at SciVerse ScienceDirect

Journal of Loss Prevention in the Process Industries 26 (2013) 1238e1245

Contents lists available

Journal of Loss Prevention in the Process Industries

journal homepage: www.elsevier .com/locate/ j lp

An assessment of organizational resilience potential in SMEs of theprocess industry, a fuzzy approach

Aleksandar Aleksi�c*, Miladin Stefanovi�c, Slavko Arsovski, Danijela Tadi�cFaculty of Engineering, University of Kragujevac, Serbia

a r t i c l e i n f o

Article history:Received 25 March 2013Received in revised form10 June 2013Accepted 15 June 2013

Keywords:Organizational resilienceFuzzy sets

* Corresponding author.E-mail addresses: aaleksic@kg.ac.rs (A.

(M. Stefanovi�c), cqm@kg.ac.rs (S. Arsovski), galovic@k

0950-4230/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.jlp.2013.06.004

a b s t r a c t

In order to establish adequate tools for the modern business environment, and with a need for newmechanisms with the goal of overcoming crisis and emerging disorder, the concept of organizationalresilience has emerged. A high level of organizational resilience represents one of an organization’starget values during a normal period of operation. In a period of crisis, the presence of resilience is evenmore needed; this is emphasized in the process industry because in these conditions when one processfails it may cause significant problems in other processes. The contribution of this paper is shownthrough a fuzzy mathematical model for assessment of organizational resilience potential in SMEs of theprocess industry. The model is verified through an illustrative example where obtained data suggestmeasures which should enhance business strategy and improve organizational resilience factors. Thisstudy forms the basis for a survey that may include a significant number of organizations from oneregion and future improvement based on benchmark and knowledge sharing.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Business practice shows that all potential risks (Spekman &Davis, 2004) and their consequences cannot be identified in orga-nizations, no matter how big they are or howmuch profit they gain.On the other hand, modern business is becoming increasinglycomplex, this is caused by the development of new technologies,which include information and communication technologies(ICT). Complexity and variable business conditions present thesources of risk that need to be managed, in the long term, in orderto ensure the sustainability of an organization (Afgan, Hovanov, &Andre, 2009). Mechanisms that are traditionally used by organi-zations to deal with these sources of risk, e.g. risk management(ISO 31000:2008) or Business Continuity Management (BS25999:2006), seem to be insufficient because most of the organi-zations are often facedwith serious issues and some of them cannotachieve long term sustainability. An important need is to open thepossibility of organizational resilience potential assessment and toclearly define its indicators (factors) which should present a clearpicture of the organization’s position in the market. This allows theopportunity to compare organizations and to enable their bench-mark. The final consequence of this should be the defining of

Aleksi�c), miladin@kg.ac.rsg.ac.rs (D. Tadi�c).

All rights reserved.

appropriate measures to improve business performance and toenable long term sustainability.

Motivation for this research is inspired by the fact that there isno unique mathematical model for assessment of organizationalresilience potential which is widely accepted (Bhamra, Dani, &Burnard, 2011). The existing assessment models treating resil-ience of organizations (Gibson & Tarrant, 2010; Stephenson, Vargo,& Seville, 2010) and supply chains (Pettit, Fiskel, & Croxton, 2010)are continuously improving. The reasons for this situation can befound in the fact that there is no unique taxonomy of risks thatcould endanger an organization, nor a single list of organizationalresilience factors. Since organizational resilience potential isdescribed by imprecise data, this paper proposes an assumptionthat the relative importance of organizational resilience factors andtheir values are uncertainties, and they may be described by lin-guistic expressions. Modeling of these linguistic expressions isbased on the fuzzy set theory (Klir & Folger, 1988; Zimmermann,2001). The fuzzy set theory supports the subjective natural lan-guage descriptors of organizational resilience and provides amethodology for allowing them to enter into the modeling process.The focus of this paper is on organizations from the process in-dustry. The process industry may be seen as a production industryusing (raw) materials to manufacture non-assembled products in aproduction process, where the (raw) materials are processed in aproduction plant, where different unit operations often take placein a fluid form, and where different processes are connected in a

A. Aleksi�c et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 1238e1245 1239

continuous flow. Having in mind the continuous flow and con-nected process, it is very important to achieve resilience of suchorganizations. The proposed industries to be included in the frameof the process industry are among others: mining andmineral, foodand beverage, pulp and paper, chemical, basic metal and otherprocess industries. Although some of the companies from theprocess industry are large and multinational with sufficient re-sources to keep their organizations able and resilient, a number oforganizations from this sector are small and medium sized enter-prises (SMEs). Many scholars have established definitions of whatconstitutes an SME (Deros, Yusof, & Salleh, 2006). The category ofmicro, small and medium enterprises is made up of enterpriseswhich employ fewer than 250 persons and which have an annualturnover not exceeding 50 million euros, and/or an annual balancesheet total not exceeding 43 million euros (Article 2 of the Annex ofRecommendation 2003/361/EC). It is also clear that SMEs arerecognized as an important sector in the process industry fordeveloping countries (for instance food and beverage) or as thepolicy in developed countries aiming to stimulate entrepreneur-ship in Europe, creating more jobs in the process industries (moreinteresting and higher quality jobs), the research community(world class research) and high-tech SMEs (new eco-efficient pro-cess technologies) by creating new markets.

