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SUPPLY CHAIN NETWORK DESIGN CLASSIFICATIONS,

PARADIGMS AND ANALYSES

Reza ZanjiraniFarahani1*, ShabnamRezapour2, Tammy Drezner3, Samira Fallah4 (1) Department of Management, Kingston Business School, Kingston University, UK

(2) The School of Industrial Engineering, The University of Oklahoma, Norman, Oklahoma, USA (3) Steven G. Mihaylo College of Business and Economics, California State University-Fullerton, Fullerton, USA

(4) Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Designing a supply chain network (SCN) consists of determining the network structure and dealing

with various decisions from strategic decisions on location and capacities of facilities to operational

and tactical concerns about transportation or inventory management policies. The structure of a

SCN has a great influence on its future performance and costs and therefore, network design is a

critical concern in supply chain management (SCM). Traditionally, most studies in the area of

supply chain network design (SCND) mainly focus on monetary objectives. They seek designing a

SCN with the aim of minimizing total costs of network or maximizing its overall profit.

Nevertheless, recently various new paradigms have emerged in SCM field. In order to guarantee

profitability and maintaining in today's market, a SCN not only has to minimize the overall costs

but also should be flexible and responsive to the environmental changes while considering different

concerns such as environmental and social factors rather than economical ones.

As a result, designing a profitable SCN requires a broader view which takes such primary concerns

in to account. In this paper, this concern is emphasized and different paradigms in SCM with

particular focus on network design are reviewed and some current studies in each paradigm are

addressed. In addition, we propose a framework of major studies in SCND area and we summarize

the strategic decisions which are answered in each section.

Keywords: Supply chain management; Network design; Paradigms; Objective Functions;

Classification.

                                                            * Corresponding. Fax: +44 020 8417 7026; Tel: +44 020 8517 5098; Email: zanjiranireza@gmail.com 

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1. Lean Supply Chain Network Design (SCND) Lean is a philosophy that originally was created for production systems. Lean production focuses on

elimination of any waste, process or resources that have no value-added for the end customer.

1.1. What is lean Supply Chain (SC)?

SC is primarily developed for lean production with the aim of achieving cost reduction by

eliminating non-value adding activities, so the objective function of these models is to minimize

total cost of the SC. Shen and Qi (2007) developed a model for optimizing the total cost of a SC

when the retailers are uniformly distributed. The result was a nonlinear integer programming model

which is solved using Lagrangian relaxation.

1.2. What is lean SCND?

Shen (2007) presented a recent review of lean SCND literature. He classified the models based on

their objective functions (minimizing chain’s total cost or maximizing its profit) and investigated

different operational costs. For studying these models, Shen (2007) introduced the model presented

by Daskin et al. (2002) as the basic model. All other models are extensions of Daskin et al. (2002)

with new assumptions. The overall characteristics of this basic model are summarized in Table 1.

Table 1. The main characteristics of lean SCND basic model (Daskin et al. 2002).

Major Assumptions

- One or more supplier, distribution centers (DCs) and retailers exist

- The location of supplier and each retailer is given - The demand quantity of any retailer is uncertain - All lead times of DCs are equal - Inventory holding costs are equal in all retailers

Number of Products Single product

Number of Periods Single period Network Flow Forward Capacity of Facilities Infinite

Outputs of the model

- Finding the optimal number of DCs - Finding the optimal location for each DC - Assigning the retailers to DCs - Determining the optimum ordering policy and the amount of

safety stock in DCs

Objective Function

Minimizing the overall cost consisting: - Fixed cost of locating the facilities - Cost of shipping products from DCs to retailers - Total cost of working inventory - Cost of safety stocks

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The basic problem is formulated as a non-linear integer-programming model. Various

approaches were proposed in different papers to solve the model. Daskin et al. (2002) proposed a

Lagrangian relaxation solution algorithm and a number of heuristics for finding the feasible

solution. Shen et al. (2003) reformulated the problem as a linear integer set-covering problem and

applied column generation for finding the solution. Shu et al. (2005) also presented an algorithm

based on column generation and variable fixing to solve the basic model.Shen (2005) developed the

general form of the basic model. He designed a multi commodity network model for a SC. To solve

the model, he applied Lagrangian relaxation embedded in a branch and bound algorithm.

Shen and Daskin (2005) extended the basic model by considering customer service issues. They

developed a multi-objective model which explores the trade-off between cost and customer service

level. They proposed two solution approaches (a weighting method and a heuristics based on

genetic algorithm) for optimizing the model. Ozsen et al. (2006) developed a model based on the

basic one which considers a limited capacity for each DC. They proposed a Lagrangian relaxation

solution algorithm for solving the model.

Shen (2006) proposed a model for designing supply chain network (SCN) to maximize the SC

profit. While cost based approach tends to satisfy the overall demand, it is not the case when the

objective is to maximize SC profit. This model is based on the fact that even in a monopoly; it is not

always beneficial to satisfy all customer demand. In some cases the cost of this approach may

exceed the profit. Their proposed model allows a firm to choose whether to fulfill an order or not.

The problem is formulated as a set-covering model and they use branch and price algorithm to solve

it. He also generalized the model under random demand and limited capacity assumptions.

Different cost components are considered in SCND problems with lean consideration. While

most models consider the associated cost of strategic decisions such as location and capacity

improvement, there are some with focus on operational costs of the SC. Due to the major impact of

SCND on operational decisions, consideration of the associated costs of this level is necessary.

Often it is difficult or even impossible to determine or predict all operational costs. As a result, most

models only consider the important ones such as inventory holding, ordering, distribution, routing,

and operating costs.

1.3. Observations of Lean SCND literature

In the majority of the literature in the field of lean SCND, the objective function is to minimize the

total cost of the chain. In these papers, fixed income for the chain is assumed. There are two

fundamental and restrictive assumptions in such papers:

• The markets in which the chain will supply its product are exclusive and there are no

competitors in the markets offering the same or substitutable products. In today's markets, this

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assumption is far from reality and its application is restrictive. By globalization and

communication technology improvements, there are few exclusive markets. For new fiercely

competitive markets, we need new SCND models.

• It is assumed that the chain will have to supply all market’s demand. This assumption is not

applicable for private (non-governmental) SCs.

There are only few studies of SCND with lean consideration in order to impose the new

requirements of today's markets (mentioned in the first assumption). Hence, revising the existing

SCND models to consider the new requirements of today's markets is one important gap of lean

SCND literature. SCND models consider a tradeoff between the income and the cost of a chain.

