Electronic copy available at: http://ssrn.com/abstract=1125496

*Corresponding author. Phone: 508-793-7659, Fax: 508- 793-8822. Email: jsarkis@clarku.edu (Joseph Sarkis). This work is supported by China Scholarship Council and the National Natural Science Foundation of China Project (70772085).

A Joint Location and Outsourcing Sustainability Analysis for a

Strategic Offshoring Decision

Yijie Dou

School of Management Dalian University of Technology

Dalian, Liaoning Province 116024, PR China Phone: (86) 411-8470-7351 Fax: (86) 411-8470-8342

Email: Romaninhim@hotmail.com

Joseph Sarkis*

Graduate School of Management Clark University 950 Main Street

Worcester, MA 01610-1477 Phone: 508-793-7659 Fax: 508- 793-8822

Email: jsarkis@clarku.edu

April, 2008

Electronic copy available at: http://ssrn.com/abstract=1125496

1

A Joint Location and Outsourcing Sustainability Analysis for a

Strategic Offshoring Decision

Abstract

With economic globalization and the emergence of extended enterprises derived from interrelationship among organizations, there has been a steady increase in offshoring outsourcing activities. Subsequently, the strategic importance of offshoring decisions is important. Traditional offshoring decisions mainly emphasize outsourcee (supplier) selection problems, with their focus upon economic factors. Sustainability, which has recently been seen as a competitive necessity in most industry, rarely enters into the modeling or discussion. Furthermore, additional and integrated facility location factors need to be involved into the offshoring decision process. To help integrate these factors and concerns, this paper constructs a model for evaluation and selection of various offshoring alternatives by simultaneously considering facility location factors, supplier selection metrics, and sustainability factors. The model allows for input from a variety of managerial decision making levels and involves the dynamic perspectives of the competitive environment in evaluating process. An empirical case illustration is applied to demonstrate the efficacy of the model. The paper closes with a discussion of managerial implications and an outlook on aspects for further research.

Keywords: Offshoring decision; Facility location; Supplier selection; Sustainability; Analytic network process; Dynamic perspective

1. Introduction

The strategic importance of outsourcing in today’s business environment has been

recognized by managers and scholars (Quinn and Hilmler 1994, Quinn 1999, Nellore and

Söderquist 2000, Globerman and Vining 2006). In the context of economic globalization

and increasing organizational and technological capacity of companies (especially

multinational companies, (MNCs)), offshoring, i.e. offshore outsourcing, has recently

received signficant attention (Farrell 2005, Levy 2005). Subsequently, one critical

challenge faced by managers is a need for systematic analysis of the strategic offshoring

decision. Recent offshoring decisions by organizations and researchers has focused on

strategic outsourcing subcontractor selection. While subcontractor selection is mainly

2

based on factors such as cost, quality, delivery and flexibility; sustainability factors are

traditionally not given significant emphasis (Brown, 2008). Also, a more holistic

offshoring and outsourcing decision needs to take into account other metrics like facility

location factors, rather than a sole consideration of supplier or subcontractor selection

factors. To assist in the offshoring and outsourcing decision process we present a

strategic decision model that incorporates sustainability factors while simultaneously

considering facility location factors and supplier (subcontractor) selection factors.

Strategic decisions need to integrate both tangible, intangible, strategic and

operational factors into an analysis (Sarkis and Talluri 2002). Current literature in the

outsourcing decision does emphasize the importance of intangible factors especially those

that may utilize managers’ perceptions (Carter et al. 2008). This paper introduces a

framework based on Analytical Network Process (ANP) that could effectively and

synthetically incorporate broad-based set of relevant factors into the strategic offshoring

decision. ANP is a generalized form of the widely used multi-criteria decision making

technique the Analytical Hierarchy Process (AHP) (Saaty 1980). Given the limitations of

AHP such as sole consideration of one way hierarchical relationships among factors,

failure to consider interactions among the various factors and “rank reversal”, ANP is

applied as a more realistic modeling method for the offshoring and outsourcing decision,

albeit the disadvantage of ANP may arise when the number of factors and respective

interrelationships increases, requiring much more effort by analysts and decision makers

(Sarkis and Talluri 2002, Jharkharia and Shankar 2007).

Overall, the purpose and contribution of this paper is to introduce a

comprehensive set of decision factors that may be utilized for offshoring and outsourcing

3

decisions, construction of a strategic ANP decision model with a joint analysis of a

facility location, outsourcing, and sustainability-oriented decision. Firstly, we provide a

brief discussion on the offshoring decision process helping to position our paper in the

research literature further identifying its contribution. A description of different

offshoring decision models is summarized, which sets the stage for the need of a holistic

offshoring decision model. We then introduce and summarize various decision factors

and incorporate the factors into an ANP decision framework. The following section paper

proposes a case application of the proposed model. The results will be discussed and

managerial insights, implications, and possible directions for future research are put

forward in the conclusion.

2. Offshoring and Outsourcing Decisions, Factors and Models

2.1. The decision process

In recent years, outsourcing has been adopted as an important strategic option for

organizations seeking to simplify their operations while focusing on their core

competencies (Ash 2007). Offshoring is a specific form of outsourcing, specifically

outsourcing abroad (i.e. offshore outsourcing). It has evolved into an important source of

business renewal and corporate transformation (Li et al. 2008). There are a host of

descriptive models identifying the phases of an entire outsourcing process and its linkage

to strategic planning (Heikkilä and Cordon 2002, McIvor et al. 1997, Gottfredson et al.

2005, Greaver 1998, Grossman and Helpman 2005). Using this literature we can arrive

at an offshoring decision process that can be separated into three distinctive managerial

decision phases: pre-evaluation, evaluation of various alternatives, post-evaluation. Pre-

4

evaluation includes competence analysis, total cost analysis, identifying what to

outsource, and related information searching. The third stage post-evaluation pertains to

contract negotiating, relationship management, and dynamic adjustments. Our model and

discussion will focus on the second step: evaluation of various alternatives.