Communities and business organizations might be conceptual-ized as a complex system (Crichton, Ramsay, & Kelly, 2009). Sus-tainability of a complex system, in this case an organization, isobtained through the constant interaction of its integral parts andenvironment. Within a changing environment, capable of signifi-cant turbulence, an organization is required to change, adapt and beresilient, in response to environmental fluctuations. The maincontribution of this paper is the introduction of a fuzzy model forassessment of organizational resilience potential. The model isbased on fuzzy mathematical support which makes it a robust andreliable tool. Bearing in mind that a model for the assessment oforganizational resilience potential is proposed, it is followed by anorganizational reference model, a definition of relevant organiza-tional resilience factors and, in the end, by a mathematicaldescription of the model achieved by using the theory of fuzzy sets.The proposed model does not define a unique set of organizationalresilience factors but a conceptual model, convenient for quantifi-cation that is reliant on existing work (Dinh, Pasman, Gao, &Mannan, 2012; Gunasekaran, Bharatendra, & Giffin, 2011;Stephenson et al., 2010).

2. Literature review

Resilience has been widely examined in the context of ecosys-tems (Folke, 2006) and socio-ecological systems (Adger, 2000).From the socio-ecological perspective, scholars have agreed thatstudying organizational resilience requires an interdisciplinaryapproach and that a system approach represents an adequate so-lution.Within a system approach, resilience and its components aredefined differently in varying perspectives (Bhamra et al., 2011). Inthe field of engineering, resilience is seen as the ability to sense,recognize, adapt and absorb variations, changes, disturbances,disruptions and surprises (Hollnagel, Woods, & Leveson, 2006).

If resilience is the focus of an organizational perspective (Hamel& Valikangas, 2003), it conveys the properties of being able to adaptto the overall requirements of the business. Organizational resil-ience implies the ability of an organization to withstand systematicdiscontinuities as well as the capability to adapt to new risk envi-ronments (Starr, Newfrock, & Delurey, 2003). Different definitionsof resilience have an influence on its constituent elements and itsassessed values in real business organizations. The mentioned is-sues have not allowed the creation of a scientific consensus on the

constituent elements of organizational resilience nor an appro-priate methodology for its comprehensive assessment.

Globalization has significantly increased clients’ expectations allover the world. SMEs have to be innovative and be able to adapt tonew challenges (Lee, Shin, & Park, 2012). In order to achieve this,SMEs have to combine old and new business models and toimprove their resilience. This is very important because SMEs formthe backbone of the EU economy e accounting for 99.8 per cent ofnon-financial enterprises in 2012, which equates to 20.7 millionbusinesses (Wymenga, Spanikova, Barker, Konings, & Canton,2012). In employment terms, SMEs provided an estimated 67.4per cent of jobs in the non-financial business economy in 2012which is very significant for the EU economy.

When SMEs are the focus, achieving resilience is determined bythe market and by the SMEs own properties. SMEs have a limitedapproach to resources (Vossen, 1998) which makes them open andvulnerable to the external environment, so they have to define anappropriate strategy and assure the resources for achieving resil-ience. Potential for organizational resilience can be developed andmanaged through a business strategy. Defining the business strat-egy, aligned with strengthening resilience, may have an influenceon sustainability of an SME and have an impact on longer termbusiness performance (Lengnick-Hall, Beck, & Lengnick-Hall, 2011).If human involvement within organizations is in resilience focus,organizations should possess the resilient qualities of human re-sources since an organization is composed of the business and thepeople who are running it. Improvement in the field of HR mayresult in developing a capacity for organizational resilience whichmay lead to an effective analysis and response to differentdisruptions.

Besides relying on human resources in an organization, manyscholars have appointed tools for improving organizational resil-ience, such as defining resilience antecedence (Demmer, Vickery, &Calantone, 2011), or have proposed a conceptual framework forresilience improvement as a continual change process (Ates & Bitici,2011). The continual change and improvement go in favor of thephilosophy of quality and the process approach which is one of thebasic assumptions of this paper.

The concept of resilience is not new for the process industry,especially for large and multinational organizations. One of the keyissues in resilience is the ability to perform continuous monitoringof a system and to follow indicators (factors) to identify the limitsand the position of the system (Vidal, Carvalho, Santos, & dosSantos, 2009). The oil and gas industry use process oriented anal-ysis such as HAZOP to enhance resilience, and complex organiza-tions such as a power plant may conduct incident analyses(Carvalho, dos Santos, Gomes, & Borges, 2008). On the other hand,there is a wide range of SMEs with the need for a simple and reli-able tool for assessment of organizational resilience, having inmindthat different processes are connected in a continuous flow.