Ignoring the existing competitors leads to imprecise modeling of SC’s income and this can result in

a significant mistake in designing the network structure which is a strategic decision. In some recent

papers such as Shen (2006), the second assumption is revised and there is no such restriction on the

chain. Instead of cost minimization, these models maximize chain’s profit in the exclusive markets.

Therefore, one of the variables of these models is the quantity of the product supplied to each

market.

There are many different competitive factors in competitive markets. Some of them (such as

distance from customers, quality of retailers, capacity of retailers and so on) are determined in the

SCND stage which are called strategic competitive factors. There are some other competitive

factors such as retail price and service level which are operational decisions affected by the SCND

decision. Thus, prediction of the income for a chain with unknown network structure is impossible

and can lead to significant deviation from its real amount. This issue determines the importance of

considering the competitive lean SCND problems.

2. Agile / Responsive SCND 2.1. What is agility / responsiveness?

Today, the needs and expectations of customers are rapidly changing. In such a changing,

competitive environment companies require new strategies and technologies to improve their

responsiveness and increase their flexibility in a cost effective way. Therefore, they focus on

developing agile SCs to reduce time to market of their products while achieving minimum total

cost. Christopher and Towill (2001) addressed the major differences between “lean” and

“responsiveness” paradigms and showed how these two could be combined to create competitive

SCs.

2.2. What is agile/ responsive SC?

Being highly flexible is a must for the responsive SCs (RSC) in order to respond to environmental

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changes by quick reconfiguration. Hence, there is a need for integrating the concept of agile

manufacturing (AM) and Supply chain Management (SCM) to achieve agility in a SC environment.

According to Gunasekaran et al. (2008), a responsive and flexible SC is achieved through the

interaction of three major elements i.e. a) value chain or a collaborative network of partners, b)

information technology (IT) and c) systems, and knowledge management (Figure 1). A level of

compatibility and interactivity is essential for a RSC system to make it possible for a firm to cope

with the changes and increasing the complexity of organizations and markets, especially in a

networked economy.

Figure 1.Major enablers of RSC (Gunasekaran et al. 2008).

Christopher and Towill (2001) developed a three level framework to introduce a RSC. The first

level represents the major principle of a RSC in terms of rapid replenishment and postponement

fulfillment. Level two constitutes the individual programs that must be applied to support the

principles in the first level, among them are quick response, the agility of organization and lean

production. The third level identifies the actions that must be taken to enable programs represented

in the second level, such as eliminating waste, comprising the time and enriching the information.

Christopher (2000) suggested that in time-based competition era, time is a competitive weapon

and those SCs which have the capability to shorten delivery times and be more responsive to their

customers will survive. He addressed the concept of responsiveness as a key towards

competitiveness and survival.

Prater et al. (2001) suggested that strategic SC responsiveness is crucial and vital for every firm

to evaluate the trade-off between SC responsiveness and their own vulnerability. They define the

concept of “supply chain exposure” which means to what extent an agile SC is vulnerable and

indeed requires restructuring and adjustment. They concluded that the number of geographical areas

which a SC covers and the number and speed of transportation modes are among the factors

Net

wor

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pa

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ing

firm

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Information technology and system

Know

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anagement

Responsive SC

Speed & flexibility

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affecting SCs exposure as well as environmental, political and technical issues.

Dotoli et al. (2006) presented a single and multi-objective optimization model to design the

structure of an integrated e-SCN. They considered an internet-based distributed manufacturing

system that has several stages such as supply of raw material, intermediate supply, and

manufacturing, distributing, retailing and recycling. These stages are related by means of material

and information flows. The objective of the model is to minimize costs by considering some

constraints such as bill of material (BOM), mutual exclusion and path. The developed model is

applied to two case studies in the desktop computer production industry.

Agarwal et al. (2007) derived the variables influencing the responsiveness of SC and their

interrelationship by using interpretive structural modeling (ISM). By studying a case SC and

considering the expert judgment, they concluded that improving quality, satisfying customers,

introducing new products, improving service level, minimizing costs, delivery speed and reducing

lead time are the primary variables which impact the responsiveness of a SC.

Li et al. (2008) provided a framework to investigate the relations between responsiveness of SC

and competitiveness of firms. By taking a work design perspective to investigate levels of SC

responsiveness, they suggested three levels as follows:i) an episodic design level, ii) operational

design level, and iii) strategic design level. The episodic design is the result of integrating alertness

of a SC to changes in tasks to its capability for responding to them by applying existing resources.

Operational design responsiveness is concerned with integrating the alertness of SC to changes in

demand or supply with its capability to respond to them by adjusting existing type of work

episodes. The strategic design responsiveness concerns with SC’s alertness to internal and

environmental changes and its capability to respond to these changes by reshaping or restructuring

current operational systems.

You and Grossmann (2008) considered lead time as a responsiveness criterion and net present

value as an economic criterion to address the problem of designing and planning SC when demand

is uncertain. They formulated the problem as a bi-criterion optimization to minimize expected lead-

times and to maximize the net present value. They applied the ε-constraint method to produce a

Pareto-optimal curve. The curve revealed the change in optimal net present value, structure of SCN

and level of safety stock in different lead time values.

Baker (2008) investigated 9 case studies to understand how distribution centers are designed and

operated in each individual business units for rapid responding to the markets. By means of the

solution each under studied company uses to deal with proposed problem, a framework is formed to

address the problem and also form a basis for future research of agility application in distribution

center level.

Luo et al. (2009) developed a model to overcome the difficulty of processing the information of

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a large number of potential suppliers. Their model was based on Radial Basis Function Artificial

Neural Network (RBF-ANN) and makes it possible to assess potential suppliers with respect to

different quantitative and qualitative criteria.

Pishvaee and Rabbani (2011) studied the responsive, multi-stage SCND problem and proposed

two mixed integer programming (MIP) models for two conditions: (a) to allow direct shipment and

(b) to prohibit direct shipment. They studied the structure of SCND problems by means of graph

theoretic approach; in this way they eliminated the complexity of the MIP models. The ability of

modeling SCND problems by a bipartite graph was also proved. Finally, by means of graph

theoretic view to the structure of problem understudied, they developed a novel heuristic solution

method.

