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A review of modeling approaches for sustainable supply chain management

Stefan Seuring

Supply Chain Management, Faculty of Business and Economics, University of Kassel, 34117 Kassel, Germany

a b s t r a c ta r t i c l e i n f o

Available online 3 June 2012

Keywords:

Supply chain management

Sustainability management

Quantitative modeling

Literature review

Environmental and social standards

More than 300 papers have been published in the last 15 years on the topic of green or sustainable (forward)

supply chains. Looking at the research methodologies employed, only 36 papers apply quantitative models.

This is in contrast to, for example, the neighboring eld of reverse or closed-loop supply chains where several

reviews on respective quantitative models have already been provided. The paper summarizes research onquantitative models for forward supply chains and thereby contributes to the further substantiation of the

eld. While different kinds of models are applied, it is evident that the social side of sustainability is not

taken into account. On the environmental side, life-cycle assessment based approaches and impact criteria

clearly dominate. On the modeling side there are three dominant approaches: equilibrium models, multi-

criteria decision making and analytical hierarchy process. There has been only limited empirical research

so far. The paper ends with suggestions for future research.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Globalization places demands on supply chain management to

reach beyond pure economic issues and matters like e.g. fair laborconditions and environmentally friendly production. This raises inter-

est in its intersection with sustainable development, which is usually

comprehended in an economic, an environmental and a social dimen-

sion (see e.g. [45]; for the link to supply chain management see [70];

[42]). Managing supply chains in a sustainable mannerhas become an

increasing concern for companies of all sizes and across a wide range

of industries. Meeting environmental and social standards along all

stages of the supply chain ensures that (at least) minimum sustain-

ability performance is reached. This more reactive approach of

responding to external pressure from governments, consumers and

non-governmental organizations (NGOs) [71] and media can be com-

plemented by the development and introduction of sustainable

products.

For the period 1990 to 2007 Seuring and Müller [70] reviewed a

total of 191 papers. Toward the end of 2010 this list has grown to

about 308 related papers which published about green and sustain-

able supply chain management. Yet, sorting these papers according

to the research methodology employed, only 36 remain which build

or use quantitative models. Hence, this paper aims at taking a longer

period into account than Seuring and Müller [70], but focuses on one

kind of research method, i.e. quantitative models, only. This allows

detailed insights into this stream of research and should reach con-

clusions on how to develop it further. This small number and share

of models is in clear contrast to, for example, the neighboring eld

of reverse or closed-loop supply chains, where Fleischmann et al.

[46] already provided a rst review on “quantitative models for re-

verse logistics”

. Furthermore, (wider) literature reviews on relatedeldsare available:(1) closed-loop supply chains[50]; (2) green supply

chains, but alsowith a focus on reverse logistics [73] and (3) sustainable

supply chain management [70]. Recently, further reviews have been

published, which address particular topics. Two examples are those

on sustainable supply chain management and inter-organizational re-

sources [48] or those relating it to a wider set of constructs in supply

chain management [49]. Mollenkopf et al. [61] have emphasized the

link to lean management and globalization issues in their review. Carter

and Easton [42] evaluate particularly related empirical research,

restricting themselves to a set of papers taken from seven journals

mainly in the domain of logistics and supply chain management. Yet,

such a review has not been attempted for quantitative models applied

to the forward supply chain.

The aim of this paper is to summarize existing research on quanti-

tative models for forward supply chains, thereby aiming at substan-

tive justication as an important step in theory building [58]. This

provides insights toward future research directions and needs.

The paper is structured as follows: as the study deals with a liter-

ature review, a classical section labeled as such is not provided. In-

stead, the paper starts by outlining the content analysis method as

applied in the research process. Next, some descriptive background

on the papers (e.g. years of publication, major journals) is presented.

Further, the ndings from the content analysis are discussed, with a

particular focus on the sustainability dimensions and modeling ap-

proaches. This will lead over to the discussion of the ndings and

brief conclusions.

Decision Support Systems 54 (2013) 1513–1520

E-mail address: [email protected].

URL:http://www.uni-kassel.de/go/scm.

