 1999 A(1)B(.3.1.1.1/B.3.1.2.2)E(1

.1)

Ouyang, 2007 A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.2)E(3.3)

Lambros and Socrates,1999 A(1)B(2.2)C(3)D(1.2) Ritchie and Brindley,

2007

A(1)B(1.1)C(2)E(3.2)

Mantrala and Raman, 1999 A(1)B(.3.1.1.1/B.3.1.2.4)D(

2.1)E(2.1)

Serbanescu, 2007 A(1)B(2.2)C(2)E(1.2)

Steven and Ronald, 1999 A(1)B(1.1)C(1)E(2.1) Stephen et al., 2007 A(1)B(.3.1.1.1/B.3.1.2.2)

C(1)E(1.1)

Sislian and Satir,2000 A(1)B(1.1)C(5) Wang and Shu, 2007 A(1)B(.3.1.1.1/B.3.1.2.2)

E(3.1)

Zsidisin et al., 2000 A(1)B(1.1)C(1) Xiao et al., 2007 A(1)B(.3.1.1.1/B.3.1.2.4)

D(2.2)E(2.1)

Hallikas et al., 2002 A(1)B(1.1)C(2)D(1.1)E(3.2) Zhou and Benton Jr, 2007 A(1)B(2.3)C(5)D(1.2)

Korpela et al., 2002 A(1)B(.3.1.1.1/B.3.1.2.1) Bailey and Francis, 2008 A(1)B(1.1)D(2.1)

Gupta and Maranas, 2003 A(1)B(.3.1.1.1/B.3.1.2.2)

D(2.1)E(1.3)

Berg et al., 2008 A(1)B(1.1)E(3.2)

Juttner et al., 2003 A(1)B(1.1)C(3)E(3.2) Braunscheidel and

Suresh,2008

A(1)B(2.2)C(1)E(3.3)

Kut and Zheng, 2003 A(1)B(.3.1.1.1/B.3.1.2.2)

D(2.1)E(1.1)

Carbonneau et al.,2008 A(1)B(.3.1.1.2/B.3.1.2.4)

D(2.2)

Zsidisin and Ellaram,2003 A(1)B(2.2)E(3.2) Chatzidimitriou et al., 2008 A(1)B(.3.1.1.3/B.3.1.2.4)

D(2.2

Chen and Lee, 2004 A(1)B(.3.1.1.1/B.3.1.2.2)

D(2.1)E(3.2)

Choi et al., 2008 A(1)B(.3.1.1.1/B.3.1.2.2)

E(2.1)

Christopher and Lee,2004 A(1)B(1.2)D(1.3)E(3.2) Dani and Ranganathan,2008 A(1)B(1.2)E(1.3)

Christopher and Peck,2004 A(1)B(1.2)D(1.3) Demirkan and Cheng, 2008 A(1)B(.3.1.1.1/B.3.1.2.4)

D(1.2)

Dailun, 2004 A(1)B(2.3) Hong and Sung, 2008 A(1)B(.3.1.1.1/B.3.1.2.2)

C(1)D(1.1)E(1.3)

Finch,2004 A(1)B(1.1)C(3) Hsiesh and Cheng, 2008 A(1)B(.3.1.1.1/B.3.1.2.4)

D(2.2)E(2.1)

Giuniperoand Eltantawy,

2004

A(2)B(1.2)C(2)D(1.2)E(2.1) Hung and Sungmin,2008 A(2)B(1.2)C(3)E(3.2)

Goankar andViswanadham,

2004

A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)E(2.2)

Kenett and Raphaeli,2008 A(1)B(1.2)C(5)E(3.1)

Jeng,2004 A(1)B(1.2)D(1.3) Khan and Greaves, 2008 A(2)B(2.1)C(3)E(3.2)

Lau et al.,2004 A(1)B(.3.1.1.2/B.3.1.2.2)

D(1.1)

Kim and Park,2008 A(1)B(1.2)C(5)

Norrman and Jansson,2004 A(1)B(1.2)E(3.3) Lee, 2008 A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)E(2.2)

Yang et al., 2004 A(1)B(1.1)D(2.1) Levary, 2008 A(1)B(.3.1.1.1/B.3.1.2.1)

C(4)D(1.2)

Blackhurst et al.,2005 A(1)B(2.2)C(1)E(3.3) Li and Kuo, 2008 A(1)B(.3.1.1.1/B.3.1.2.2)

C(2)E(3.2)

Donk and Vaart, 2005 A(1)B(2.3)D(2.1) Manuj and Mentzer, 2008 A(1)B(1.2)C(5)E(3.2)

Hendricks and Singhal, 2005 A(1)B(2.2)C(5) Moghadam et al., 2008 A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)E(2.2)

Juttner, 2005 A(1)B(2.2)C(1)E(3.2) Neureuther and Kenyon,

2008

A(1)B(2.2)C(2)D(2.1)