Pan and Nagi (2013) discussed the concept of virtual organization as an efficient tool for agile

manufacturing. The virtual organization is formed by agile rivals who share their resources as well

as their core competencies to accomplish a product. The accomplishment of this particular product

is not feasible without this integration. They also addressed the relevance and importance of

considering strategic and tactical planning issues in agile manufacturing. Pan and Nagi (2013)

consider the problem of forming the virtual organization to form a particular product in a multi

echelon and multi period supply chain. They formulate this problem as a mixed integer linear model

with the aim of selecting the firms to form the organization as well as addressing the tactical

decisions of each company such as production, inventory, and transportation. The objective

function of the model minimizes the total costs including production, holding raw material and

finished products, transportation costs and capacity as well as alliance associated costs between two

companies. They solved the proposed model by a Lagrangian solution heuristic.

Liu and Papageorgiou (2013) addressed the problem of planning and optimizing the production,

distribution and capacity of a global supply chain of an agrochemical company. In this problem, the

initial capacity of the firm could not satisfy the growing demand and therefore applying a capacity

expansion strategy was required. They formulated the problem with a multi-objective mixed-integer

linear programming (MILP) model which seeks minimizing the total cost, flow time and lost sales.

In other words, they considered cost (cost of raw materials, inventory, transportation, formulation

and duties), responsiveness (total flow time) and customer service level (total lost sales)

simultaneously. They applied ε-constraint method as well as lexicographic mini-max method to

solve the problem and determine the optimal capacity, production and flows as well as level of

inventory and sales.

2.3. Observations of Agile/ responsive SCND literature

Competitive business environment makes the chains to be not only responsive but also lean. The

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results of this responsiveness in the attractiveness of the chain for the customers and the chain's

future income have not been investigated yet. It is frequently mentioned that only responsive chains

can be successful in today's competitive markets, but there is no research considering

responsiveness as a competition factor in the markets. In other words,impact of responsiveness to

the income of the competitors should be analyzed. Being responsive is costly and the trade-off

between responsiveness and its income should be investigated as future research for SCND.The

reason for this gap is the difficulty of predicting this income.There are some other factors that can

be considered such as the index of responsiveness in the competition of the competitors which have

been neglected in the literature. Examples include lead-time required for supplying the ordered

goods or ability of the chain to respond to all the potential demand by keeping extra capacity (or

holding extra inventory). Naturally, customers’ preference determines which indices to select.

3. Green, reverse and closed-loop SCND 3.1. What is greenness?

Traditionally, separate units in organizations were responsible for ensuring environmental

excellence in different steps of firms’ value chain. But nowadays, after the revolutions in quality

concerns (1980s) and also revolution in SCs (1990s), it became clear that in order to have the best

results in considering environmental issues, integration between different ongoing operations is

needed (Srivastava2007). One of the primary concerns of industrialized countries has become

reducing wastes. As the result, in SC literature, some works are focused on physical design of

product recovery networks which also called backward or reverse logistics (RL). The major

objective in this type of studies is to identify the characteristics of these networks; comprising

physical locations, facilities, and transportation links, communicating and transmitting used

products from former users and re-using them. Recovering products occur in three major steps;

firstly used products are collecting from disposer market to recovery facilities, this step is called

collection phase. At last, recovered products are collecting from recovery facilities to the demand

points of the re-use market. This phase is called re-distribution step.

The specific characteristics of product recovery determine the structure of intermediate network.

This network could consist of a simple processing in a single facility or multi complex processing

steps. Green consideration in SC includes any attempt in the design and operating stages of a SC to

reduce the environmental effects of its activities.

3.2. What is reverse and closed-loop SC?

Closed-loop SC.It is possible that disposer market and re-use market coincide (Fleischmann et al.

2002); this case is called closed-loop SC,which is more complex to design due to the interaction

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among collection and re-distribution.

Forward and reverse SC. Reverse SC flow, which considers flow of the waste after the end

consumer emphasizing processes like collection, recovery, recycling, etc, are becoming important.

While most traditional models only consider forward flow, some studies consider reverse flows and

a few consider both flows.The reverse and forward network models have some major differences.

One of the primary differences is the activities which carried out within network. In reverse

networks, the span is considered from a market which sets free used products (disposer market) to

the other one which demands recovered products (re-user market). As it is usually difficult to

forecast the demand for recovered products, uncertainty is a common characteristic of reverse

networks (Fleischmann et al. 2002). While in forward networks it is possible to control the quantity,

quality and timing of inputs.

3.3. What is green SCND?

After emerging of Green SC management (GrSCM) concept, green SCND concerning

environmental considerationsgained a growing interest. The main focus of GrSCM is on decreasing

the deterioration of environmental impacts while gaining higher business profits (Wilkerson 2005).

GrSCM is defined as “integrating environmental thinking into SCM, including product design,

material sourcing and selection, manufacturing processes, delivery of the final product to the

consumers as well as end-of-life management of the product after its useful life” (Fleischmann et al.

2002). Pishvaee et al. (2010) presented a comprehensive review on green, reverse and closed-loop

SCND. Most of the research publications in the literature focus on forward SC flows which is

material flow from upstream toward downstream and end consumers.

3.4. Green SCND literature

Meixell and Gargeva (2005) reviewed and classified the works with focus on decision support

models for designing the network structure of global SCs in SCND literature.They investigated the

related models from four different dimensions: i) decisions made in this kind of problems,ii) metrics

introduced and used to evaluate the performance of global chains,iii) integration degree in the

network design decision making process and iv) how globalization issues considered in the

formulated models. They compared the research works in this field and the real needs and

challenges of real global SCs and concluded that there is not any appropriate correspondence

between them. Finally they proposed some future research to motivate more practically-oriented

research in this field.

Literature of Green SC is very extensivecompared to agile SC. Srivastava (2007) categorizes its

literature into three main groups based on its context in SC:

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Research on the importance of green considerations in SC

Research on green design: This kind of works usually deals with lifecycle assessment of

the product or process. How a product should be designed or a process should work to be

more environmentally friendly?

Research on green operations: These problems deal with operational aspects of reverse

logistics, open-loop or closed-loop SCs and their network design problem. Based on the

classification suggested by this paper, we only focus on the third group. In the field of

SCND, mainly three kinds of works have been done in the proposed third group.

• SCND problems of mainly forward chains with the objective functions of waste

management (reduction in pollution, disposal, etc.): First we review some of the recent works

in the third category, when the objective of the SCND models is minimizing the wastes or

pollutions. Krikke et al. (2003) developed a quantitative model to support decisions on product

and logistics network design. The model was applied to the problem of SCND for refrigerators.

They considered cost as a linear function of the volume with a fixed set-up component for

facilities while environmental issues were considered as linear functions of energy and waste.