0167-9236/$ – see front matter © 2012 Elsevier B.V. All rights reserved.

doi:10.1016/j.dss.2012.05.053

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2. Describing the method and base for the literature review

This study forms part of a wider literature review at the intersec-

tion of sustainability and supply chain management. The methodolo-

gy applied has already been described in detail [70]. Papers were

identied by means of a structured keyword search on major data-

bases and publisher websites (Ebsco, Springerlink, Wiley Inter-

science, Elsevier ScienceDirect, Emerald Insight). Keywords such as“purchasing”, “sourcing”, “supply” and “supply chain”, and “logistics/

logistical” were combined with sustainability related ones, such as

“sustainable/sustainability ”, “sustainable development”, “environ-

ment(al)”, “green”, “social” and “ethics/ethical”. Subsequently, pa-

pers were screened in detail in a two-step process. First, there were

three issues excluded from further analysis: (1) reverse logistics

and remanufacturing, as they have already been subject to self-

contained literature reviews already [46,50], (2) ethical behavior of

purchasing staff, and (3) public procurement. Second, the papers were

unanimously assigned to one research method, where ve categories

were applied: modeling (the one used here), theoretical or conceptual,

case study, survey or literature review.

A content analysis was conducted to systematically assess the pa-pers [55,56,63]. Material collection has already been described by

means of the literature search and reduction mode. For the analysis

itself, a set of criteria is used at rst for describing the sample. Then,

the discussion is taken into the content analysis itself, where a mix-

ture of deductive and inductive as well as quantitative and qualitative

criteria is chosen. Respective criteria applied for the content analysis

are outlined below.

2.1. Basic terminology

2.1.1. Sustainable supply chain management (SSCM)

The denition of Seuring and Müller [70] who take standard de-

nitions of supply chain management [59] serves as a starting point:

“Sustainable SCM is the management of material, information andcapital ows as well as cooperation among companies along the sup-

ply chain while integrating goals from all three dimensions of sustain-

able development, i.e., economic, environmental and social, which are

derived from customer and stakeholder requirements. In sustainable

supply chains, environmental and social criteria need to be fullled

by the members to remain within the supply chain, while it is

expected that competitiveness would be maintained through meet-

ing customer needs and related economic criteria.”

2.1.2. Quantitative modeling

According to Bertrand and Fransoo [39] this is “model-based

quantitative research, i.e. research where models of causal relation-

ships between control variables and performance variables are devel-

oped, analyzed and tested”

. Such research methods are frequently

used in supply chain management research (see e.g. [38,60,57]). Sub-

sequently, four different quantitative approaches are distinguished,

which are briey introduced, while the full justication for using

them as categories is provided later in the paper.

2.2. Sample and descriptive analysis

The overall sample contains 309 papers in total (status papers

published up to the end of 2010). Out of this sample only 36 papersapply quantitative models, thereby only contributing little more

than 12% of the total number of papers.

Fig. 1 highlights the timely distribution of the 36 papers. Only two

papers were published before the year 2002 [29,31,6,7,24]. There is a

small peak in 2005 with seven papers, but this seems just accidental

as there was no special issue which would easily explain it. In current

years, there is an almost stable output with four or ve papers pub-

lished for each year in 2007–10 (see Fig. 1).

Regarding journals, where such papers appear most often, Journal

of Cleaner Production (JCLEPRO) is the leading journal with nine pa-

pers (or 24%) published. Four papers appeared in the European

Journal of Operational Research (EJOR), and three each in the Interna-

tional Journal of Production Economics (IJPE) and the International

Journal of Production Research (IJPR), in total contributing another27% of the sample. The rest of the papers are distributed across a

range of other journals.

2.3. Criteria applied in the content analysis

Establishing criteria for content analysis can be based on a deduc-

tive or an inductive approach. Here, the criteria are mainly derived

deductively and are based on the already mentioned literature re-

views in the eld (particularly [70] criteria). Yet, this would not be

suf cient for addressing all relevant issues, so in some cases, criteria

can only be established while working with the material. This was

the case here for the assessment of the modeling approaches applied

in the paper.

The following dimensions will be discussed, briey explained and

justied:

• First, the environmental, social and economic criteria or perfor-

mance objectives are assessed. In a second step, the integration of

environmental and/or social issues with economic objectives is an-

alyzed. This is in line with the typical three dimensional compre-

hension of sustainability (see e.g. [45]) and has been discussed in

the eld of supply chain management before (see e.g. [71,42]).

• The modeling approach taken in the paper is described. As there

was no clear starting point for this analysis, the categories were de-

rived inductively. Three categories, i.e. life-cycle assessment

models, equilibrium models and analytical hierarchy process, were

used right at the start, while the other two only appeared during

the coding.• The link to empirical data forms the nal dimension of the analysis

[42,69]. This allows insights into the eld research done toward ll-

ing the models with empirical data.