According to the OECD (2007) offshoring alternatives include offshoring to

newly constructed plants or companies (e.g. foreign direct investment (FDI)), offshoring

to an existing location, and outsourcing to a subcontractor abroad not affiliated with the

organization. Under this categorization, evaluation of various offshoring alternatives may

include evaluation and choice of offshoring locations using currently or newly built

affiliated (owned) subsidiaries or offshoring to external subcontractors abroad.

2.2. Models

The current literature on offshoring decision models can be classified into two groups:

the first group focuses on the firm’s choice of organizational forms (e.g. Mclaren 2000;

Grossman and Helpman 2002, 2004, 2005; Antràs and Helpman 2004; Antràs et al.

2006), which rests in the domain of international trade theory. The first group of models

is chiefly at a macroeconomic level and centers on the organizational form choice

between outsourcing and integration, and the location choice between at home and

abroad. While the macro-level models of offshoring decisions are particularly useful in

understanding the macroeconomics and broad choice of trade and foreign direct

investments (FDI), they do not address issues of the offshoring decision for a specific

firm. The second stream of models is at the firm level, few models can be found in this

second group (Ruiz-Torres and Mahmoodi 2008). Most of the current models focus on

outsourcer evaluation and selection (de Almeida 2007, Araz et al. 2007, Cao and Wang

5

2007), which are essentially theoretically close to supplier selection decision models

(Sarkis and Talluri 2002, Narasimhan et al. 2006). Locational factors are typically not

taken into account by these models. Although some models (e.g. Chan and Kumar, 2007)

do include some location factors like political stability and economic condition when

discussing global supplier selection criteria. Nevertheless, these location models’ facility

decision factors are limited. Overall, we have found that traditional firm-level offshoring

supplier selection models fail to holistically incorporate facility location factors while

facility location in the offshoring decision research seem to neglect supplier selection

factors.

The location of economic activities has been an important topic for economic

analysis since the seminal works of Alfred Weber (the impact of transportation costs on

the location decision), Johann Heinrich Von Thünen (land use model), Walter Christaller

(Central Place Theory) and William Alonso (Central Business District) (Carod 2005).

Although the institutional approach of facility location theory maintains the important

role of competitors, customers and suppliers in facility location decisions (Hayter 1997),

research on facility location decisions rarely incorporate supplier selection factors. Very

little research has modeled the joint offshoring decision (i.e., simultaneous evaluation of

offshoring location, different existing cross border affiliates, and various subcontractors

abroad). Hence a systematic evaluation model that integrates the factors of facility

location and supplier selection in offshoring decisions can contribute to better

management modeling and decision making.

The models for facility location and supplier selection decisions utilize a variety

of methodologies ranging from simple scoring and matrix methods to more advanced

6

mathematical programming and game modeling approaches. ANP falls some where in

the middle of these techniques, not requiring the complexity of mathematical modeling

for decisions, but offering a more robust solution than simple scoring methods (Sarkis

and Sundarraj 2000). Part of this complexity arises from the relationships and

interlinkages among decision factors. These interdependencies among factors can be

explicitly considered through pairwise comparisons in ANP modeling. The pairwise

comparisons used as the elemental inputs to ANP can incorporate intangible factors like

managers’ perception by allowing decision-makers to integrate their measurement of

relative importance among the factors. Further, ANP’s structuring factors in a

hierarchical (or clustering/network) relationship aids managers comprehension of the

various linkages among factors, especially strategic performance metrics that need to be

linked to operational level measures. To this end, the paper applies a model that

effectively considers facility location factors, supplier selection factors and the complex

relationship among them in strategically evaluating joint offshoring and outsourcing

alternatives. Also, as shown in the following section, we integrate relevant location,

supplier selection and sustainability factors in developing our model.

2.3. Factors

An in-depth offshoring decision making (OECD 2007) needs to simultaneously consider

both the facility location factors and outsourcee (subcontractor/supplier) selection factors.

As mentioned above, the outsourcee selection problem has strong similarities, if not

identical, to the supplier selection problem. Hence, the extant literature on supplier

selection can contribute to the construction of outsourcee selection factors. For the

7

remainder of our discussion the term “supplier selection factors” will be used as

representative of “outsourcee selection factors” or “subcontractor selection factors”.

2.3.1. Facility location factors

International facility location decisions have attracted much attention in recent years

(Mudambi 1995, Brush et al. 1999, Prasad et al. 2000). Three approaches have been

employed to explain industrial facility decisions: neo-classical, behavioral, and

institutional approaches (Hayter 1997). Subsequently, the facility location decision

primarily emphasizes economic, behavioral and institutional factors.

The neo-classical approach maintains that firms choose facilities to locate as a

result of cost minimization and profit maximization. Such location factors like labor

costs, transportation costs, market size and locational business climate are of central

importance. From this view, rationality and perfect information of decision makers are

often assumed, which are typically unrealistic assumptions. Realizing the limitation of

these assumptions, behavioral approaches consider firms as agents with bounded

rationality and imperfect information. The location of a facility is interpreted as a

decision-making process, and the key behavioral explanation of facility location is the

firms’ perception and evaluation for an ‘information bed’ (Hayter 1997). ANP can aid

this decision-making environment by allowing the incorporation of managers’

perceptions into the decision making process. Both neo-classical and behavioral

approaches have been subject to criticism due to their implicit assumption of a static

environment (Brouwer et al. 2004). Consequently, in the institutional approach, the

facility location decision is described in terms of factors that formulate strategy and

emphasis is placed upon strategic issues like competition, current facilities and market

8

penetration (Hayter 1997). A broad set of traditional locational factors from the literature

(Hayter 1997, Sarkis and Sundarraj 2002) are shown in Table 1.