Hugo and Pistikopoulos (2005) developed a multi-objective methodology based on operations

research (OR) to explicitly include life cycle assessment (LCA) in process of SCND. Their

model includes strategic (such as site selection, demand allocation and etc.) as well as

operational decisions (including optimal production profiles material flows within the SC). Hugo

et al. (2005) proposed a multi-objective optimization approach for hydrogen networks, with the

aim of investigating the trade-offs between investment and greenhouse gas emissions.

Bojarski et al. (2009) addressed the problem of optimizing the design and planning of SCs

when environmental issues are considered. Strategic decisions considered in this model consist

of locating facilities, selecting processing technology and planning production-distribution. To

perform the assessment of impacts the IMPACT 2002+ methodology was selected and a feasible

implementation of a combined midpoint–endpoint evaluation was provided. Guillen-Gosalbez

and Grossmann (2009) considered the Eco-indicator 99 code (for quantifying the environmental

impact) and formulate a bi-objective stochastic mixed-integer nonlinear program (MINLP) to

maximize net present value and minimize environmental impacts in a model. Pinto-Varele et al.

(2011) also exploit Eco-indicator 99 (particularly, damages to health of human caused by

electricity and diesel consumption over the entire SC) and addressed the problem of SCND when

the aim is to maximize the annual profits with consideration of environmental issues. They

focused on minimization of carbon footprint and used symmetric fuzzy linear programming

(SFLP).Pishvaee and Razmi (2012) presented a multi-objective fuzzy model in order to design

an environmental SC. The model minimizes the multiple environmental impacts and the total

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costs as well. In order to asses and quantify the environmental impacts of alternative SCN

structure, a life cycle assessment-based (LCA-based) method is applied. Moreover, an interactive

fuzzy solution approach is used to solve the proposed model. Wang et al. (2011) proposed a

multi-objective optimization model to address the environmental investment decisions which

make a tradeoff between total cost and the environment influence. CO2 emission considered as

the only environmental index in this model.

• SCND problems of backward flows in reverse logistics or closed / open loop SCs:There are

other green SCND problems that consider environmental considerations from the perspective of

designing the backward network of recollecting, reassembling, remanufacturing, etc. process of

used product. Now,we briefly review some of the recent works in this field.Listes and Dekker

(2005) developed a stochastic programming based approach to address the problem of designing

a two-level network to locate storage and cleaning facilities across a set of potential sites while

the net revenue is maximized. The model is applied to a real case on recycling sand from

demolition waste in Netherlands. Lee and Dong (2008) presented a deterministic programming

model to address the problem of designing a logistics network for recovery of end-of-lease

computer products. In order to reduce the complexity, they proposed a heuristic approach with

two steps which decomposes the primary problem in to a location-allocation and a revised

network flow problem. Min et al. (2006) developed a nonlinear mixed-integer programming

model as well as a genetic algorithm to address the problem of reverse logistics with product

returns. Their main focus is to determine the number of centralized return centers and their

location to collect, sort and consolidate return products which have large distance to repair

facilities.

Srivastava (2008) presented a multi-product, multi-echelon and value recovery network

model to maximize reverse logistics profits. They devised a strategy to reduce the size and

complexity of the problem. They followed a hierarchical optimization approach with two steps.

In the first step, their model finds the location of collection centers by considering strategic and

customer constraints criteria and in the second step, the model determines the location and

capacity addition decisions for rework sites at different time periods. Beamon and Fernandes

(2004) presented a multi-period integer programming model which uses the present value

method to address the problem of designing product recovery networks. The main purpose of the

model is to determine warehouses and collection centers to be open and their sorting capabilities

as well as the amount of material flow between them. Francas and Minner (2009) discussed the

network design problem for a company which manufactures new products and also

remanufactures the used one by replacing some components or reprocessing them. Under

uncertain demand and return flow, they developed two different network configurations and

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evaluate the capacity and performance of each structure. In the first structure, the new and

returned products are considered to be processed by share facilities while in the second ones

separate facilities carry out the manufacturing the new products and remanufacturing the used

ones. Moreover, they considered two market types; firstly, they assumed that manufacturing and

remanufacturing products are perfect substitutes and together satisfy the market demand.

Secondly, they consider a market in which the customers are not indifferent to the new and

reproduced products and each has its own demand. They formulated the problem as a single-

period, two-stage stochastic model and discussed some numerical studies. The results show the

decentralized network is more beneficial for multi-product manufacturing and especially in

separate markets for used and new products while in case of common market, the integrated one

is much more profitable. Francas and Minner (2009) also state that the size of network, costs of

investment on manufacturing capacity as well as the structure of markets have significant impact

on the choice of a network configuration.

• Combination of the above cases:Additionally, there are few works that consider both of the

aforementioned issues simultaneously such as Feng and Gerals (2008). They developed a bi-

objective optimization model to address the problem of designing a reverse logistics network

with returns, in which the returns require repair services. The objectives of the model are to

minimize the total costs and also to minimize the total cycle time tardiness while determining the

arrangement of facilities capacity as well as the associated transportation flows between

customers and the facilities. They applied a combination of scatter search, the dual simplex

method and the constraint method to solve the model. Qiang et al. (2013) addressed the problem

of designing a closed-loop supply chain network which consists of suppliers of raw materials,

manufacturers and retailers. They modeled the problem from the associated entities point of

views to derive their optimum conditions. From the retailer view point, they assumed that each

retailer is responsible to satisfy the uncertain demands of customers by new products or

remanufactured ones in its own demand market while the customers are indifferent to either

product. They also assumed that the recycled products are collected directly from demand

market by the manufacturers. They formulated the problem as a finite-dimensional variational

inequality problem.

It is usually mentioned in the literature of green SC that legislation of countries especially in

Europe and East Asia makes the industries manage their own products when they have reached their

end of life (EOL). However recently a great number of manufacturers became aware of the profit

opportunities of remanufacturing and start product collection and recovery voluntarily. Carpet

(Fishbein 2000), packaging (Rondinelli et al. 1997), tires (Ferrer and Whybark 2003), automotive

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parts (Hormozi 1997), batteries (McMichael and Hendrickson 1998), photocopiers, cellular

telephones and high-tech manufacturers (Ayres et al. 1997) are some of these instances. In addition

to profit opportunity, product take back by manufacturers provides product durability and brand

reputation which is important in today's competitive markets.