Organizational resilience might be analyzed as a fuzzy issue(Pendall, Foster, & Cowell, 2010) since a lot of events described withinsufficient data, such as shocks, or “slow burns”, have an influenceon it. The concept of organizational resilience potential may seemto be an intangible one, but there can be identified appropriateorganizational features which can reflect the overall size of it(Somers, 2009). In order to keep the measurement consistent, theassessment should be done on the level of organizational units withassets that can be influenced by managers. If industrial processesare the focus, contributing factors of resilience (Dinh et al., 2012)may be assessed, which can give a clear picture of the state of theprocesses and their ability to bounce back if disturbance occurs.Since there are a lot of variables that may influence resilience ofcritical processes (Carvalho et al., 2008) and the overall resilience ofan organization, it may be helpful to employ a fuzzy approachwhile

A. Aleksi�c et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 1238e12451240

doing the assessment of resilience. While assessing and enhancingorganizational resilience, an organization may be presented usingan appropriate model (e.g. the Viable system model) which can bedescribed by fuzzy numbers (Chan, 2011). This represents the basicidea of this paper which is to propose a conceptual model forassessment of organizational resilience potential in SMEs which isbased on resilience factors and the process approach.

3. A model for assessment of SME resilience potential

The elements of the model for assessment of organizationalresilience potential in this paper are defined resilience factorswhich are assessed in the scope of the reference model of anorganization.

The reference model may be seen as a general model of an or-ganization which can be used for gaining other forms of models. Inorder to define an organization and to integrate its parts, it ispossible to use different reference models (PERA (Purdue Organi-zation Reference Model), reference standardeISO 14258eConceptsand Rules for Enterprise). The motivation for process approachemployment within the presentation of an organization referencemodel has emerged from the fact that it has been widely used inmany different fields, especially in the field of quality. Analyzing anorganization as a single concept is not adequate because an orga-nization must have connections with external entities, in order toperform its function, since it is part of a supply chain or businessnetwork (Möller & Rajala, 2007). Since at an organization level,resilience results from processes and dynamics which create andretain resources (Vogus & Sutcliffe, 2007), it can be assumed that anappropriate referencemodel of an organizationmay be obtained bythe process approach. It implies that an organization is viewed as anetwork of interrelated processes that are focused towardachieving organizational goals (Oakland, 2004), so in this paper, theorganization is represented by n of its processes. In this case, thenumber, nature and scope of business processes which are identi-fied in the analyzed group of SMEs depend on many factors such asthe economy sector, company size or types of business activities.

The basic assumption of the new resilience potential assessmentmodel is to propose a network of processes that will represent anorganization and measure resilience potential of each processbased on resilience potential factors. An organization referencemodel is impacted by internal, external and management(enabling) elements (Gunasekaran et al., 2011) which imply theneed for each of these elements to be presented using resiliencepotential factors. There is no unique division of resilience factorswhich would completely determine organizational resilience po-tential, and the proposed resilience potential factors have beenadopted from existing models (Dinh et al., 2012; Gunasekaran et al.,2011; Stephenson et al., 2010) and they are clarified in section 4.2.The proposed internal factors for the assessment of organizationalresilience potential are: (1) planning strategies, (2) capability andcapacity of internal resources, (3) internal situation monitoring andreporting, (4) human factors and (5) quality. The external resiliencepotential factors are: (6) external situation monitoring andreporting, (7) capability and capacity of external resources. Theenabling resilience potential factors are: (8) design factor, (9)detection potential, (10) emergency response, (11) a safety man-agement system. The proposed resilience factors should beassessed annually so organizational resilience potential may bemonitored during a period of time. Assessed organizational resil-ience potential should be scoped in one of the three proposed re-gions (defined in section 4.3) which are the baseline fororganizational resilience assessment. This will provide sufficientdata for top management who should manage an organization’sresilience over a period of time.

The proposed resilience factors have been chosen with theintention of fitting the needs of the SME process industry. Theymaybe compared with the organizational resilience factors presentedby the model of Somers (2009). The factors proposed by the newmodel significantly exceed the overall resilience potential influenceon an organization compared to the mentioned model. The scale ofmeasure of the proposed model is defined on the interval 0e1 andit is arranged by fuzzy numbers, in comparison to the mentionedmodel and its Likert scale.