Table 1 about here

2.3.2. Supplier selection factors

Supplier selection can impose a direct and significant impact on a firm’s business

performance (Kannan and Tan 2002). Suppliers can serve as a source of competitive

advantage (Simpson et al. 2002). Accordingly, supplier selection is a critical strategic

organizational challenge. While quantifiable criteria like cost, quality, delivery are

routinely applied to evaluate suppliers (Sarkis and Talluri 2002), other areas, containing

more difficult-to-quantify (qualitative, intangible) factors such as supplier-customer

relationship development may prove to be even more significant (Kannan and Tan 2002,

Simpson et al. 2002). Overall, supplier selection factors can be grouped into two

categories or clusters: strategic performance metrics and organizational factors. The

strategic performance metrics cluster focuses on five major metrics including cost,

quality, time, flexibility, and innovativeness, which have been identified as competitive

priorities (Wheelwright and Hayes 1985). The organizational factors cluster consists of

three sets of factors: culture, technology, and relationships, which focus less on the

competitive factors and more on the capabilities and characteristics of the organizations

that will form the partnership. A summary of these clusters adapted from Sarkis and

Talluri (2002) and Chan (2003) is shown in Table 2.

Table 2 about here

9

Among the conventional facility location research neither environmental nor

social sustainability factors are emphasized. The environmental consideration in location

research focuses on environmental regulations, i.e. “pollution havens” seeking behavior

(Daly 1995) and environmental amenity (Hayter 1997). Multi-national companies

offshoring location practices to developing countries have been criticized due to taking

advantage of low-wage workers, lax environmental regulations, weak workplace

standards, and contributing to social and environmental degradation (Doh 2005).

Companies seeking to manage these reputational considerations require a systematic

integration of sustainable factors into firms’ offshoring location decision-making

necessary. Increasingly more authors are addressing supplier selection issues in the light

of environmental aspects (Min and Galle 1997, Noci 1997, Handfield et al. 2002,

Humphreys et al. 2003, Sarkis 2006). Much of this literature has focused upon

environmental issues integration in the supplier and location decision processes. There

still exists a necessity to incorporate other, social, sustainability factors such as social

equity and employee health. A comprehensive overview of sustainability factors in

facility location and supplier selection will be presented next.

2.3.3. Sustainability factors

Historically, consideration of environmental issues in facility location decision research

has focused upon basic environmental aesthetics and compliance-oriented environmental

regulation (Hayter 1997). Correspondingly the supplier selection has seen efforts to

incorporate environmental factors into supplier selection. Much of the research has

shown that environmental concerns are chiefly associated with conformance to

environmental regulations and cost reduction orientation (Min and Galle 1997).

10

Sustainable development and sustainability is frequently interpreted as a synthesis

of economic, environmental and social development (Gauthier 2005). Even though

environmental considerations in facility location and supplier selection decisions have

existed, a more systematic inclusion of other sustainability factors should be incorporated

into the offshoring decision. The previous dual concerns of economic and environmental

aspects in offshoring decisions need to be expanded into a triad that involves social

factors, i.e. human rights abuses, child labor, and irresponsible investment. International

companies are increasingly acknowledging the importance of social issues like human

rights, labor and corruption (Rivoli 2003). Consequently, consideration of both

environmental and social factors need to be at the forefront of companies’ offshoring

decision agenda.

In terms of the supplier selection decision, environmental and social factors are

introduced from a firm level perspective (versus a regional locational perspective).

Environmental factors can be categorized into two categories: environmental

performance and environmental practices. According to Levy (1995), environmental

practices refer to policies and procedures, such as monitoring discharges and periodical

audits; while environmental performance is in reference to resource consumption and

pollution production. A well-accepted categorization of environmental practices

distinguishes between “pollution prevention” and “pollution control” (Gil et al. 2001).

Pollution control, pollution prevention and management system have also been used to

comprehensively describe environmental practices (Klassen and Whybark 1999). Table 3

summarizes these organizational level environmental factors (sources include Klassen

and Whybark 1999 and Gauthier 2005).

11

Table 3 about here

Gauthier (2005) groups social factors into two categories: internal social criteria

and external social criteria. Internal social criteria refer to employment practices such as

labor sources, gender diversity and occupational health and safety at work. External

social criteria regard the relationship with contractual stakeholders like suppliers and

customers, and relations with other stakeholders like local communities and NGOs. Table

4, summarizes the various social factors and sub-factors that may be utilized in supplier

selection decisions (sources of these factors include Gauthier 2005, GRI 2008,

Labuschagne et al. 2005, and Presley et al. 2007).

Table 4 about here

For facility location groupings and decisions, sustainable considerations would be

at the broader locational community and regional level of analysis. Sustainability and

sustainable development issues pertaining to cities, towns and settlement patterns has

created a variety of labels including sustainable cities, sustainable urban development,

and sustainable communities (Beatley 1998). The ultimate purpose of sustainable

development pertains to achieve the integrative combination of environmental symbiosis,

economic growth, and social equity (Lin and Lee 2005). Common features of sustainable

communities have been identified in the literature, including: compact, higher-density

development, more efficient use of land and space; the “greening” of communities with

greater emphasis on open space; highlighting public transit, and generating mixed-use

environments which are more amenable to walking and less dependent upon autos; and

efficient resource consumption, low pollution (Beatley 1998). Table 5 and Table 6

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respectively introduces these environmental and social factors for locational decisions

(sources include Bossel 1999, ESI 2008, Lin and Lee 2005, UN 2007).

Tables 5 and 6 about here

2.4. The planning horizon

The planning horizon is pertinent to the dynamic and competitive nature of organizations.

Both facility location researchers (e.g. Brouwer et al. 2004) and supplier selection studies

(e.g. Sarkis and Talluri 2002) have recognized the importance of incorporating a dynamic

perspective to decision-making. Including planning horizon factors into a decision model

will aid to involve strategies of the organization, which are critical for short-term and

strategic decisions. For brevity while maintaining the essence of a planning horizon

consideration, this paper applies the dichotomous categorization of short-term (ST)

versus long-term planning horizons (LT).

3. The ANP model

The ANP methodology we introduce in this paper is overviewed in Figure 1. The initial

step focuses on identification and selection of the most salient factors to be evaluated by

the organization. In our case illustration we complete we have identified the salient

factors with an asterisk in Tables 1-6. This determination of salient factors, typically

reduction of a larger set of factors, often needs to be executed so that the number of

pairwise comparison (the step after formulation of the decision network) is practicable.

The next step pertains to the construction of a decision network. Following the elicitation

of pairwise comparisons is the calculation of relative importance of the factors. A

formulation of a supermatrix and the calculation of stabilized weights from the

13

supermatrix are the final two steps to arrive at a solution to an evaluation of a set of

alternatives. A sensitivity analysis may need to be completed, in order to determine the

robustness of the alternative weights and whether to accept the result or to make

adjustments to the set of factors through a feedback loop.