Toffel (2004) believes that product remanufacturing reduces production cost of industries,

improves their brand reputations, helps the manufacturers to easily adapt with new emergent

customer expectations and protects the industries aftermarkets. All these factors can improve the

competitive strength of a company in associated markets in order to capture more demand. Some

companies have found out that refurbished components, resulted from recovered take back

products, can be substituted instead of new parts as spares or in remanufacturing process. For

example Mercedes-Benz Company recollects, disassembles, refurbishes the important component

of EOL cars and sell them to its customers with significant discount. Similarly in 1999, Ford

Company started to buy EOL cars in different countries to have another spare parts resource.

Another example is Xerox Company. This company saves millions of dollars yearly by recovering

its EOL photocopiers. As it can be seen, remanufacturing can generate new markets and income

resources for the companies.

Sometimes, a new kind of competition can emerge among the new and recovered products of

companies which can also affect the income of the chain in another way. It is obvious that

remanufacturing strategy, in addition to its environmental effects, can affect the future income of

SCs. Using refurbished components of EOL products can significantly reduce the production costs.

For example Xerox could reduce 20-70 percent of its consumed materials, water and energy by

using refurbished components of its EOL products. Reducing production costs can lead to lower

prices. Lower price is one the most important factor of competition. On the other hand, activating

product remanufacturing in an industry can improve the environmental image of its brand. A survey

done by King and Mackinnon (2002) demonstrates that environmental efforts of a company can

strongly influence the customers’ intention to purchase the products of that company. For example

when Kodak, Fujifilm and Hewlett-Packard started to take back single-use cameras and EOL

computers respectively, they received positive media coverage which influenced their market share.

Therefore, it seems necessary to investigate the effects of using recovery strategies in brand

reputation, captured market share and future income of the chain in competitive markets. Using

these strategies can be considered as a competition factor of a chain. However there are no

quantitative models in the literature that investigate the effects of these factors in the competition

ability of the chain.Recently, new expectations of customers make some companies shift to product

recovery. For example there is a growing trend in the computer industry to lease rather than sell to

customers. Following this strategy has created the necessity of product remanufacturing after

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leasing expiration. For example, business customers in USA expect Dell Company to collect EOL

computers as a service of selling new computers. Additionally, some governments force

governmental agencies to only purchase from companies which recover their own products

(Majumder and Groenevelt 2001). Hence, analyzing the impact of product recovery in customer

behavior and capturing market share seems necessary.

Protecting aftermarkets is the other advantage of product remanufacturing. Aftermarket is the

market of parts and accessories which helps to maintain or improve previous purchases. These

markets are so profitable for companies,because they can attract new customers by supplying like-

new products with prices which are 45 to 65 percent lower than the new ones. Allowing

independent small remanufacturers to enter this market can damage the brand reputation of the new

product and is considered as a threat for the potential demand of these markets. Recent interest of

some auto manufacturers such as Ford and Mercedes Benz in their EOL cars is for precluding

independent competitors from approaching their branded spare parts. Some companies such as

Lexmark offer discount for customers to encourage them to return their printer cartridges for

remanufacturing (Toffel 2004).

It is obvious that greenness of SCs has a significant impact in their attractiveness for customers

and as the result theirmarket share and income. Remanufactured products help the companies to

keep their aftermarket and provide a new income source for it. Using product recovery strategy

improves the reputation of the companies’ brands and enhances their market shares. Producing and

supplying remanufactured products can increase the number of competing products of the markets.

In this case, analyzing the competition of the competing products and predicting their resulted

income is necessary. The point is that greenness by itself can be considered as a competitive

advantage for a SC; therefore, we can look at it as an opportunity (which persuades consumers to

buy) rather than a costly function.Making the chains green consists of several activities such as a)

establishing collecting centers and operational activities of EOL product collecting; b) establishing

refurbishing centers for checking EOL products, disassembling and segmenting valuable

components; c) remanufacturing components; d) recycling materials; e) disposing the disposals. All

these activities impose some fixed and operational costs for the chain. In the SCND stage, designers

should try to impose an appropriate tradeoff between these costs and the predicted income.

Obviously, the need for this kind of efforts in the literature of the green SCND is completely

visible.

4. Sustainable SCND 4.1. What is sustainability?

The World Commission on Environment and Development (WCED) defined sustainability as using

15  

resources to meet present needs while the ability of future generations for meeting their needs is not

compromised. The whole process of designing, sourcing, producing and distributing products

consumes a huge volume of resources and as the SCs concern the product from processing raw

materials to delivery to the customer, they can play a primary role in preserving environmental

sustainability.

Traditionally, sustainability refers to the ability of a business to maintain in the long term. But

this concept has been improved in the 21st century and now is referred to as the ability of an

organization to meet the financial, social and environmental requirements of the present generation

without disabling the future generations to meet their requirements (Brundtland 1987). This new

definition not only considers financial performance, but also addresses environmental and social

performances of the firm as three key issues of sustainability. Although these three aspects may

look unrelated in the first glance, they are strongly interconnected and interdependent in the real

world.Enhancing the scarcity of resources and increasing the cost of energy and other resources are

the main motivations for companies to move toward sustainability. Recently, companies have

realized that considering sustainability as a strategy in all of their business activities can reduce risk,

increase business value, be considered as a competitive advantage, and make tangible and

intangible profits.

4.2. What is sustainable SC?

The need for taking necessary actions towards increasing consumption of natural resources as well

as increasing awareness of society towards environmental issues has changed the conventional view

of SCs and emerged Sustainable SC (SSC) concept.Therefore, sustainability has three main

dimensions as economic, environmental and social (Buyukozkan and Berkol 2011) and SSC

management is defined as managing the flow of material, information and capital along with

cooperation between firms for achieving economic, environmental and social goals. Interested

readers can refer to updated references on sustainable SC; here are several instances: Abbasi and

Nilsson (2012), Gimenez and Tachizawa (2012), Carter and Easton (2011) and Browne et al.

(2005).

4.3. Sustainable SCND literature

Neto et al. (2008) addressed the problem of designing logistics networks in terms of environment

and costs by developing a multi-objective programming model. They considered transportation,

manufacturing, use of products, testing and end-of-use activities as the primary activities which

have an impact on environmental and economic aspects of a logistics network. The model is also

applied in a real case in the European Pulp and Paper sector.

16  

Nagurney and Nagurney (2010) addressed the problem of determining the capacity of several SC

activities of a firm such as manufacturing, storage and product distributing. The objective of their

model is to minimize the total design/operation costs as well as to minimize the emission associated

with manufacturing plants, facilities for storage and transportation modes.Different other research

has been done in the literature of sustainable SCND including Seuring and Muller(2008), Chaabane

et al. (2012), Gupta et al. (2011) and Gunasekaran and Spalanzani (2012).