The ndings from applying these dimensions are now presented.

3. Analysis of the papers

In the following sections, the different dimensions of the analysis

will be presented. To allow easier presentation of the material, tables

will be used summarizing the single dimensions as embedded in the

papers. Unless stated otherwise, all gures refer to the sample of 36

papers analyzed here.

Fig. 1. Time distribution of the analyzed papers.

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3.1. Sustainability dimensions

Conceptualizing sustainability in three dimensions seems to be

widely accepted [45,42]. It allows an easy comprehension of the

integration of economic, environmental and social issues. This also of-

fers the justication of applying it in this paper. Hence, the papers are

categorized, based on how they relate to sustainable development. As

all papers are taken from management related literature, the eco-

nomic dimension forms an integral part and was therefore nottaken as a separate category. Yet, the papers show a very strong dom-

inance of addressing environmental issues (34 papers). There are no

papers that exclusively focus on social issues, and also integrating

all three dimensions of sustainability is only present in two of them

(i.e. [5,15]).

Seuring and Müller [70] emphasize the need for increasing coop-

eration along the supply chain, if sustainability goals are to be

reached (also [49]). Hence, this should be reected in related goals.

A closer look is therefore taken on each dimension and on which

goals are put forward.

3.1.1. Economic dimension

As only papers dealing with supply chain management are taken

into account, it is logical that economic issues are addressed. Most

often, total cost or net revenue is taken as indicators. Yet, there are

a number of papersnot providing insights into what kind of economic

goals is pursued. This holds for a rst set of papers that mainly follow

a life-cycle assessment (LCA) approach (e.g. [34]). Often, such papers

compare different alternatives toward their environmental perfor-

mance (e.g. [11,13]). In a number of cases, the current economic

situation is (inherently) seen as a kind of baseline for evaluating al-

ternatives on their environmental impact. This holds particularly for

the LCA related papers (e.g. [34,35,4,11]) or those applying AHP

[31,19,20,25,26].

3.1.2. Environmental dimension

Most papers spend much more effort on explaining related envi-

ronmental issues. In many cases, life-cycle assessment data forms

the starting point for the analysis. Hence, energy demand and CO2-emissions (e.g. [4,11,35,28]) are among the frequently mentioned

topics. Yet, in a number of cases, rather comprehensive lists of envi-

ronmental impact criteria are taken up, such as referring to all kinds

of natural capital (e.g. [36]) or resources, such as water or energy as

well as waste (e.g. [26,23,17]). Overall a wide range of environmental

aspects is taken into account. A deeper look provides some critique to

the typical approach chosen. The relationship between the LCA-based

environmental impacts and their management in the supply chain

often points to supplier selection (e.g. [29,20,25]) and optimization

issues, such as transport to end customers [33,11]. Hence, LCA based

approaches seem to dominate here as they allow comprehending

and modeling product-related impacts (for a conceptual note see

[64]).

3.1.3. Social dimension

The social dimension is the one that is addressed the least often in

the papers. Building on a rather strict assessment, 34 of the 36 papers

do not mention it at all. In four cases one could even argue that the

term corporate social responsibility (CSR) is rather misused in the

title of the papers looking at their content [8–10,21]. While all carry

CSR in their title, they rather model environmental issues, but not so-

cial impact related ones. Cruz [8,9] as well as Cruz and Matsypura [10]

briey refer to risk associated with not having (enough) CSR. Hsueh

and Chang [21] use it as starting point for a discussion on prot

sharing.

Yet, turning to the denition of CSR (for an overview see [44]), it is

evident that this is about voluntary measures of companies, particu-

larly in the relation between business and society. Hence, CSR cannot

be subject to a simplistic comprehension of just a single factor put

into any kind of quantitative model. While this is a normative state-

ment, the ongoing debate on CSR supports such a statement (e.g. [47]).

This would basically leave all assumptions of supply chain management

unquestioned and rather support a marginally changed business as

usual, where sustainability is still subordinate to economic issues. Just as

one closely related example, Halldórsson et al. [52] pointed out that

such an assumption is insuf cient for addressing sustainability.

A much wider perspective is taken by a few other papers. Rathermacro-economic social aspects are put forward by Clift [5], Foran et

al. [15] and Ukidwe and Bakshi [36]. They cover employment and in-

come distribution, thereby pointing to the wide range of responsibil-

ities of companies.