4. Illustrative case

To detail the proposed methodology we provide an illustrative case example. Suppose a

company A is seeking to make an offshoring decision. Company A competes on factors

that include cost, quality, delivery time, and flexibility. In recent years, company A has

recognized the growing urgent competitive status that it is facing in its significant market

C. Top level managers of Company A are considering a comprehensive offshoring

decision that will involve simultaneous consideration of facility location factors and

supplier selection factors.

We can assume that some filtering process to reduce the number of alternative

locations and suppliers has been completed, to obtain an alternative set, an initial

threshold evaluation is applied. A multi-staged alternative choice approach is shown in

Figure 2 (in addition to the ANP methodology, which executed to identify a final

alternative choice). As seen in Figure 2, four alternatives are considered in ANP model

illustration in this paper: Affiliate A in Location A (AAA) which could be considered an

FDI and Supplier A in Location A (SAA), Supplier B in Location B (SBB), and Supplier

C in Location B (SCB), which would be considered a complete outsourcing to external

subcontractors. The four alternatives will be included in the final decision network.

4.1. Determination of salient factors

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An original list of facility location and supplier selection factors, adapted from previous

literature and models, has been formulated and shown in Tables 1-6. Given the specific

business environment faced by Company A, salient factors will need to be determined.

Determination can be completed through many filtering and grouping tools such as

affinity diagrams. This step is implemented to help keep the number of pairwise

comparisons among the factors at a manageable level. The salient factors yielded in the

process are shown by an asterisk in Table1-6. Only the highest level factors are

incorporated for each of the clusters. The sub-factors can still play a role by helping

define the factors selected. If there is a cluster of factors that are deemed especially

important by management, they could be included in the analysis. In addition, the

feedback from earlier ANP iterations may help further refine the salient factors to be

included in the analysis.

4.2. Formulation of decision network

The construction of the hierarchical decision network is one of the important determinants

of the amount of complexity that will be required by the analysis. After formation of

clusters of salient factors, their relationships amongst each other and even within each other

will need to be determined. An initial discussion with the decision makers will be required

(or an evaluation of previous literature) to help determine the relationships. There are

formal tools available to aid in determining these structures and relationships such as

interpretative structural modeling (Sarkis et al. 2008). A proposed high-level ANP

decision network is shown in Figure 3. Central in this figure is the objective of

strategically selecting an alternative. Two sets of strategic clusters, facility location factors

15

and strategic supplier selection factors, form the core of this decision network and are a

culmination of factors in Tables 1-6.

Three clusters (i.e. the locational factors cluster in facility location factors cluster

set, and the strategic performance metrics and organizational factors clusters in the strategic

supplier selection metrics cluster set) are influenced by and influence the planning horizon

factors cluster. The two-way arrows among the levels mean there are interdependent

relationships amongst these clusters. For example, the first directional relationship would

determine if the locational factor is most important over a ‘short-term’ planning horizon,

which factor over the ‘long-term’. The other directional relationship would question a

specific locational factor’s relative importance in the short-term and the long-term. In the

strategic supplier selection metrics cluster set of Figure 3, the strategic performance metrics

cluster shows an internal interdependency (a curved arrow), which indicates that factors

within this cluster will influence each other and the impact of these factors amongst

themselves need to be considered in the evaluation process. For example, cost (a strategic

performance metric) may be influenced by other strategic metrics like quality factors.

Another important interdependency is the cross set relationships of social factors

and environmental factors in each of the cluster sets with corresponding social and

environmental clusters. The environmental and social factors for facility location cluster

set are influenced respectively by the social and environmental factors in the strategic

supplier selection cluster set, and vice versa. For example, the “consumption and

production patterns” location environmental factor may be variably influenced by supplier

environmental dimensions such as “resource consumption” or incorporating an

“environmental management system.” Conversely, supplier environmental dimensions

16

may be variably influenced by environmental location factors such as “environmental

health requirements” and “ecosystem vitality”.

Each of the alternatives, shown in the alternatives cluster, will be evaluated on each

of the various factors. These relationships are shown by the one-way arrows between the

alternatives selection set and the other clusters.

The letters identifying each of the arcs represent specific sub-matrices within the

supermatrix. A more detailed set of factors within the decision network, with those

specifically identified for each of the clusters, appears in Figure 4.

4.3. Elicitation of pairwise comparison

Pairwise comparisons will need to be elicited for this model. Typically, a team of decision

makers will need to be included in this type of strategic decision making exercise. The

team should probably include managers from a variety of functions including operations,

engineering, procurement, marketing, human resources and environmental areas, since

factors in each of these organizational functional areas will be evaluated.

Pairwise comparison questions examples include the following:

1) To assess the importance of the factors to the decision an example pairwise

comparison question would be: "with respect to the overall decision, how

much more important is Strategic Locational Issues (SI) when compared to

Labor Factors for a location (LF)?"

2) To assess the impact on a cluster from other clusters (interdependencies

among clusters) an example question would be: "How much more important

is the impact of “Cost” than “Quality” in the “Short Term” for the

organization?"

17

3) Pairwise comparison questions regarding the impact of the components

within its cluster set: For example for strategic performance metrics relative

importance on other strategic performance metrics, "How much more

influence does “Quality” have on “Cost” when compared to “Flexibility”?”

4) Pairwise comparison questions regarding the relative performance of

alternatives on the various factors: for example "With respect to Community

Factors (CF), how much better does AAA perform when compared to

SAA?").

These questions are usually answered in an anchored range of descriptions from

Extremely more (a value of 9) to Extremely less (a value of 1/9) (see Saaty 1980).