Resilience factors need to be assessed on the process level inthe proposed model which means that the existence of regulatedprocesses provides the main constraints to the model. An assess-ment is defined by linguistic expressions which rely on fuzzy sets.The need for having resilient processes is aligned with the goal ofachieving high performance and long term sustainability of an or-ganization. A management team (the top manager or owner,quality manager and financial manager who are familiar withthe organizational processes and their condition) assess the orga-nizational resilience. Uncertainties in: (1) the relative importanceof organizational resilience factors for each business process, (2)the organizational resilience factor values, and (3) the regionsof organizational resilience potential are considered. These un-certainties are described by predefined linguistic expressionsbecause it can be assumed that a management team’s estimates arefar better shown by linguistic expressions than precise numbers.The number and type of linguistic expressions defined by a man-agement team depend on the type of economic activity, organiza-tional size, or some other condition.

4. Modeling of uncertainties and organizational resiliencefactors

This modeling of uncertainty of the proposed fuzzy model isdescribed in this section. It is assumed that uncertainties aredescribed by predefined linguistic expressions. According toZimmermann (1997), the fuzzy sets theory could be the mostappropriate way for modeling linguistic expressions.

A fuzzy set is represented by its membership function for whichthe parameters are shape, granularity and location on the universeof discourse. The membership function shape of a fuzzy set canbe obtained based on one’s experience, the subjective belief ofdecision makers, intuition and contextual knowledge about theconcept modeled (Zimmermann, 1978), and uncertainty availableon the treated linguistic variables (Berkan & Trubatch, 1997).However, subjectivity in determining membership function hasbeen considered as theweakest point in fuzzy sets theory (Petrovi�c,1997). In the literature (Markowski, Mannan, & Bigoszewska, 2009),the triangular and trapezoidal fuzzy numbers are commonly usedfor the modeling of different types of uncertainties. Fuzzy sets ofhigher types and levels have not, as yet, played a significant role inthe applications of fuzzy sets theory (Klir & Yuan,1995) because themembership function of higher order makes mathematical calcu-lation more complex and at the same time it does not improve theaccuracy of the calculation. The authors’ motivation for employ-ment of the triangular fuzzy numbers has arisen from the fact thattriangular fuzzy numbers offer a good compromise betweendescriptive power and computational simplicity.

Granularity is defined as the number of fuzzy numbers assignedto the organizational resilience factors and their values. Lootsma(1997) suggested that only seven categories, at most, can be used.

In general, the domain of fuzzy sets can be defined on differentmeasurement scales. In this paper, the domains of the triangularfuzzy numbers which describe the relative importance of theorganizational resilience factors, the organizational resilience fac-tor values and the regions of organizational resilience potential are

A. Aleksi�c et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 1238e1245 1241

defined on the scale [0e1]. The value 0 denotes that organizationalresilience factor i, i ¼ 1,..,I has very low importance for businessprocess p, p ¼ 1,..,P, and has the lowest value, as well as the lowestlevel assigned to the low resilience region, respectively. Value 1stands for the highest relative importance of organizational resil-ience factor i, i¼ 1,..,I for business process p, p¼ 1,..,P, and its highestvalue, and the very safe situation assigned to the high resilienceregion, respectively.

4.1. Modeling of the relative importance of organizational resiliencefactors

In order to closely assess the relative importance of resiliencefactors, the management team, doing an assessment, shouldanalyze the influence of factors on a process level. All the organi-zational resilience factors for the considered business processes arenot usually of the same relative importance. Also, they can beconsidered as unchangeable during the considered period of time(one year). The relative importance of the organizational resiliencefactors can be changed if strategic management defines a newbusiness strategy. The relative importance involves a high degree ofsubjective preferences of the management team.

In this paper, it is assumed that each decisionmaker assesses therelative importance of the organizational resilience factor for eachbusiness process. The management team should analyze resiliencefactor importance on the defined business processes. The fuzzyrating of each decisionmaker is described by one of the five definedlinguistic expressions which can be modeled by triangular fuzzy

number Ww e

ip ¼ ðx; leip;meip;u

eipÞ with the lower and upper bounds

leip;ueip and modal value me

ip, respectively.

These linguistic expressions are modeled by triangular fuzzynumbers which are given in the following way:

very low importance-Rw

1 ¼ ðx;0;0;0:2Þlow importance-R

w

2 ¼ ðx;0;0;0:4Þmoderate importance-R

w

3 ¼ ðx;0:2;0:5;0:8Þhigh importance-R

w

4 ¼ ðx;0:6;1;1Þvery high importance-R

w

5 ¼ ðx;0:8;1;1Þ

4.2. Modeling of organizational resilience factor values

The organizational resilience factor values at the level of eachbusiness process cannot be stated quantitatively because decisionmakers most often base their estimates on evidence data, experi-ence, knowledge, etc. In this paper, the authors have introduced theorganizational resilience factor which should be clarified in favor ofeasier assessment.