Some people consider sustainability as a huge cost for a SC. In other words, some believe social

and environmental objective functions have conflict with the main objective of the SC (making

profit). We believe this is not necessarily true if we consider a long period of time.Risk mitigation is

one of the most important benefits of sustainability. Improving the sustainability of a company can

reduce health, environmental, and business risks. For example consider the disaster caused by BP

Deepwater Horizon Company in the Gulf of Mexico. The company concentrated on short term cost

reduction strategy rather than long term sustainability. This inappropriate strategy led to loss of 11

lives (health risk), unrecoverable damage of ecosystems (environmental risk), highly damaged

reputation and financial performance (business risk). The company had to spend $1 billion for

cleaning up and lost $7.5 billion in its shareholder value (Laszlo 2003; Kahn 2010; Bluestein 2010).

In the 21st century major part of large customers require their suppliers to be sustainable (such as

Wal-Mart), in the other case, suppliers face with the risk of lost business to sustainable competitors

in the markets.

The other key benefit of sustainability is process improvement. Companies choose sustainability

as one of their main strategies to try and decrease resource and energy usage, enhance productivity,

and reduce waste in their system. These eco-efficiency activities lead to cost reduction and more

profitability of their organizations. Clif Bar Company is a healthy energy bar producer with $150

million revenue. This company saved $400,000 each year by eliminating shrink wrap from its

packaging systems. An auto dealership in Florida could save $60.000 each year by implementing an

energy efficiency improvement program. Reducing electricity consumption and natural gas

emission are the other advantages of this program for this dealership.

The third advantage of sustainability is leading firms to design and re-design environmentally

friendly products which are made from natural and chemical free materials. For example, some

restaurants try to add some organic food offerings for their customers.

The other advantage of sustainability is opening up new markets for the company or emerging

existing ones with new marketing models. For example, the income of ZipCar Company increased

670 percent between 2005 and 2008 by expanding its market through car rental offering. By

investigating the real needs of untapped auto market segment, they conclude that this kind of people

do not need a car but frequently need one to use for a short time. They recognize that these people

17  

need transportation service not a car. Long term lease models are the other kind of these new

marketing models. Other value source in this category is finding secondary markets for recovered

products and by-products.

The other category of increased business value due to enhanced sustainability is amended brand

reputation, customer and employee loyalty, company culture and stakeholder perception. At first,

StonyField Farm was a small business with few employees and some cows. But customer loyalty

and word-of-mouth promotion transformed it to a worldwide producer of organic daily products in

eighteen years with more than three hundred million dollars annual income. Employee loyalty is

another critical issue for companies. Because the cost of hiring, training, and productivity losing of

a new employee is about 2 times more than that position's annual salary. According to a study

“eighty percent of respondents to a survey in developed countries prefer to work in firms with

reputation of sustainability reputation and this figure is higher in US” (Widger 2007).

The other benefit of sustainability is called “changing the rules”. Leading companies in

sustainability can achieve a position which allows them to set industry standards. Such companies

can benefit from this leadership position. It leaves its less sustainable competitors behind and

solidifies the competitive advantages of the leading firm. Clearly, sustainability is a strong

competitive advantage for companies and can significantly influence the income of the

organizations. Therefore, modeling the consequences of sustainability in the competition ability of

the firms and their future income is necessary in SCND stage. As said before, companies should

consider sustainability as one of their core strategies. Since SCND is a strategic decision, it should

be consistent with all other strategies of the company. Some of decisions related to sustainability

such as selecting objective markets, product and production process redesign affect the network

structure of the chain. According to the literature of sustainable SCND mentioned above, lack of

models to quantify these effects of sustainability in the competition ability of the chain and its

future income is obvious. Ignoring these effects and their consequences leads to a large deviation

from optimal SCN structure and significant losses of the chain.

5. Risk management in SCND 5.1. What is risk?

Risk is usually defined as the probability of an unpleasant event occurrence. Whereas some areas

like project management consider both positive and negative risksoften negative aspects of risk are

highlighted in the literature. Risk management is identification, assessment, prioritization,

monitoring, and control of risk and uncertainty. Some believe that in order to manage risk, we need

to consider phases like preparation, prevention, mitigation, etc. In addition, to address the definition

of risk it is important to discuss its association with uncertainty. Rosenhead et al. (1972) distinct the

18  

concept of risk and uncertainty by dividing the decision making environment to three main groups:

certainty, uncertainty and risk. The uncertainty occurs in situations where it is not possible to

attribute the probability of a decision’s outcome. On the other hand, in a risky environment the

probability distribution of a decision’s outcome is known. However, besides probability

distribution, there are some other ways (such as fuzzy sets and possibilities) to model the likelihood

of a decision outcome (Klibi et al. 2010). As the result, the proposed distinction between risk and

uncertainty is not accepted by all researchers. Uncertainty results to risk, due to the fact that risk is

inherent in uncertain information, models and situations.

Interested readers can refer to Baghalian et al. (2013) to see a comprehensive review about risk,

uncertainty and robustness with focus on SC.

5.2. What is risk management in SC?

Tomlin (2006) presented three different strategies to reduce the effects of uncertainties and

disruptions in SCs asadapting inventory control system, multi-sourcing and also proactively

mitigating risks. Chopra and Sodhi (2004) categorized the associated risks of SCs into eight groups:

delay, forecast inaccuracies, procurement failures, disruptions, system breakdowns, intellectual

property breaches, capacity, and inventory problems. Among these delay, forecast and disruption

are related to physical flows of material and product throughout the chain. Forecast risks results

from the mismatching between the chain's prediction and the actual demand of the markets. In this

paper, we call it demand-side uncertainty just like Lin and Wang (2011). Delay risk occurs when a

supplier of a chain cannot deliver the orders on time due to inside problems of its system such as

overuse of capacities, impairment of facilities, labor dispute and etc. and disruptions occurs when

natural or man-made phenomena such as earthquake, flood, inclement weather, war, labor strike

and etc.,by affecting the production and transportation infrastructure, disrupt the flow of material.

By ignoring the source of the problem, we call both of them supply-side disruption in this paper.