3.1.4. Integration of the three dimensions

The integration of all the three dimensions plays a central role, but

is not often addressed so farin related research [70]. Previousndings

have rather conrmed that the social dimension needs much better

integration with the economic and environmental ones.

A second approach is taken by looking at the goal relations among

the three dimensions (see Table 1). It has to be noted rst that each

paper is assigned here to one category only, while Seuring and Müller

[70] assigned papers into more than one category, therefore having atotal of 234 entries for 191 papers. Comparing the gures, it can be

seen that trade-offs among the environmental and the economic di-

mensions are most often taken as a starting point for building the

models. This will be discussed subsequently, after the modeling tech-

niques have been introduced.

This analysis already pointsto a clear research gap regarding social

aspects as well as the overall integration of the three sustainability di-

mensions. While this will not be an easy or simple task, such research

is much needed and would also allow links of sustainable supply

chain management toward other emerging elds, such as the Base-

of-the-Pyramid debate (see e.g. [51,66]).

3.2. Modeling approaches

Looking at the models proposed, they can be grouped into four

categories (see Table 2). The four categories were not chosen deduc-

tively, but emerged inductively while trying to group them when

reading and analyzing the material: (1) life-cycle assessment based

models, (2) equilibrium models, (3) multi-criteria decision making

(MCDM), and (4), applications of the analytical hierarchy process

(AHP). The overall objective is usually a cost minimization effort,

where the entities that incur costs and the respective cost elements

are specied. Each approach and the typical elements pertained

therein will be briey presented.

It has to be mentioned that each paper is assigned to one category

only. The equilibrium models and the multi-objective decision mak-

ing (MCDM) models take somewhat similar starting points and both

aim at nding a balance between different (environmental and eco-nomic) performance criteria. As a further issue, there are three papers

applying simulation as their method [24,34,14,30]. Yet, they do so ei-

ther for providing insights into LCA cases [24,34] or for evaluating

Table 1

Goal relations among the three sustainability dimensions.

Goal relations Seuring and Müller [70]

(N=191 papers)

(N=234, multiple counting allowed)

Modeling papers

(N=36 papers)

Win–win-situations 124 (53%) 7 (19%)

Trade-offs 72 (31%) 20 (56%)

Minimum performance

for environmental

and social issue

37 (16%) 9 (25%)

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variables among a multi-objective decision making approach [14].

Hence, they are grouped into the respective categories.

Table 2 provides an outline of the different modeling approaches

used, briey describing the typical approach taken and listing the pa-

pers that were grouped into the respective category.

3.2.1. Life-cycle assessment (LCA) based studies

Life-cycle assessment is a product based on environmental assess-

ment techniques (e.g. [54]), which was even standardized in the ISO14041. It is rst discussed here, as it often forms a kind of background

for the other modeling approaches. LCA-type data is pointed to in 29

of the 36 papers. The papers mainly present life-cycle assessment

data and illustrate its use in a specic context or case application.

This is well in line with earlier analysis, such as the concept suggested

by Pesonen [64], who already pointed to the use of life-cycle

assessment based criteria in supply chains. Furthermore, Seuring

[67] elaborates that LCA-based criteria usually provide the product

optimization perspective which precedes the supply chain optimiza-

tion. In this respect, it is no surprise that LCA type data serves as a

background for subsequent optimization in the papers reviewed

here (e.g. [14,13]). The link to products is evident, as a core line of re-

lated research is labeled as supply chain management for sustainable

products (e.g. [70,69]).This also offers a link to the already discussed environmental di-

mension and related performance objectives (see Section 3.1). In

total, a broad range of environmental emissions and impacts is re-

ferred to, while related impact assessments usually concentrate on

the direct emission level.

One further typical element is the focus of a number of papers on

one product or industry, such as aluminum [24,35,13], milk [34], elec-

tronics [18] or services, particularly book distribution [11] or wine

distribution [4]. This allows reducing the environmental issues dealt

with as well as making respective supply chain related decisions

more evident. While the LCA-studies therefore often stay on general

supply chain wide aspects and in many cases do not center on a single

actor or actor network in the supply chain, such is usually the case in

the other approaches taken in respective papers.