Kleindorfer and Saad, 2005 A(1)B(1.2)C(1) Ohbyung et al., 2008 A(1)B(.3.1.1.3/B.3.1.2.2)

D(2.2)

Abbas et al., 2006 A(1)B(1.1)D(1.1) Ritchie and Brindley,2008 A(1)B(1.2)C(3)E(3.2)

Bogataj and Bagataj, 2006 A(1)B(.3.1.1.1/B.3.1.2.2)

C(5)E(2.1)

Sohn and Lim, 2008 A(1)B(.3.1.1.2/B.3.1.2.2)

C(4)D(2.2)

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Brun et al., 2006 A(1)B(1.2)C(1)E(1.1) Szwejczewski et al., 2008 A(1)B(1.2)C(4)E(2.1)

Cigolini and Rossi, 2006 A(1)B(1.2)D(1.1) Tapiero and Grando,2008 A(1)B(1.2)C(5)

Faisal et al., 2006 A(1)B(2.1)C(1) Thomas and David,

2008

A(1)B(.3.1.1.2/B.3.1.2.4)

D(1.3)

Gaudenzi and Borghesi,

2006

A(1)B(.3.1.1.1/3.1.2.1)C(3)

E(2.2)

Wong and Arlbjorn, 2008 A(1)B(2.2)C(2)E(3.3)

Ojala and Hallikas, 2006 A(1)B(1.1)C(3)D(1.2) Zhao et al., 2008 A(2)B(2.1)C(1)

Peck, 2006 A(1)B(1.2)E(3.3) Zsidisin et al., 2008 A(2)B(2.2)C(3)E(3.2)

Shockley and Ellis,2006 A(1)B(2.2)C(1)D(1.1) Haisheng et al., 2009 A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)

Tang, 2006 A(1)B(1.3) Jiang et al., 2009 A(1)B(1.2)C(5)

Tang, 2006b A(1)B(1.1)E(3.3) Knemeyer et al.,2009 A(1)B(1.2)C(4)E(3.3)

Wu. et al., 2006 A(1)B(.3.1.1.1/B.3.1.2.1)

C(1)D(1.1)

Michael and Nallan,2009 A(1)B(1.2)C(1)E(3.3)

WagnerandBode,2006 A(1)B(2.2)C(3)D(1.1) Narasimhan and

Talleri,2009

A(1)B(1.2)C(5)E(3.1)

Aggarwal and Ganeshan,

2007

A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)E(2.2)

Neiger et al. ,2009 A(1)B(1.2)E(1.1)

Blackhurst et al.,2007 A(1)B(2.3)C(1)D(1.2)

E(3.1)

Oke and Gopalakrishnan,

2009

A(2)B(2.1)C(3)

Boute et al., 2007 A(1)B(.3.1.1.1/B.3.1.2.2)

D(2.1)E(2.1)

Ponomarov and Holcomb,

2009

A(1)B(1.3)

Devaraj et al.,2007 A(2)B(2.2)C(3) Rao and Goldsby , 2009 A(1)B(1.3)

Faisal et al., 2007 A(1)B(2.1)C(5)E(3.2) Sarkar and Mohapatra, 2009 A(1)B(.3.1.1.2/B.3.1.2.2)

D(1.2)

Forme et al., 2007 A(2)B(1.2) Sodhi and Tang, 2009 A(1)B(2.2)C(2)D(2.1)

Goh et al., 2007 A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)E(3.2)

Trkman and

McCormack,2009

A(1)B(1.1)D(1.2)E(3.3)

Haan et al.,2007 A(1)B(2.2)C(2) Vanany et al.,2009 A(1)B(1.3)

Harland et al.,2007 A(1)B(2.2)C(5)E(3.1) Boin et al., 2010 A(1)B(1.2)C(3)E(3.2)

Jammernegg and

Reiner,2007

A(1)B(.3.1.1.2/B.3.1.2.4)

C(1)D(2.2)

Giannaikis and Louis, 2010 A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)E(2.2)

Kersten et al.,2007 A(1)B(1.1)C(1) Ellis et al., 2010 A(1)B(2.2)C(1)E(3.3)

Tuncel and Alpan, 2010 A(1)B(.3.1.1.2/B.3.1.2.4)

C(1)D(2.2

Ketzenberg et al.,2007 A(1)B(1.2)C(5) Tang and Musa, 2010 A(1)B(1.3)C(1)

Khan and Burnes,2007 A(1)B(1.2)E(3.2) Wanger and Neshat, 2010 A(1)B(.3.1.1.1/B.3.1.2.2)

D(1.1)E(3.2)

Kocabasoglu et al., 2007 A(1)B(2.2)C(1)E(3.2) Wuand Olson, 2010 A(1)B(1.2)C(5)

Lee et al., 2007 A(2)B(2.2)D(1.1)

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