The number of clusters can be determined by knowing the number of factors

within a cluster and the various relationships they will be evaluating. In the example, we

can assume that if J represents the set of clusters, Kj (j ∈ J) the set of components within

cluster j, and L represents the set of alternatives, then the number of PWC’s can be

obtained by expression (1). In this case, there are number of 603 pairwise comparison

questions.

|J|(|J|-1)/2 + |J|(|J|-1)(|J|-2)/2 + ∑∈

−

Jj2/))1|K(||K(| jj + ∑∑

∈

−

∈ jKk2/))1|L(||L(|

Jj (1)

An example completed pairwise comparison for facility locational factor items based on

the overall organizational objective strategic alternatives selection (the controlling factor)

is shown in Table 7. An example pairwise comparison question for this matrix is: “How

much more important is the Accessibility Factor (AF) than the Community Factor (CF)

for the selection of an offshoring alternative?” In this example, we put in the value “4”

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in Table 7 which means that management responded with AF as moderately more

important than CF.

4.4. Calculation of relative importance of the factors (“weights”)

Once the pairwise comparisons are completed, the local priority vector w (defined as the

eigenvector) is computed as the unique solution to:

,max wAw λ= (2)

Where λmax is the largest eigenvalue of A. The solution for the eigenvalues and relative

importance weights may be calculated from Web HIPRE3+ (Mustajoki and Hämäläinen

1999), an Internet interactive software available for decision analysis

(http://www.hipre.hut.fi/).

The results of the pairwise comparison matrix for the strategic performance

cluster (Table 7) show that strategic issues factor (SI) was perceived to be the most

important locational factor (0.273). The relative importance weights of this matrix are

then introduced into the supermatrix at a later stage.

4.5. Formulation of supermatrix from the weights

Table 8 shows the structure of the supermatrix for the ANP decision network that is

considered in the case. In Table 8, the second row (and column) represents the objective

or “decision” element (i.e. Strategic Offshoring Alternatives Selection). The next eight

rows (and columns) represent the clusters (i.e., planning horizon, locational factors in

facility location decision, social factors in facility location decision, environmental

factors in facility location decision, social factors in supplier selection, environmental

factors in supplier selection, strategic supplier performance factors, and organizational

factors in supplier selection), and the final one row (and column) represents the

19

alternatives cluster. Finally, to facilitate convergence (Section 4.6 below), Saaty (1996)

recommends the addition of the interdependence of each alternative on itself. Thus an

identity matrix (I) is included in the supermatrix for the alternatives cluster. The letters in

the cells are submatrices and match the letters on the arcs in Figure 3.

All the salient factors in this decision network (41 total factors in all) and their

respective relative importance weights are shown in Table 9. Notice that in Table 9 the

bolded numbers (also representing submatrix “B” from Table 8) are the same as the

relative importance weights for the locational factors on the overall offshoring decision

calculated in Table 7. This is the original unadjusted supermatrix that will be used in the

evaluation of the alternatives.

4.6. Calculation of long-term (“stable”) weights from the supermatrix

To arrive at the final solution, a set of stable weights will need to be determined. To do

this we initially have to normalize the supermatrix in Table 9 -- divide each element by

its column sum -- so that each column adds to one (making the supermatrix “column

stochastic”). This will help to guarantee convergence to a stable set of weights. The

normalized supermatrix is then raised to a significantly large power. This execution

provides the converged (or stable) weights of the elements of the network on one another,

similar to a Markovian analysis. The numbers of interest in the resulting supermatrix are

weights of the alternatives on the decision, i.e., the last 4 rows of the first column. The

final weights of the four alternatives, which were obtained after raising the supermatrix to

the 64th power, are WALT, OBJ = (0.188, 0.293, 0.28, 0.239). These results show that the

20

second alternative, Supplier A in Location A (SAA) would be the best overall location

and supplier to be selected.

4.7. Completing a sensitivity analysis

There is no one approach in applying sensitivity analysis within ANP. Typically, some

form of perturbation to the initial set of weights of one factor or cluster of factors can be

made to determine the sensitivity of the solution. A large supermatrix, such as this one,

with many existing relationships will usually result in relatively little change (a robust

solution) to the final results. The sensitivity analysis will inform us if our solution is

robust. To apply a sensitivity analysis evaluation to the decision network in Figure 3, we

will perturb sub-matrix M of Table 8 (weights of the PH cluster with respect to LF

cluster), other sub-matrices can be chosen.

To complete this analysis we specifically alter the weights of ST with respect to

various factors of LF cluster in the perturbation sub-matrix M. We do with three extreme

situations: the weight of ST with respect to all factors of LF cluster is 0 (represented by

WST=0), the weight of ST with respect to all factors of LF cluster is 0.5 (represented by

WST=0.5), and the weight of ST with respect to all factors of LF cluster is 1 (represented

by WST=1). Correspondingly, we have WLT=1 (when WST=0), WLT=0.5 (when WST=0.5),

and WLT=0 (when WST=1). To complete the analysis, the three perturbations are

introduced into the ANP supermatrix Table 9, the resulting supermatrix is normalized,

and then raised to a large power to obtain the final set of weights for each of the

perturbations (see Table 10). The results show that all levels the rank ordering of the four

alternatives has no changes (SAA is the best constantly, while SBB the second, SCB the

21

third, AAA the fourth), and the values of the four alternatives fluctuate very little.

Therefore, the original ANP solution WALT, OBJ

= (0.188, 0.293, 0.28, 0.239) can be

viewed as being robust. Additional perturbations, holding the remaining items constant

show similar results. Yet, additional evaluations may be completed by altering the

various factors and alternatives that should be considered. We will not do that at this

stage.

5. Managerial issues and implications

The following managerial implications can be derived from the current study.

1) Given that longtime criticism has been imposed upon international companies for

the significant negative environmental and social effect of their offshoring

activities, it necessitates the incorporation of sustainability factors into offshoring

decision-making. In practice, besides the sustainability factors, the simultaneous

consideration of facility location factors and supplier selection metrics provides

managers more systemic and comprehensive insight into the whole offshoring

situation. ANP is valuable not only for the final result, but also the various

elements of the process including determining salient factors, the relationships

among the factors, and the relative importance determinations and discussions that

are included with them. That is, sometimes the journey for arriving at the decision

is just as important as the final decision.

2) Since managers and their inputs are critical to this the evaluation process, too

many alternatives, factors and relationships can easily cause fatigue. Therefore, an

initial satisficing (threshold approach) (see Figure 2) and filtration of factors (i.e.