3.2.2. Equilibrium models

The already mentioned line of research on assessing performance

of an overall supply chain or even industry is continued within the

papers aiming at indentifying an equilibrium. This is a standard

modeling technique [62] and is well established (e.g. [57]). Such equi-

librium would be established by assessing what the optimal level of

investment into environmental (abatement) technologies and re-

spective economic returns would be. Kainuma and Tawara [23] start

this debate by proposing a multiple utility function approach, whichbuilds on metrics in three dimensions: (1) LCA, (2) supply chain re-

turn on assets and (3) customer satisfaction. A starting point for relat-

ed research was presented with the shared savings contract model for

indirect material [7]. The environmental consideration is easy to com-

prehend as it aims at reducing the amount of indirect materials being

applied. Staying with environmental considerations but aiming at a

much wider equilibrium among natural and economic capitals,

Ukidwe and Bakshi [36] present their model, mainly building on ther-

modynamic input–output analysis. Nagurney and Toyasaki [27] for-

mulate a more specic model based on manufacturers, retailers and

consumers, but stay vague about what kind of emissions is actually

dealt with.

The above already criticized papers modeling CSR activities also

fall into this category. Hsueh and Chang [21] stay particularly vague

about what CSR would imply: “Herein, undertaking CSR is originally

utilized for coordinating the decentralized supply chain.” In the

model itself, this is only present in a production function as “per-

ceived production and inventory cost of manufacturer (considering

CSR)” [21]. Cruz [8] handles this in a similar manner, even claiming:

“We note that any level of social responsibility activities between any

two parties in the supply chain requires a strong level of collaboration/

cooperation between them.” Such an approach is made somewhatclear-

er in the paper by Cruz and Matsypura [10] who claim that “engagement

in CSRis assumed to reducetransactioncosts, waste,as well as risk.” This

is one of the assumptions forming the background for modeling a re-

spective network of manufacturers and retailers. In the equilibrium, an

optimal solution is found for CSR related investments, products' ows

along the supply chain and respective prices.

A similar approach is presented by Saint Jean [30], but is based onemission standards, which seems easier to comprehend. Here, the

minimum performance level of environmental and social standards

[70] is pointed to, which seems much more straightforward than

unspecied investments into CSR. This refers to the overall contribu-

tion of these models, which evaluate the consequences of introducing

some sort of minimum performance criteria. While the problem

statement and model formulation for environmental issues seem

straightforward, the integration of social issues needs further elabora-

tion and a clearer positioning whether minimum standards, e.g. for

working conditions and payment, are reached or whether voluntary

CSR activities are expected. It is emphasized again that these models

evaluate the overall equilibrium among a given set of market actors,

but do not directly aim at their decisions. Such focus is taken within

the other modeling approaches.

3.2.3. Multi-criteria decision making (MCDM)

The link to the previous category is evident, in e.g. the paper of

Sheu et al. [33], who use multi-objective programming but also assess

the equilibrium among manufacturing chain and reverse chain, in-

cluding e.g. transaction costs and recycling fees. This showcases the

typical idea of the papers where different objectives have to be met

at the same time, which makes the point for such multi-criteria deci-

sion making approaches (e.g. [60]). Hence, the focus is usually not so

much on reaching an equilibrium situation, but rather dealing with

trade-offs among conicting objectives. There is a somewhat weak

line among these papers looking more at single company/supply

chain based decisions and the previous category. An ef cient logistics'

network conguration is at the core of the arguments presented by

Table 2

Grouping papers according to modeling techniques.

Modeling approach Typical element of

the model

Related papers

Life-cycle assessment (LCA)

models (11 papers)

Assessing

environmental

impacts along a

supply chain and

minimizing them.

[3–6,11,13,15,18,24,34,35]

Equilibrium models (9papers)

Balancingenvironmental and

economic factors and

nding an

equilibrium or

optimal solution.

[7–10,21,23,27,30,36]

Multi-criteria decision

making (6 papers)

Optimization of

economic and

environmental

criteria, usually

balancing trade-offs

or identifying opti-

mal solutions.

[14,16,17,22,28,33]

Analytical hierarchy

process (8 papers)

Structuring a

decision process

thereby obtaining a

solution based on

semi-quantitativecriteria and respec-

tive weights.

[1,2,12,19,20,25,26,29,31,32]


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Neto et al. [28]. In line with the LCA studies and equilibrium models,

they mention that trade-offs would be based on societal decisions

based on Pareto ef ciency. Their model could help in making respec-

tive decisions and is clearly linked to the equilibrium models already

described. Hugo and Pistikopoulos [22] also use LCA data as their

starting point and integrate this into modeling the supply chain for“environmentally conscious strategic investment planning”.