Some of current studies of SCND consider uncertainties under which the SC will operate. Some

factors like market demand, disruption of suppliers in on-time delivery of ordered goods and other

designing parameters are considered uncertain .In today’s business environment, uncertainty is

undeniable.Due to their decentralized nature, SCs are very vulnerable against uncertainties. As SCs

are important part of today’s market, managing SCs risks has become an integral part of SCM

which involves design of a robust SCN structure and management of product flow throughout the

network and make it possible to predict, deal with and recover from the disruption.

According to Sarkar et al. (2002), the labor strike in 2002 results in shutting down of29 ports in

the West coasts of United States and as the result closure of New United Motor Manufacturing

production factory.The recent destructive earthquake in Japan in 2011 made Toyota Motor

19  

Company to close 12 assembly plants which results in production loss of 140,000 autos (Talmadge

and Kageyama 2011).This problem leads to disruption of Toyota’s chain's manufacturing

subsystem.

In addition to the impairment occurred in production facilities and factories throughout Japan,

many Japanese companies faced the problem of supplying the required material as well as fuel and

power. In these types of disasters, the disruptions in supplying and manufacturing are the main

problems of firms. As mentioned by Norrman and Jansson (2004), firing event in one of the most

important suppliers of Ericsson leads to shut down its manufacturing plants for several days. There

are many other real-life showing risks, disruption, uncertainty and their effects on SC performance

(Cavinato 2004). Interested readers can refer to Jüttner et al. (2003) to have more information about

the relationship between categories of risk sources in a network.

5.3. Risk management in SCND literature

Many research publications in the field of SCND consider different sources of uncertainties in their

modeling. Some of them only optimize the expected value of objective functions (stochastic SCND

models) but there are research papers which also consider the performance of the chain in each

realization of uncertain part of model and mainly focus on decreasing the deviation between the

performances of the chain in different situations which lead to robust SCs.

Many works have been done in the literature in the field of managing demand-side uncertainty in

SCND. Now we review some of the recent research.

Aghezzaf (2005) considered a SCND problem with uncertain demand and proposed a robust

optimization approach for the model. Schütz et al. (2009) considered both short-term and long-term

uncertainty in demand. They formulated the multi-commodity SC design problem and solved it in

two stages: i) strategic location decisions are made in the first stage and ii) operational decisions are

made in the second stage. The objective function is to minimize costs, including investment and

expected operating costs. They compared stochastic and deterministic models to solve this problem.

Their solution technique was a blend of sample average approximation (SAA) and decomposition.

Pan and Nagi (2010) formulated a robust optimization model for the problem of SCND when

customer demand is uncertain. They assume that there is an organizational web of partners wishing

to work as an optimal SC and respond to an existing opportunity in the market. This SC produces a

single product and the operation time of each company is one time period. The model tries to

choose one partner for each echelon and makes decision about the production plan, inventory level,

and amount of backorder. The objective function is minimizing total costs (including cost

variability, and a penalty for infeasibility).

El-Sayed et al. (2010) also addressed a multi-product SCND problem with uncertain demand.

20  

Georgiadis et al. (2011) modeled the problem of SCND in case of multi-product production

facilities when production resources, warehouses, distribution centers, customer zones and

operating are shared while demand is time varied and uncertain. They modeled uncertainty as some

probable scenarios and formulated the problem as a MILP. They also applied standard branch-and-

bound techniques to achieve global optimum. In order to show the applicability of developed

model, a case study concerning the establishment of Europe-wide SC is also introduced.

Qi and Shen (2007) introduced unreliable supply in a SC. They considered a multi period

problem in which the retailers of the chain order a certain good from the existing supplier in each

period and the supplier responds to orders via his facilities. Delivered amount of ordered product to

the chain's each retailer includes a probabilistic shortage. Because some portion of it is lost,

mistakes and damages are possible. They modeled the delivered amount as the ordered amount and

a random variable associated with the supplier’s facility which serves the retailer. The objective of

their model is maximizing the SC profit. They considered the given product retail price, facility

location, inventory, and safety stock costs as well as transportation and penalty costs for retailers.

They used a bisection search and outer approximation algorithm to solve the model.

Chopra et al. (2007) considered the flow planning throughout a chain with determined network

structure. This chain consists of a buyer and two available suppliers. First supplier is cheaper, but

prone to disruption and second supplier is completely reliable, but more expensive. In this paper,

they considered supply side uncertainty and highlighted the necessity of decoupling recurrent

supply risk and disruption risk. Disruption is modeled by scenario and recurrent supply is

considered as a random variable with given distribution function.

Yu et al. (2009) considered an order splitting problem in a two tiered SC consists of a

manufacturer and two available suppliers with disruption probability. First available supplier has

lower price but more prone to disruption and in contrast, the second supplier has higher price but

lower disruption probability. Schmitt and Snyder (2010) considered the optimal ordering and

determining the reserve quantities of a two-tiered SC which involves a firm and its suppliers. One

of these suppliers is prone to disruption and has yield uncertainty whereas the second one is

completely reliable and always available but more expensive. They modeled and compared this

problem in two cases: single-period and multi-period and discussed about the advantages of multi-

period consideration in compare to the other. They also use Branch & Bound technique to solve this

problem.

There are also works in the literature that consider uncertainty in different parameters of the

model. Hwang (2002) considered routing decision in SCND problem with stochastic traveling time.

Lowe et al.(2002) considered risk of exchange rate in SCND problem and solve it by a two-phase

multi-screening approach. Alonso-Ayuso et al. (2003) considered a multi-period SCND problem

21  

with uncertain product net price, raw material procurement cost, and demand.They solvedthe

problem by a two stage stochastic programming model.

Snyder et al. (2007) proposed a stochastic multi-period model in which demand, cost and

distance are all random variables and are described by discrete scenarios. They presented a

Lagrangian relaxation based exact algorithm for solving the model. The basic model assumes that

the locations of the retailers are known.

Azaron et al. (2008, 2009) developed a multi-objective stochastic programming approach for SC

design under uncertainty. The uncertain parameters were market demand, supplies of suppliers,

processing cost, transportation cost and capacity expansion cost. The objective functions include (i)

minimization of the current investment and the expected future cost, (ii) minimization of the

variance of the total cost and (iii) minimization of the financial risk. They used goal attainment

technique to obtain the Pareto- optimal solutions.

Nickel et al. (2012) considered uncertainty of the demand and interest rates in a multi-period

multi-commodity stochastic SCND with financial decisions and risk management. The objective of

their model is to minimize the total cost (considering the investments made, the revenues, and the

transportation costs).

Klibi et al. (2010) is one of the most recent review papers in the field of uncertainty in SCND.