22

choice of salient factors, see section 4.1) is needed to make the decision-making

process less complex and more efficient. With 603 pairwise comparisons that

have to be completed the analysis may become too cumbersome. Dividing up the

discussion into phases over multiple days or by different groups may mitigate

some of the fatigue. But, a useful aspect of this analysis is that after an initial

application of the model, future iterations may only require changes in some of

the factors and relationships. That is, some scores will change very little over

time.

3) The technique provides a ranking of the final alternatives, which provides a

measure for performance gaps among various offshoring alternatives, which the

management can use effectively. For example, a comparison and evaluation

between affiliates and suppliers is captured, which may help create some

competition among affiliates and suppliers who may wish to benchmark their

organizations against each other. Further, the results can provide a negotiating

tool by the buyer to encourage the corresponding performance improvement of

unselected candidates.

6. Summary and conclusion

An ANP model for strategic offshoring decisions has been proposed in this paper. The

selection process effectively considered strategic, operational, tangible and intangible

measures; facility location factors and strategic supplier selection factors are also

incorporated simultaneously into the evaluation process. In addition to these typical

business and economic factors, sustainability factors including both environmental and

social sustainability metrics and factors were introduced. The model also integrated the

23

dynamic aspects of the competitive environment in evaluating various offshoring

alternatives. A case illustration is applied to demonstrate the applicability of the model. It

is shown that to obtain precise and accurate input from various managerial decision-

making levels in implementing the proposed methodology is significant.

There are a variety of managerial implications, some of which we touched upon,

related to the methodology proposed. The technique has significant flexibility to also act as

a benchmarking and process improvement tool, for supplier negotiation purposes, and

decisions regarding supplier development initiatives.

Even with all the advantages of this approach, there are some limitations and also

room for further research. The first major issue is that the example provided here is only

exemplary. An actual application to help validate the approach is necessary. We have

made some suppositions on the effort and complexity of the methodology, yet managers

may not necessarily view this approach as more complex than their current practices for

offshoring determination and selection. In addition, we did not fully develop early

threshold and filtering approaches which may be used with this methodology at the front

end. In addition the final results may be integrated with optimization tools at the back-end.

These two items provide ample opportunity for future research.

24

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29

Table 1. Locational factors in facility location decision * Strategic Issues (SI) * Labor Factors (LF)

Competition Labor Costs Current Facilities Availability of Semi and Unskilled Labor Market Size and Penetration Availability of Skilled Labor Expansion Capabilities Education System * Accessibility Factors (AF) Extent of Labor Unionization Accessibility of Suppliers Right-to-work Law Accessibility of Customers Training Support Accessibility of Transportation Service * Utility Factors (UF)

Proximity of Production Material Sources Suitability of Electrical Service Proximity of Natural Markets Suitability of Telephone Service Proximity of National Markets Availability of Natural Gas Proximity of Large Cities Adequacy of Cost of Water Supply * Community Factors (CF) Suitability of Waste Disposal Service Physical Attractiveness * Risk Factors (RF)

Community Attitude towards Industry Foreign Exchange Risk Social Make-up of Inhabitants Government Intervention Suitability of Houses Political Risk Community Race Relations Economic Risk Fire Protection and Insurance Legal Risk Police Protection Natural Disaster Risk Adequacy of Local School * Plant Site Factors (RSF)

Suitability of Environmental Amenity Suitability of Site Parking Facilities Suitability of Medical Facilities Site Development and Construction Costs Suitability of Shopping Facilities Room for Expansion * Business Climate Factors (BCF) Plant Site Topographic Features Suitability of Repair and Maintenance Services Plant Site Adequacy and Costs Suitability of Business, Facility and Legal Plant Building for Sale or Lease Compatibility of Other Industry Financial and Special Factors (FSF) Suitability of Building Zones Hometown of Company Official Suitability of Zoning Restrictions Tax Structure Suitability of Environmental Regulations Government Incentive Availability of Public Technical Training Repatriation Allowances State of Local Planning Assistance Source: compiled from Hayter (1997: 107) (Watts 1987:170 (adapted from Moriarty 1983:70-71)), Sarkis and Sundarraj (2002: Table 1). Note: the selected salient factors to appear in the ANP model in this paper are identified by *.

30

Table 2. Strategic supplier selection factors Strategic Performance Measures Organizational Factors

* Cost (CT) * Culture (CE) Low Initial Price Feeling of Trust Compliance with Cost Analysis system Management Attitude/Outlook for the Future Cost Reduction Activities Strategic Fit Compliance with Sectoral Price Behavior Top Management Compatibility * Quality (QY) Compatibility among Levels and Functions Conformance Quality Suppliers Organizational Structure and Consistent Delivery * Technology (TY) Quality Philosophy Technological Compatibility Prompt Response Assessment of Future Manufacturing * Time (TE) Suppliers Speed in Development Delivery Speed Suppliers Design Capability Product Development Time Technical Capability Partnership Formation Time Current Manufacturing Facilities/Capabilities * Flexibility (FY) * Relationship (RP) Product Volume Changes Long-term Relationship Short Set-up Time Relationship Closeness

Conflict Resolution Communication Openness Service Capability Reputation for Integrity Innovativeness (IS) New Launch of Products New Use of Technologies

Source: based on Sarkis and Talluri (2002: Table 1); see also Chan (2003). Note: the selected salient factors are shown by *.

31

Table 3. Environmental metrics in supplier selection decision Categories Factors Sub-factors

* Pollution Controls (PC) Remediation End-of-pipe Controls

* Pollution Prevention (PPE) Product Adaptation Process Adaptation

Environmental Practices

* Environmental Management

System (EMS)

Establishment of Environmental Commitment and Policy Identification of Environmental Aspects Planning of Environmental Objectives Assignment of Environmental Responsibility Checking and Evaluation of Environmental Activities

* Resource Consumption

(RC)

Consumption of Energy Consumption of Raw Material Consumption of Water Environmental

Performance * Pollution Production

(PPO)

Production of Polluting Agents Production of Toxic Products Production of Waste

Source: adapted from Klassen and Whybark (1999: 606), and Gauthier (2005: 200). Note: the selected salient factors are shown by *.