The internal resilience factors framework (Gunasekaran et al.,2011) is mostly concerned with management issues and, besidesquality, organizational behavior and managerial characteristics arefurther developed based on the evidence from literature (Lengnick-Hall et al., 2011; Stephenson et al., 2010). Planning strategies is afactor that is associated mostly with the process of managementand strategy and it should be assessed in the manner of thedeployed strategies for achieving resilience. Defining an appro-priate strategy is crucial in a time of disruptions for managingresilience. The capability and capacity of internal resources is afactor that has an impact on any business process because withoutresources no process can bounce back, which is one of the resiliencebase assumptions, and operate normally. This factor should beassessed in the manner of adequate management of the processingof internal resources and existing procedures in a time of crisis.

Internal situation monitoring and reporting is a factor that shouldbe assessed in the scope of an organizational information systemand assets that are dedicated to organizational awareness. Whenhuman factors are assessed in the scope of organizational resiliencepotential, the management team should mostly analyze the level ofcompetencies of human resources and their motivation. Quality inthe organizational resilience assessment potential represents oneof the basic assumptions and it implies that an organization can beviewed as a network of interrelated processes that are directedtoward achieving organizational goals. This factor should beassessed in the scope of the defined quality goals and their overallmonitoring and measurement.

External resilience factors (Gunasekaran et al., 2011) have thegreatest impact on the processes that have a relation with externalentities, such as sales or purchasing. These factors are developedand analyzed based on the evidence from literature (Stephensonet al., 2010). External situation monitoring and reporting repre-sents a factor that should be assessed in the scope of informationprocessing from outside the organization through the informationsystemwithin the organization. Capability and capacity of externalresources is a factor that should be analyzed in the scope ofadequate management of process resources which are introducedfrom outside the organization, such as power supply or electricity.

The enabling resilience factors (Gunasekaran et al., 2011) areclosely connected to the production process although they mayhave an influence on other business processes, such as mainte-nance. These factors specially fit the process industry and resilienceengineering of industrial processes (Dinh et al., 2012). The designfactor should be analyzed and assessed in the scope of the pro-cesses’ design as well as the industrial assets’ design. The detectionpotential should be analyzed in the scope of any possible deviationfrom process realization and its assessment should take into ac-count the defined procedures for detection of faults within theprocesses. Emergency response assessment should take into ac-count existing procedures for organizational response whichshould be implemented in a time of crisis. During assessment, thesafety management system should be analyzed with the pro-cedures associated with safety of employees and the safety of in-dustrial assets.

In this paper, the organizational resilience factor values areadequately described by linguistic expressions which can be rep-resented as triangular fuzzy numbers v

wip ¼ ðx; Lip;Mip;UipÞ with

the lower and upper bounds Lip;Uip and modal value Mip,respectively.

For the problem considered in this paper, five linguistic ex-pressions are proposed, which are modeled by triangular fuzzynumbers as follows:

very low value- vw1 ¼ ðy;0;0;0:2Þ

low value- vw2 ¼ ðy;0:1;0:3;0:5Þ

medium value- vw3 ¼ ðy;0:3;0:5;0:7Þ

high value- vw4 ¼ ðy;0:7;0:9;1Þ

very high value- vw5 ¼ ðy;0:8;1;1Þ

4.3. Modeling the regions of organizational resilience potential

An organization may have different levels of resilience potentialand this paper proposes the defining of the three state regions oforganizational resilience potential (Somers, 2009) e low resilienceregion, mid-point resilience region, and high resilience region. Theproposed regions represent the baseline for organizational resil-ience assessment which means that the obtained organizationalresilience potential values will fit into one of them. The regions of

A. Aleksi�c et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 1238e12451242

organizational resilience potential can be modeled by one of thethree predetermined linguistic terms. These linguistic expressionsare modeled by trapezoidal fuzzy numbers.

The trapezoidal fuzzy numbers for modeling the regions oforganizational resilience potential are:

� Low resilience region e Sw

1 ¼ ðx;0;0;0:2;0:4Þ� Mid-point resilience region e S

w

2 ¼ ðx;0:1;0:3;0:5;0:7Þ� High resilience region e S

w

3 ¼ ðx;0:6;0:8;1;1Þ

Since the overlap from one trapezoidal fuzzy number to theother is very high, it obviously indicates that there is a lack ofknowledge about the regions of organizational resilience potentialor a lack of sufficient partitioning. The proposed values of thedefined regions represent the initial draft assessed by experts’opinion in the SME process industry in Serbia. These values may bechanged and adjusted according to the specific needs of the treatedgroup of organizations.

5. The proposed fuzzy model for an assessment oforganizational resilience

For the management team carrying out the analysis, thefollowing tasks are important: (1) to determine the organizationalresilience factor which has the lowest impact on reduction of theresilience potential, (2) to determine the resilience value of eachbusiness process, (3) to determine organizational resilience of anenterprise and (4) to determine the region of an organization’sresilience potential.

In general, the management team defines P, different businessprocesses, in any enterprise which can be formally presented usinga set of indices P ¼ f1;.; p;.; Pg. The index for a business processis denoted as p. The organizational resilience factors are presentedby a set of indices I ¼ f1;.; i;.; Ig. The total number of organi-zational resilience factors is I, and i is the index of these factors.