More into detail are e.g. Fichtner et al. [14], assessing joint invest-

ments into inter-

rm energy supply and respective savings. Quitesimilarly, Geldermann et al. [16] look at a bicycle manufacturer in

China. Heat integration, water management and solvent recovery

are the environmental issues addressed and optimized. While these

two papers already point to supply chain design, this is emphasized

in the two papers remaining in this category.

3.2.4. Analytical hierarchy process (AHP)

The logic of LCA-data is also evident in the paper applying the

AHP, similar techniques or modications thereof. The AHP is also a

multi-objective decision making technique [65]. The major difference

is that it is not based on a full mathematical formulation of a certain

problem, but is called a semi-quantitative decision making technique

simplifying and structuring decisions (e.g. [65,53]). This justies an

own category, different from the two previous ones. The two paperspublished early in the entire data set are based on this semi-

quantitative decision technique [29,31]. The AHP allows evaluating

not only complex decision situations, where environmental and eco-

nomic goals are assessed at the same time (e.g. [31,32,19,12]), but

also more specialized decisions, such as looking at the role of

hazardous substance management [20] or green supplier selection

[29,26,25] and supplier development practices [1,2]. The last two pa-

pers somewhat form their own category as they apply rough set the-

ory. Yet, as this also deals with incomplete information and follows

similar aims as the AHP, it was decided to keep them in the same

category.

Overall, the decisions evaluated focus on improving environmental

performance toward greening respective products [41,70]. Compared

to the previous two categories, the aim is not so much reaching an equi-libriumor optimal approach but rather pointing toward the complexity

of decision making and emphasizing the inuence of the decision

makers. AHP allows taking different decision criteria into account and

evaluating them without necessarily connecting all of them into one

quantitative model. Hence, such an approach may also be called

multi-objective decision making (e.g. [26]), but building on managerial

judgments.

3.3. Interrelation of modeling approaches and goal relations

As a further step, it is interesting to combine the two analytic di-

mensions of goal relations and modeling approaches. As briey men-

tioned, the analytic dimensions and categories for the content

analysis are often derived inductively and are then validated againstthe interpretation and understanding they offer.

This kind of construct validity is derived from the two analytic di-

mensions, jointly offering interesting insights into how the typical

models look and thus allowing a distinction among them (discrimi-

nant validity). This is shown in Table 3.

The equilibrium models as well as the multi-objective decision

making build on trade-off situations among environmental and eco-

nomic goals. There is only one MCDM paper which takes its starting

point from a win–win situation [14]. This is also the only one building

on data from a practical environment.It almost seemsobvious that these models take their starting point

from trade-offs between the economic and environmental dimen-

sions. Such trade-offs are a critical aspect of sustainable supply

chain management and often form the starting point for respective

action [71]. Further, trade-offs are more straight forward to model

in these approaches.

All three goal relations are observed only for the LCA based papers.

This is in line with the previous nding that LCA data forms a back-

ground for many of the studies presented here. While the trade-offs

dominate, the two other categories are also found. Foran et al. [15]

as well as Tan and Khoo [35] take win–win-situations as their starting

point. Both apply an analysis of statistical data. The latter looks at

cleaner production and process improvements in the aluminum sup-

ply chain. Foran et al. [15] look at different products relevant for pri-

vate consumption (e.g. cotton and vegetables).

There are three papers arguing for minimum conditions for envi-

ronmental and social performance [34,3,18]. The paper by Sonesson

and Berlin [34] is based on the milk supply chain, while Brent ad-

dresses the automotive one. All papers have in common that they

rather argue on a macro-level, well in line with the win–win-category

ones. It seems to be more straightforward to depart from a trade-off

assumption if the level of analysis is not focused on just a single com-

pany and its respective supply chain.

For the AHP papers, there are contrasting ndings as there are no

papers dealing with trade-offs. While four look at win–win situations

[31,32,26,12], there are six taking environmental issues as minimum

standards against which economic decisions have then to be made

(see Table 2). The selection of the decision criteria is more exible

within the AHP as they are rather connected in a logical but not in amathematical matter. This allows choosing criteria that represent ei-

ther win–win-situations or minimum standards. The most recent de-

velopment in this category, namely being the application of rough set

theory [1,2], also allows evaluating performance issues of each com-

pany as well as the supply chain in total, an issue not addressed in

the previous publications in this category.

3.4. Illustrations and empirical data

Many modeling papers contain a theoretical example of numerical

illustration of the model presented (see Table 4). This is also found

here, as 28 papers in total do so. The purpose of such an illustration

usually emphasizes the consequences of the decision making for areal life application. Yet, very few papers actually build on empirical

research. On most occasions, the illustration is “made up”. Only one

paper is purely theoretical and does not contain any such information

[5]. Few examples are found to be different. Fichtner et al. [14] use an Table 3

Relationship among goal relations and modeling approaches.