32

Table 4. Social metrics in supplier selection decision Categories Factors Sub-factors

* Employment Practices

(EP)

Disciplinary and Security Practices Employee Contracts Equity Labor Sources Diversity Discrimination Flexible Working Arrangements Job Opportunities Employment Compensation Research and Development Career Development

Internal Social Criteria

* Health and Safety

(HS)

Health and Safety Incidents Health and Safety Practices

* Local Communities

Influence

(LCI)

Health Education Housing Service Infrastructure Mobility Infrastructure Regulatory and Public Services Supporting Educational Institutions Sensory Stimuli Security Cultural Properties Economic Welfare and Growth Social Cohesion Social Pathologies Grants and Donations Supporting Community Projects

* Contractual Stakeholders

Influence

(CSI)

Procurement Standard Partnership Screens and Standards Consumers Education

External Social Criteria

* Other Stakeholders

Influence

(OSI)

Decision Influence Potential Stakeholder Empowerment Collective Audience Selected Audience Stakeholder Engagement

Source: complied from Gauthier (2005), social indicators of Global Reporting Initiative (GRI, 2008), Presley et al. (2007), and Labuschagne et al. (2005). Note: the selected salient factors are shown by *.

33

Table 5. Environmental factors and sub-factors in facility location decision Factors Sub-factors *Environmental

Health

(EH)

Environmental Burden of Disease Adequate Sanitation Drinking Water

Indoor Air Pollution Urban Particulates Local Ozone

*Ecosystem

Vitality

(EV)

Regional Ozone Sulfur Dioxide Emissions Water Quality Water Stress Conservation Risk Effective Conservation Critical Habitat Protection Marine Protected Areas Growing Stock of Forestry Marine Trophic

Trawling Intensity of Fishery Irrigation Stress Agricultural Subsidies Intensive Cropland Burned Land Area of Agriculture Pesticide Regulation Greenhouse Gas Emission/Capita Greenhouse Gas Emissions/Electricity Generated Industrial Carbon Intensity

*Consumption

and Production

Patterns

(CPP)

Materials Use Energy Use Depletion of Nonrenewable Resource Regeneration of Renewable Resource Green Consumption

Waste Generation Waste Treatment Waste Disposal Waste Recycling

Source: based on Environmental Performance Index (EPI, 2008); see also Lin and Lee (2005: Table 1), and UN (2007: 14). Note: the selected salient factors are shown by *.

34

Table 6. Social factors and sub-factors in facility location decision Factors Sub-factors

* Poverty (PY)

Income Poverty Income Inequality Sanitation Drinking Water Access to Energy Living Conditions

* Governance (GC) Corruption Crime

* Health (HH)

Life Expectation at Birth Health Care Delivery Nutritional Status Health Status and Risks Old Age Provisions

* Education (EN) Educational Level Literacy

Demographics (DS) Population Growth Tourism

Natural Hazards (NH) Vulnerability to Natural Hazards Disaster Preparedness and Response

* Individual Development

(ID)

Civil Liberties and Human Rights Equity Individual Autonomy and Self-determination Right to Work Social Integration and participation gender and class-specific role material standard of living qualification specialization family and life planning horizon leisure and recreation arts

* Community Development

(CD)

Security Sense Cultural Properties Social Cohesion Social Pathologies

Source: based on the revised CSD (the Commission on Sustainable Development) indicators (UN, 2007: 10-14); see also Bossel (1999: 17). Note: the selected salient factors are shown by *.

35

Table 7. Pairwise comparison matrix and relative importance weight results for facility locational factors cluster and impact on objective

Objective SI AF CF BCF LF UF RF RSF W

SI 1 2 4 5 2 5 4 2 0.273

AF 0.5 1 4 2 2 4 4 5 0.204

CF 0.25 0.25 1 4 4 5 2 5 0.172

BCF 0.2 0.5 0.25 1 5 4 4 4 0.13

LF 0.5 0.5 0.25 0.2 1 5 2 2 0.085

UF 0.2 0.25 0.2 0.25 0.2 1 5 4 0.06

RF 0.25 0.25 0.5 0.25 0.5 0.2 1 2 0.041

RSF 0.5 0.2 0.2 0.25 0.5 0.25 0.5 1 0.036

36

Table 8. Generic supermatrix with sub-matrix identification

OBJ PH LF SL ENL SUPS SUPEN ORG SPM ALT OBJ 0 0 0 0 0 0 0 0 0 0 PH A 0 M 0 0 0 0 X Z 0 LF B J 0 0 0 0 0 0 0 0 SL C 0 0 0 0 T 0 0 0 0 ENL D 0 0 0 0 0 V 0 0 0 SUPS E 0 0 P 0 0 0 0 0 0 SUPEN F 0 0 0 R 0 0 0 0 0 ORG G K 0 0 0 0 0 0 0 0 SPM H L 0 0 0 0 0 0 α 0 ALT 0 0 N Q S U W Y β I Note: OBJ is short for strategic alternatives selection, PH for planning horizon, LF for locational factors in facility location decision, SL for social factors in facility location decision, ENL for environmental factors in facility location decision, SUPS for social factors in supplier selection, SUPEN for environmental factors in supplier selection, ORG for organizational factors in supplier selection, SPM for supplier performance metrics in supplier selection, and ALT for alternatives selection.