The organizational resilience factors have different relativeimportance for the identified business processes in an organization.It almost does not change at all over time. The fuzzy ratings of theorganizational resilience factor importance are performed by eachdecision maker and they are modeled by triangular fuzzy numbersWw e

ip ¼ ðx; leip;meip;u

eipÞ. The organizational resilience factor values at

the process level can be described using linguistic expressionsmodeled by the triangular fuzzy numbers v

weip ¼ ðx; Leip;Me

ip;UeipÞ,

i¼ 1,...,I; p¼ 1,...,P; e¼ 1,...,E. Determining the relative importance ofthe organizational resilience factor i, i ¼ 1,...,I for each businessprocess p, p ¼ 1,...,P and determining organizational resilience fac-tor values can be stated as a group decision making problem. Theaggregation of individual opinions of decision makers, which haveunequal importance in the group consensus, is performed by thefuzzy ordered weighted averaging operator (FOWA) which isexplained in literature (Chen & Chen, 2003; Merigó & Casonovas,2008). The FOWA operator is an extension of the orderedweighted averaging (OWA) operator (Yager, 1988). The relativeimportance of each of the organizational resilience factors for eachbusiness process are denoted as, w

wip ¼ ðx; lip;mip;uipÞ and their

values are denoted as, Vw

ip ¼ ðx; Lip;Mip;UipÞ.The fuzzy decision matrix, D

w¼ ½d

w

ip�IxP is constructed. The ele-

ments of this matrix dw

ip; i ¼ 1;.; I; p ¼ 1;.; P are calculated as amultiplication of the organizational resilience factors’ relativeimportance,w

wip and their values, v

wip; i ¼ 1;.; I; p ¼ 1;.; P. Fuzzy

operation multiplication does not give triangular fuzzy numbers,but it is possible to express approximated values o as triangularfuzzy numbers (Kwang, 2005).

In this case, the fuzzy IFeTHEN rules must describe the relationbetween organizational resilience and the region of organizationalresilience potential. In general, there are a number of ways fordetermining the IFeTHEN rules. In this paper, the rules are builtfrom the management team’s knowledge, experience and fromdata. Because of that, it can be said that the system is simplified bydiscarding the least significant rules. There are three productionrules modeled by the triangular fuzzy number S

w

q; q ¼ 1;2;3.The Algorithm of the proposed fuzzy model is presented as

follows:

Step 1. Calculate the aggregated relative importance andaggregated value of each of the organizational resilience po-tential factors by using FOWA (Chen & Chen, 2003; Merigó &Casanovas, 2008).

FOWA�wwe

ip

�¼

X3e¼1

we$be and FOWA�Vwe

ip

�¼

X3e¼1

we$Be

where:

we is the importance of the decision maker e, e ¼ 1,..,E

be, Be is the eth largest of the fuzzy numbers wwe

ip; Vwe

ip, respec-

tively. The comparison of triangular fuzzy numbers wwe

ip; Vwe

ip isbased on the method which is presented in literature (Bass &Kwakernaak, 1977; Dubois & Prade, 1980).Step 2. Calculate the weighted values of the organizationalresilience potential factors,

dw

ip ¼ �x; aip;bip; cip

�

dw

ip ¼ ww

ip$Vw

ip; i ¼ 1;.; I;p ¼ 1;.; P

Step 3. Calculate the resilience potential of each business process

p, Rw

p:

Rw

p ¼ 1I$XI

dw

ip; i ¼ 1;.; I; p ¼ 1;.; P

i¼1

The business process with the least resilience potential, p* isgiven according to expression (Bass & Kwakernaak, 1977; Dubois &Prade, 1980):

minp¼1;.;P

Rw

p ¼ Rw

p*

Step 4. Calculate the overall resilience of enterprise, Rw:

Rw

¼ 1P$XPp¼1

Rw

p

Step 5. Find the representative scalar of the fuzzy number Rw, r

by using the moment method (Dubois & Prade, 1980).Step 6. The region of resilience potential in the observed enter-prises can be defined according to the rule:

IF the value of “overall resilience” equals r, THEN the region oforganizational resilience potential is described by the linguisticexpression where max

q¼1;2;2mSw

qðx ¼ rÞ ¼ m

Sw

q*.