Goal relations Modeling approach Sum

LCA

model

Equilibrium

model

Multi-criteria

decision

making

Analytical

hierarchy

process

Win–win situations 2 0 1 4 7

Trade-offs 6 9 5 0 20

Minimum performance

for environmental and

social issues

3 0 0 6 9

Sum 11 9 6 10 36

Table 4

Empirical research presented in the papers.

Empirical content No. of papers (N = 36)

Theoretical or numerical example 28 (all others not listed below)

Statistical data 5 [6,15,24,34,36]

Empirical data 2 [14,19]

None 1 [5]


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industrial network in the Karlsruhe area, Germany, as background for

their model, thereby relating to real world data. Others take industrial

sector data, such as the example of the European pulp and paper in-

dustry [28], but stay at an illustrative level. Such statistical data is

used most often in LCA-type studies, which make up four of the ve

papers offering such an approach [6,15,24,34]. Only Ukidwe and

Bakshi [36] build a thermodynamic input–output model on 488 sec-

tor data sets for the USA, thereby assessing an equilibrium situation

between natural and economic capitals (Table 4).Besides the already mentioned model based on real technical data

by Fichtner et al. [14], there is only one paper presenting empirical

data. A survey feeding into the decision making process is found in

Hsu and Hu [19], who take responses from 87 managers for validating

their AHP model.

Overall, it has to be concluded that the link to empirical data is

missing for most of the related research. Yet, combining the models

presented with empirical data should offer interesting insights and

hence provides a sound direction for future research.

4. Discussion

This paper provides a rst review of publications applying a

modeling approach for green or sustainable supply chain issues. Thecontribution of this review lies in aggregating the so far scattered

publications on this topic and providing an overview on the ap-

proaches taken therein.

4.1. Sustainability dimensions

Several conclusions can be drawn, which rst of all relate to the

three dimensions of sustainability:

• The social dimension is almost completely missing or sometimes

comprehended in a far too simplied manner. It might be dif cult

to model social impacts. This is in line with wider literature reviews

(e.g. [73,70,49]), which also found a lack of research on social as-

pects. Hence, there seems to be a challenge for the wider range of related research, while social aspects might have to be evaluated in

detail before they are suitably integrated in such (multi-objective)

modeling approaches.

• The environmental dimension is mainly treated by building on life-

cycle assessment-based categories. Yet, many papers stay vague

about the kind of environmental impacts taken into account.

Some specify the environmental impacts of a particular product or

supply chain process and mention how to deal with them. Such

models would offer the chance to look at particular impacts and

offer guidance to practitioners on how to deal with them. This

also points to a more theoretical stream of research, which assesses

the role of LCA-data in supply chain decisions. Here, a link to empir-

ical researchon the decision makers would be relevant in particular.

• In the economic dimension, “total” cost-based or decision relatedcost and revenue approaches dominate. This does not really capture

how proactive companies strive to implement green or sustainable

supply chains and thereby actively managing a number of perfor-

mance objectives. Some of the AHP related papers point to this

and thereby get closer to what Seuring [69] calls supply chain man-

agement for sustainable products. Widening the scope of economic

performance criteria would be a welcome contribution.

• Last but not least, the integration of the three dimensions as well as

the interrelations among sustainability dimensions and objectives

demand further research. Previous modeling research mainly

looks at trade-offs. While this might be a sound starting point for

decision making, the consequences of either win–win situations or

environmental and social standards as minimum criteria also war-

rant further research.

4.2. Limitations

The rst limitation of this paper comes from the small sample size

of only 36 papers. This has been justied as no previous literature re-

view or similar approach has been presented on quantitative model-

ing approaches in sustainable supply chain management. A second

limitation is that only a number of quantiable criteria are used for

the content analysis. Only meaning embedded into these categories

is extracted from the papers. Yet, it is obvious that each paper wasread more than once during the coding and thereby other aspects

came forward in a more inductive manner. The range of codes could

be expanded, so that e.g. the environmental dimension is analyzed

in more detail. Furthermore, the quantitative models could be

assessed toward the constraints taken into account and based on

the different variables being modeled. Taking the analysis to such

depth might even require restricting the sample further. This leads

over to directions for future research.

