technology capability , and service) and four green criteria (total pro-
duct life cycle cost, green image, pollution control, and environm ental
management). Hashemi et al. (2015) proposed an a grey relation
analysis and ANP for green supplier selection, accounting for primary
criteria of cost, quality, and technology and green criteria of pollution
production, resource consumption, and management commitments.
Huang and Keskar (2007) applied AHP with carbon footprint con-
siderations, along with finance, delivery, service, and organizational
performance. Zhang et al. (2013) developed a nonlinear multi-objec-
tive optimization model for green supplier selection that accounted
for pollution emitted by gasoline consumption during tr ansportation,
cost, delivery rate, transportation time, and service level, solved with a
Pareto genetic algorithm. Akman (2015) integrated fuzzy c-means and
VIK OR methods to evaluat e green suppliers based on green design,
pollution prevention, green image, green capability, and environ-
mental management. Kumar and Jain (201 0) developed a DEA model
considering carbon footprint monitoring. Mahdiloo et al. (20 15) also
applied DEA considering technical and environmental criteria, referred
to as an eco-efficiency measurement. Theiben and Spinler (2014)
evaluat ed the efficiency of green suppliers based on their CO
2
emis-
sion level using ANP. Kuo et al. (201 4) developed a carbon footprint
inventory route model based on the vehicle routing pr oblem.
3. Development of resilient supplier selection criteria
The ability to withstand, adapt to, and recover from a disrup-
tion is generally referred to as resilience,adefinition with which
many would largely agree (Haimes 2009, Aven 2011, Barker et al.,
2016). Resilience is a concept that is increasingly gaining traction
in government, industry, academia, and popular science (Park
et al., 2013, Zolli and Healy, 2013, Hosseini et al., 2016).
Resilient supply chain practices have been a well-studied topic
for the last decade or so. Particularly in a supply chain context,
Sheffi (2005) defined the resilience of a firm within a supply chain
as its inherent ability to maintain or recover its steady state be-
havior, thereby allowing it to continue normal operations after a
disruptive event. Rice and Caniato (2003) highlighted that supply
chain resilience in the upstream level could be enhanced with the
multiple-sourcing of suppliers, sourcing strategies to allow
switching of suppliers, and commitment to contracts for material
supply. Christopher and Peck (2004) emphasized that developing
visibility to a better view of upstream inventories and supply
conditions would positively contributes to the resilience of supply
chain context, while Tang (2006b) pointed out the importance of
flexible supply base (sourcing).
However, in contrast to the extensive work to explore the role
of primary and green criteria in the supplier selection problem,
accounting for the concept of resilience in supplier selection is
relatively new and with no consensus on factors contributing to
the resilient characteristics of suppliers. Rajesh and Ravi (2015)
proposed a grey relational analysis method for selecting suppliers
considering vulnerability, collaboration, risk awareness, supply
chain continuity management for selection of resilience suppliers.
Torabi et al. (2015) developed a two-stage stochastic programming
model to solve a resilient supplier selection and allocation pro-
blem under operational and disruption risks, accounting for four
resilience-building strategies including supplier business con-
tinuity plans, extra inventory maintained by the supplier, for-
tification of suppliers, and contracting with backup suppliers. Sa-
wik (2013) investigated the problem of developing a resilient
supply portfolio, including the pre-positioning of emergency in-
ventory as a primary strategy to mitigate the effects of a
disruption, using a mixed integer programming model with con-
cepts from value-at-risk and conditional value-at-risk. Sawik
(2016) proposed a risk-averse optimization model in the presence
of a supply chain disruption with two different service levels
measures: the expected worst-case demand fulfillment rate and
the expected worst-case order fulfillment rate with consideration
that suppliers are geographically dispersed. Haldar et al. (2014)
proposed a fuzzy group decision making approach for resilient
supplier selection where the importance degrees of supplier at-
tributes are expressed in terms of linguistic variables. Reyes and
Nof (2015) proposed resilience by teaming (RBT) association de-
cisions, inspired in the “fault-tolerance by teaming” principle from
collaborative control theory to form network and re-configure
mechanisms. The main findings of their research show that supply
chain networks using RBT association rules result in increased
quality of service with no signifi
cant cost increases for normal
operations.
Vugrin et al. (2011) defined the resilience capacity of a system
as a function of the absorptive, adaptive, and restorative capacities
of the system, clearly identifying pre-disruption and post-disrup-
tion planning. We make use of this concept of resilience capacity
and its three dimensions to explore the factors contributing to a
resilient supplier in the supplier selection problem.
Absorptive capacity is the extent to which a system (or a sup-
plier in the context of this study) is able to absorb shocks from
disruptive events, implying proactive planning for resilience or the
development of pre-disaster strategies that can be considered as a
first line of defense. Absorptive capacity can be viewed as being
endogenous to the system (Vugrin et al., 2011). It is similar to the
concept of inherent resilience described by Rose (2009) as the
“ordinary ability to deal with crises.” Features of absorptive capa-
city in the context of supplier selection are proposed here.
Geographical segregation: Segregation or separation of a supplier
geographically from natural disasters can reduce the likelihood
of adverse impacts on the supplier if the disaster occurs. Al-
luded to previously in the discussion of automakers after the
Japanese earthquake and tsunami, Nissan and Toyota requested
that their part suppliers establish facilities that are geo-
graphically separated from disaster prone areas. Note that not
only should the location of suppliers be segregated from natural
disasters but also the location of suppliers in a multi-sourcing
supply chain network (Vugrin et al., 2011).
Surplus inventory: Although maintaining more on-hand in-
ventory may increase holding costs, it can also enhance the
ability of the supplier to absorb a disruptive event. Note that
pre-positioned inventory levels are restricted by space avail-
ability. Torabi et al. (2015) discussed that pre-positioning extra
inventory can enhance the resilience of a supplier. Turnquist
and Vugrin (2013) developed a stochastic model for design of
resilience in infrastructure distribution networks, and they treat
extra inventory as a feature of absorptive capacity in a distribu-
tion center. Little (2005) suggests that New York City’s recovery
following the terrorist attacks of September 11, 2001, would
have been hampered had more organizations taken an inven-
tory reduction (e.g., just-in-time) philosophy.
Backup supplier contracting: A disrupted supplier may contract
with a backup supplier to fulfill manufacturer orders. Such a
contract is assumed to be in place prior to a disruption. Con-
tracting with a backup supplier can be viewed as a form of re-
dundancy, a common absorptive capacity enhancement philo-
sophy in infrastructure systems (Vugrin et al., 2011).
Physical protection: Physical protection and facility safety can
S. Hosseini, K. Barker / Int. J. Production Economics 180 (2016) 68–87 71