Table 1Fuzzy ratings of relative importance of organizational resilience factors.

p ¼ 1 p ¼ 2 p ¼ 3 p ¼ 4 p ¼ 5 p ¼ 6

i ¼ 1 Rw

4; Rw

5; Rw

4 Rw

3; Rw

2 ; Rw

2 Rw

5; Rw

5; Rw

5 Rw

3; Rw

4Rw

5 Rw

4; Rw

4; Rw

3 Rw

1; Rw

2; Rw

2i ¼ 2 R

w

4; Rw

3; Rw

3 Rw

5; Rw

5 ; Rw

4 Rw

4; Rw

2; Rw

2 Rw

3; Rw

2; Rw

2 Rw

4; Rw

2; Rw

5 Rw

4; Rw

2; Rw

1i ¼ 3 R

w

4; Rw

5; Rw

5 Rw

4; Rw

5 ; Rw

2 Rw

4; Rw

3; Rw

4 Rw

4; Rw

1; Rw

3 Rw

2; Rw

3; Rw

3 Rw

4; Rw

1; Rw

3i ¼ 4 R

w

4; Rw

1; Rw

3 Rw

4; Rw

4 ; Rw

4 Rw

3; Rw

1; Rw

3 Rw

3; Rw

3; Rw

3 Rw

3; Rw

2; Rw

3 Rw

2; Rw

2; Rw

1i ¼ 5 R

w

3; Rw

2; Rw

3 Rw

3; Rw

5 ; Rw

5 Rw

3; Rw

2; Rw

3 Rw

3; Rw

3; Rw

3 Rw

3; Rw

4; Rw

4 Rw

3; Rw

2; Rw

1i ¼ 6 R

w

4; Rw

2; Rw

2 Rw

3; Rw

5 ; Rw

4 Rw

3; Rw

5; Rw

5 Rw

3; Rw

4; Rw

4 Rw

3; Rw

1; Rw

3 Rw

3; Rw

2; Rw

2i ¼ 7 R

w

3; Rw

2; Rw

3 Rw

2; Rw

2 ; Rw

5 Rw

4; Rw

3; Rw

3 Rw

2; Rw

4; Rw

3 Rw

4; Rw

5; Rw

5 Rw

4; Rw

3; Rw

4i ¼ 8 R

w

3; Rw

1; Rw

3 Rw

4; Rw

5 ; Rw

5 Rw

3; Rw

1; Rw

3 Rw

3; Rw

3; Rw

3 Rw

4; Rw

4; Rw

3 Rw

3; Rw

3; Rw

2i ¼ 9 R

w

3; Rw

3; Rw

3 Rw

5; Rw

5 ; Rw

5 Rw

4; Rw

5; Rw

3 Rw

5; Rw

5; Rw

3 Rw

4; Rw

3; Rw

3 Rw

1; Rw

2; Rw

3i ¼ 10 R

w

4; Rw

3; Rw

3 Rw

4; Rw

3 ; Rw

4 Rw

4; Rw

5; Rw

5 Rw

2; Rw

4; Rw

5 Rw

4; Rw

3; Rw

1 Rw

4; Rw

3; Rw

2i ¼ 11 R

w

4; Rw

1; Rw

2 Rw

5; Rw

5 ; Rw

3 Rw

2; Rw

3; Rw

1 Rw

3; Rw

4; Rw

5 Rw

2; Rw

3; Rw

3 Rw

1; Rw

2; Rw

2

Table 2Fuzzy ratings of organizational resilience factor values.