4.3. Research directions

Further conclusions are drawn regarding the modeling ap-

proaches taken as well as the so far weak link to empirical data:

• A critical link is the one to empirical research. While there is plen-

ty of empirical research on sustainable supply chain management

(see [42,69]), such data is not linked to the formal assessments of-

fered by quantitative models. While this might be challenging,

building on empirical data for the model development should pro-

vide a sound link into other streams of research within the wider

eld.

• There is also the open question about the popularity of models for

closed-loop supply chain management and reverse logistics and

not for the forward supply chain in research. The reasons can only

be speculated on. This cannot be concluded from the current body

of literature, but might be an issue for further analysis.• One further direction might be the link to supply chain contracts.

While a review of this topic has already been provided by Tsay etal. [74], this has not been connected to environmental and sustain-

ability issues. The single link to this topic is the analysis of shared

savings contracts ([7], as a further paper see [43]).

• Finally, the link into the supply chain management literature should

be strengthened. There is rarely a link into the literature on strate-

gic supply chain design (e.g. [68]), supply chain performance and

collaboration literature, which sees a number of interesting devel-

opments (see e.g. [72,37]). One such attempt has been made in a re-

cent paper by Wang et al. [75], who analyze supply chain network

design against environmental criteria. A further example links

green supply chain management to stock market performance [40].

The analysis needs further expansion. Future research can take up

some of the challenges to ll gaps in the still emerging intersection of

sustainability and supply chain management.

4.4. Future research questions

As a consequence of the previous analysis and research directions,

a number of broader research questions can be raised. Two broad

groups of research questions are proposed. The rst one relates to as-

pects of sustainability, while the second group integrates with supply

chain management thought.

Regarding questions on sustainability, it is obvious that the envi-

ronmental side has been addressed more often so far. This also

holds for the many contributions already looking at carbon based

emissions. Therefore, the link into the social dimension of sustainabil-

ity is emphasized here.


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• How can the social dimension be integrated into respective models?

Such approaches might have to build on the assessment of particular

social impacts, thereby reducing the overall complexity.• What is the interrelation among the social and the environmental

dimensions? This might be an issue where multi-objective optimi-

zation or the AHP allows reaching further conclusions.• Can the interrelation among all three dimensions of sustainability

be modeled? What are suitable approaches for doing so? Address-

ing this question, it would be worthwhile taking a look at functionsother than logistics, operations and supply chain management. This

should allow shedding light on the challenges but also respective

opportunities in research on sustainable supply chain management.

Questions on supply chain management:

• How does environmental and social performance impact supply

chain performance? While there are a number of contributions al-

ready on this question, a wider or more detailed analysis would

still be benecial.• How can contracts and supply chain cooperations be understood

further, so that sustainability issues are not just seen as trade-offs.

As the analysis in Table 3 is revealed, the assumption of trade-offs

dominates this stream of research. It might be interesting to see al-

ternative approaches and identify win–win solutions without beingoverly simplistic.

These research directions and questions would help developing

the eld further while only a selected set of them can be discussed

here.

5. Conclusion

The paper provides a review of the status of research on sustain-

able supply chain management applying (mathematical) modeling

techniques. The sustainability dimensions, the particular modeling

approaches taken as well as the empirical content as presented in

the papers have been assessed. It is evident that the environmental

dimension clearly dominates and social aspects are widely ignoredor interpreted in an unusual manner. Life-cycle assessment type stud-

ies and respective data form the backbone of the environmental de-

bate in the papers, while cost minimization still seems to dominate

the economic dimension. The different modeling approaches (equi-

librium models, multi-objective optimization and analytical hierarchy

process) form only a subset of the wider range of methods available.

The ndings of the paper summarize the status of research on apply-

ing modeling techniques in sustainable supply chain management

and offer insights into directions for future research.

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Stefan Seuring is a full professor of supply chain management at the University of Kassel, Germany. Previously, he worked at the Waikato Management School, Hamilton,New Zealand and was a visiting professor at the Copenhagen Business School. He holda PhD and habilitation from the Carl von Ossietzky-University of Oldenburg, Germany.He has published widely on sustainability and supply chain management.

http://www.qualitative-research.net/index.php/fqs/article/view/1089/2386http://www.qualitative-research.net/index.php/fqs/article/view/1089/2386http://www.qualitative-research.net/index.php/fqs/article/view/1089/2386http://www.qualitative-research.net/index.php/fqs/article/view/1089/2386http://www.qualitative-research.net/index.php/fqs/article/view/1089/2386

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