37

Table 9. ANP supermatrix for offshoring decision network

obj ST LT SI AF CF BCF LF UF RF RSF PY GC HH EN ID CD EH EV CPP EP HS LCI CSI OSI PC PPE EMS RC PPO CE TY RP CT QY TE FY AAA SAA SBB SCB

obj 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ST 0.2 0 0 0.1238 0.2354 0.2315 0.1567 0.3541 0.2365 0.1236 0.2569 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2356 0.3595 0.2598 0.2596 0.3579 0.2159 0.2368 0 0 0 0

LT 0.8 0 0 0.8762 0.7646 0.7685 0.8433 0.6459 0.7635 0.8764 0.7431 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.7644 0.6405 0.7402 0.7404 0.6421 0.7841 0.7632 0 0 0 0

SI 0.273 0.035 0.12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

AF 0.204 0.034 0.13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CF 0.172 0.0675 0.21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

BCF 0.13 0.048 0.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

LF 0.085 0.187 0.12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

UF 0.06 0.0245 0.15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

RF 0.041 0.1195 0.12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

RSF 0.036 0.4845 0.05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

PY 0.23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1536 0.1259 0.1269 0.159 0.259 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

GC 0.15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2156 0.1269 0.2489 0.256 0.246 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HH 0.04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1369 0.1259 0.263 0.324 0.123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

EN 0.35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1269 0.1236 0.159 0.112 0.123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ID 0.153 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2596 0.2584 0.169 0.009 0.147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CD 0.077 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1074 0.2393 0.0332 0.14 0.102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

EH 0.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.235 0.269 0.236 0.269 0.235 0 0 0 0 0 0 0 0 0 0 0

EV 0.25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.365 0.536 0.562 0.159 0.149 0 0 0 0 0 0 0 0 0 0 0

CPP 0.45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4 0.195 0.202 0.572 0.616 0 0 0 0 0 0 0 0 0 0 0

EP 0.26 0 0 0 0 0 0 0 0 0 0 0.236 0.269 0.269 0.1563 0.269 0.258 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HS 0.13 0 0 0 0 0 0 0 0 0 0 0.354 0.258 0.159 0.2593 0.259 0.147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

LCI 0.148 0 0 0 0 0 0 0 0 0 0 0.126 0.1236 0.246 0.1259 0.135 0.126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CSI 0.059 0 0 0 0 0 0 0 0 0 0 0.1 0.1596 0.1269 0.12 0.176 0.268 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

OSI 0.403 0 0 0 0 0 0 0 0 0 0 0.184 0.1898 0.1991 0.3385 0.161 0.201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

PC 0.25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.259 0.123 0.235 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

PPE 0.14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.345 0.258 0.125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

EMS 0.23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.123 0.369 0.321 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

RC 0.28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.141 0.147 0.156 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

PPO 0.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.132 0.103 0.163 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CE 0.28 0.259 0.126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

TY 0.43 0.147 0.126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

RP 0.29 0.321 0.321 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CT 0.28 0.123 0.159 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.35 0.36 0.26 0 0 0 0

QY 0.19 0.005 0.247 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.26 1 0.52 0.38 0 0 0 0

TE 0.34 0.058 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.35 0.24 1 0.36 0 0 0 0

FY 0.19 0.087 0.011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.39 0.41 0.12 1 0 0 0 0

AAA 0 0 0 0.328 0.135 0.231 0.236 0.15 0.132 0.215 0.072 0.012 0.236 0.022 0.005 0.123 0.236 0.256 0.263 0.123 0.2154 0.25 0.26 0.26 0.059 0.064 0.1 0.23 0.26 0.53 0.23 0.21 0.23 0.26 0.159 0.058 0.3 1 0 0 0

SAA 0 0 0 0.412 0.154 0.252 0.215 0.13 0.081 0.258 0.258 0.637 0.125 0.649 0.29 0.256 0.156 0.143 0.159 0.25 0.159 0.25 0.32 0.15 0.714 0.714 0.6 0.12 0.24 0.21 0.12 0.1 0.21 0.32 0.637 0.637 0.2 0 1 0 0

SBB 0 0 0 0.135 0.521 0.315 0.159 0.26 0.188 0.105 0.637 0.258 0.354 0.279 0.655 0.145 0.236 0.143 0.247 0.36 0.36 0.26 0.14 0.35 0.143 0.143 0.2 0.32 0.32 0.1 0.3 0.09 0.5 0.4 0.105 0.258 0.1 0 0 1 0

SCB 0 0 0 0.125 0.19 0.202 0.39 0.46 0.599 0.422 0.033 0.093 0.285 0.05 0.05 0.476 0.372 0.458 0.331 0.267 0.2656 0.24 0.28 0.24 0.084 0.079 0.1 0.33 0.18 0.16 0.35 0.6 0.06 0.02 0.099 0.047 0.4 0 0 0 1

38

Table 10. Sensitivity analysis of the illustrative case

Perturbations of matrix M

WST=0 WST=0.5 WST=1 Original

AAA (4th) 0.188 0.187 0.185 0.188

SAA (1st) 0.294 0.292 0.290 0.293

SBB (2nd) 0.278 0.283 0.280 0.280

Final weights

of the four alternatives

SCB (3rd) 0.240 0.238 0.237 0.239

39

Determine Salient Factors

Form Decision Network

Elicit Pairwise Comparison

Supermatrix Formulation

Calculate Stable Weights

Complete Sensitivity Adjustment

Yes

No

Make Decision

Figure 1. Various steps of ANP methodology

Feedback

40

All Potential

Alternatives

Four

Alternatives

One

Alternative

Threshold Evaluation ANP

Figure 2. Overview of the multi-staged alternative choice approach

41

Figure 3. A high level schematic of the network decision hierarchy for offshoring alternative selection

Planning

Horizon

Strategic

Performance

Metrics

Alternatives

Selection Set Strategic Offshoring

Alternatives Selection

Environmental

Factors

Locational

Factors

Social

Factors

Environmental

Factors

Social

Factors

Organizational

Factors

Facility

Location

Factors

Strategic Supplier

Selection Factors

J, M

P, T

A

L, Z

α

R, V

K, X

N Q

S

B, C, D

E, F, G, H

β

Y U

W

Note: The letters on the lines indicate sub-matrices in Table 8.

42

Strategic Alternatives Selection

DecSelection

Facility Location Factors

AAA

Alternative Selection Set

Figure 4. Detailed graphical representation of Analytical Network Hierarchy for strategic alternative selection

Planning Horizon

Short Term Long Term

Strategic Supplier Selection Metrics

Objective

SI AF CF BCF LF UF RF PSF PY GE HH EN ID CD EH EV CPP

PC PPF EMS RC PPO EP HS LCI SCI OSI CT QY TE FY CE TY RP

Locational Social Environmental

Social Environmental Strategic Performance Organizational

SAA SBB SCB