They introduce SCND as determining location, number and capacity of production-distribution

facilities in a chain. The paper posits that there are several sources of uncertainty affecting the

design of SCNs and categorizes them from a SCN view point in three main groups including

endogenous assets, SC partners and exogenous graphical factors. Klibi et al. (2010) stated that as

the result of these potential uncertainties the deterministic SCND models could not guarantee the

expected performance of future SCs. In addition, they state that under uncertain environment the

level and quality of available information could vary. Depending on the available information when

making decisions, three main types of uncertainties occur including randomness, deep uncertainty

and hazard. According to the research, although there are some papers on the two first groups,

currently there is not any SCND work on hazard. The paper also argues that to ensure long-term

profitability and sustainable future value creation for all stakeholders, it is not sufficient to evaluate

the performance of a SCN by considering static operational and financial criteria such as economic

value added (EVA), service level, market share and etc. As the result, when designing a SCN, the

SC future performance needs to be evaluated by considering its robustness, responsiveness and

resilience.

5.4. Observations of risk management in SCND literature

In the literature, SC risk management is only considered from the perspective of the costs it can

22  

result. There is not any study which investigates the advantages of risk management for a chain’s

competitiveness and profitability.

Several strategies are used by SCs to mitigate the negative effects of disruption in physical flows

such as having substitutable production and distribution facilities in the chain, having strategic

reserves in different tiers of the chain, and keeping extra capacity of the facilities. Imposing these

strategies needs some considerations that should be planned in the network design stage. Decision

about the number, capacity and location of facilities and the production flow throughout the

network are made in the SCND stage.On the other hand, ability of a SC to supply the demand to the

customers under all conditions, even in crisis, can increase customer loyalty and increase the

attractiveness of the chain in the competitive markets. Being a pioneer in risk management can help

a firm to be able to use the crisis condition to capture the market share of the other competitors

which do not have any plan to supply their customers in these conditions. Quantifying the risk

mitigation ability, as a competitive advantage which influences SC’s ability to capture market share,

is the first step for competitive SCND with risk mitigation considerations. However, there is a lack

of sufficient, precise and practical means to quantify risk and its effects.

6. Other SCND variations Two different production systems are considered in SCND problems: Built-to-stock (BTS) which is

the traditional one used in push SCs, and build-to-order (BTO) which is the recent one used in pull

SCs. Recently, the success ofhigh-tech companies like Dell, BMW, Compaq and Gateway leads to a

great attention to build-to-order SC (BTO-SC) (Ghiassi and Spera, 2003). A lot of companies are

now developing BTO-SCs in order to empower themselves for competing in global market and

secure their market share. In the process of developing BTO-SC, companies focus on mass

customization and e-commerce as well as shortening the cycle of planning and lead-times (Tyan et

al., 2003).Kannan et al. (2009) developed an “integrated forward logistics multi-echelon

distribution inventory supply chain model” (FLMEDIM) as well as “closed-loop multi-echelon

distribution inventory supply chain model” (CLMEDIM) for the built-to-order environment. They

used particle swarm optimization and genetic algorithm (GA) to solve the problem. The model was

applied to two real-world cases in India.

Gunasekaran and Ngai (2009) reviewed a number of papers on analyzing and modeling of BTO-

SC. Their main goal was to pave the way for effective designing, developing and managing BTO-

SC as well as presenting areas of future studies. Lin and Wang (2011) studied SCND under supply

and demand-side uncertainties. They modeled strategies such as postponement, centralized/

decentralized stocking and supplier sourcing base and embedded them in SCND in proactive way

instead of a permissive reaction to uncertainty. This model was implemented for a BTO-SC. There

23  

are a few research papers on BTO-SC and the numbers of studies which explicitly concern the

problem of BTO-SCM with modeling are very limited.

In addition to objective function and production system, the SCND models differ in several

aspects. Some of the models consider a chain for a single product while others discuss multiple

products. Some models consider capacitated facilities while others assume non-capacitated

facilities. Some models are single period and some are multi-period.

7. Framework of SCND problems The specifications of the SCND problems investigated in the literature are so different from each

other. Each of them has some specific assumptions usually imposed by the environmental

conditions and each makes special decisions about the network structure of the chain. In this paper,

we present a framework in SCND including the major research publications in the literature of the

SCND. Figure 2 illustrates this framework and summarizes the strategic questions answered in each

section; these strategic decisions are not usually made all together.

24  

Figure 2. Comprehensive specifications of SCND problems

Network (Wang, 2009; Gumus et al., 2009)

Discrete set (Azaron et al., 2008; Azaron et al., 2009)

SCND models

Network structure of

SC Objective of model

Environmental condition of

model N

umbe

r of f

acili

ties

Loca

tion

of fa

cilit

ies

Cap

acity

of f

acili

ties

Qua

lity

of fa

cilit

ies

Capacitated facilities are considered (Tsiakis et al., 2001; Shih, 2001) Capacity limitation of facilities is ignored (Daskin et al., 2002; Shen et al., 2003)

Quality of facilities is considered (Azaron et al., 2008; Javid and Azad, 2010) Quality of facilities is ignored (Li et al., 2010; Sawik, 2011)

(Georgiadis et al., 2011; Lin and Wang, 2011) Lean SCND (Zanjani et al., 2010; Xu et al., 2008)Agile / responsive SCND (Srivastava, 2007; Wang et al., 2011) Green SCND (Buyukozhan and Berkol, 2011; Chaabane et al., 2012) Sustainable SCND

Unc

erta

inty

/ di

srup

tion

of e

nviro

nmen

t (S

anto

so e

t al.,

200

5; P

eidr

o et

al.,

200

9)

Com

petit

ors /

riva

ls o

f mar

kets

(R

ezap

our e

t al.,

201

1a; R

ezap

our e

t al.,

201

1b)

Net

wor

k flo

ws

Forward flows (Shen and Qi, 2007; Goh et al., 2007) Backward flows (Shih, 2001; Jayaraman et al., 2003) Both flows (Ko and Evans, 2007; Listes, 2007)

Technology type &

production philosophy

(Klibi et al., 2010) Type of technology Determining decomposition point

(Gunasekaran and Ngi, 2009; Lin and Wang, 2011)

Non-strategic decisions of

SC

Flow of material among facilities (Shen, 2005; Shen and Daskin, 2005) Transportation mode (Klibi et al., 2010) Inventory decisions (Daskin et al., 2002; Qi and Shen, 2007) IT application (Gunasekaran et al., 2008) Knowledge management (Gunasekaran et al., 2008)

25  

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