p ¼ 1 p ¼ 2 p ¼ 3 p ¼ 4 p ¼ 5 p ¼ 6

i ¼ 1 vw4; v

w3; v

w3 v

w1; v

w2 ; v

w3 v

w3; v

w3; v

w3 v

w4; v

w2; v

w4 v

w4; v

w4; v

w3 v

w1; v

w3; v

w3

i ¼ 2 vw4; v

w3; v

w3 v

w4; v

w3 ; v

w3 v

w4; v

w3; v

w3 v

w1; v

w3; v

w3 v

w4; v

w2; v

w3 v

w2; v

w2; v

w3

i ¼ 3 vw5; v

w5; v

w4 v

w4; v

w5 ; v

w2 v

w4; v

w3; v

w3 v

w2; v

w4; v

w3 v

w2; v

w2; v

w3 v

w2; v

w3; v

w3

i ¼ 4 vw2; v

w3; v

w3 v

w4; v

w3 ; v

w4 v

w4; v

w4; v

w3 v

w1; v

w1; v

w3 v

w4; v

w2; v

w3 v

w1; v

w3; v

w2

i ¼ 5 vw4; v

w2; v

w3 v

w4; v

w4 ; v

w5 v

w3; v

w3; v

w3 v

w3; v

w4; v

w3 v

w4; v

w5; v

w3 v

w1; v

w2; v

w2

i ¼ 6 vw1; v

w3; v

w3 v

w3; v

w3 ; v

w2 v

w4; v

w5; v

w5 v

w2; v

w3; v

w3 v

w2; v

w2; v

w1 v

w1; v

w1; v

w2

i ¼ 7 vw2; v

w2; v

w3 v

w3; v

w3 ; v

w2 v

w4; v

w4; v

w5 v

w4; v

w3; v

w3 v

w4; v

w4; v

w4 v

w4; v

w2; v

w3

i ¼ 8 vw4; v

w3; v

w3 v

w5; v

w4 ; v

w5 v

w4; v

w3; v

w3 v

w1; v

w2; v

w2 v

w4; v

w4; v

w3 v

w2; v

w1; v

w2

i ¼ 9 vw3; v

w2; v

w2 v

w4; v

w3 ; v

w5 v

w3; v

w3; v

w3 v

w4; v

w4; v

w4 v

w2; v

w1; v

w2 v

w1; v

w1; v

w3

i ¼ 10 vw1; v

w1; v

w3 v

w4; v

w2 ; v

w2 v

w5; v

w5; v

w3 v

w4; v

w5; v

w3 v

w2; v

w1; v

w3 v

w2; v

w3; v

w2

i ¼ 11 vw3; v

w3; v

w3 v

w5; v

w5 ; v

w5 v

w2; v

w4; v

w4 v

w4; v

w4; v

w3 v

w3; v

w2; v

w3 v

w2; v

w2; v

w2

A. Aleksi�c et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 1238e1245 1243

6. Illustrative example: an SME from the process industry

The subject of this research is small enterprise that belongs tothe process industry sector in Central Serbia. SMEs represents veryimportant economy factor in all parts of Europe. Illustrative data forthis assumption are: (1) In Serbia, SMEs together accounted for99.4% of all enterprises (Republic Statistical Office of Serbia, 2010),(2) In UK, SMEs together accounted for 99.9% of all enterprises(Office for National Statistics, 2013), (3) SMEs give approximately60% of the total GDP of the EU (Lukacs, 2005). The analyzed orga-nization has an ISO 9001 certificate and it has established adequatebusiness processes. Processes in this organization are: manage-ment (p ¼ 1), production (p ¼ 2), marketing and sales (p ¼ 3),purchase (p ¼ 4), design and development (p ¼ 5), and supportprocesses (p ¼ 6) (Oakland, 2004). The considered organizationalresilience indicators are: (1) planning strategies, (2) capability andcapacity of internal resources, (3) internal situation monitoring andreporting, (4) human factors and (5) quality. The considered orga-nizational resilience indicators are discussed in section 3.

Table 3The weighted organizational resilience factor values and business process resilience valu

p ¼ 1 p ¼ 2 p ¼ 3

i ¼ 1 (0.29,0.64,0.81) (0.01,0.05,0.27) (0.24,0.5,0.7)i ¼ 2 (0.15,0.43,0.7) (0.58,0.97,1) (0.09,0.22,0.49)i ¼ 3 (0.58,0.97,1) (0.3,0.59,0.74) (0.22,0.53,0.76)i ¼ 4 (0.06,0.25,0.47) (0.36,0.8,0.92) (0.09,0.3,0.6)i ¼ 5 (0.06,0.22,0.53) (0.48,0.82,0.95) (0.04,0.19,0.49)i ¼ 6 (0.06,0.13,0.15) (0.14,0.28,0.62) (0.5,0.85,0.95)i ¼ 7 (0.03,0.14,0.39) (0.07,0.16,0.4) (0.25,0.61,0.87)i ¼ 8 (0.07,0.24,0.52) (0.58,0.97,1) (0.07,0.24,0.52)i ¼ 9 (0.03,0.18,0.46) (0.51,0.83,0.92) (0.17,0.31,0.66)i ¼ 10 (0.04,0.12,0.35) (0.15,0.42,0.64) ()0.51,0.87,0.92i ¼ 11 (0.06,0.17,0.39) (0.52,0.87,0.95) (0.04,0.13,0.43)Rw

p (0.16,0.32,0.53) (0.34,0.62,0.77) (0.202,0.43,0.67

The management team consisted of the top manager or owner,the quality manager and the financial manager, which was verysuitable for the size of the organization and its defined businessprocesses. The importance of members of a management team isdetermined based on the results of good practice in the SME pro-cess industry in Serbia. In this case, the importance of the owner is0.35, the importance of quality manager is 0.4 and the importanceof the financial manager is 0.25. The management team concludedthat the relative importance is not the same for each factor. Thefuzzy ratings of the relative importance of organizational resiliencefactors and the organizational resilience factor values are assignedas is explained in section 6 (Table 1).

After determining the fuzzy ratings of the relative importance oforganizational resilience factors, the management team assessedthe fuzzy ratings of organizational resilience factor values (Table 2).

By applying FOWA, the aggregated relative importance value oforganizational resilience indicator internal situation monitoringand reporting (i ¼ 3) for production (p ¼ 2) is:

FOWA�Rw

4; Rw

5; Rw

2

�¼ 0:35$R

w

5 þ 0:4$Rw

4 þ 0:25$Rw

2

¼ ð0:52;0:75;0:85ÞBy applying the proposed Algorithm (from Step 1 to Step 3) the

obtained results are presented in Table 3.The support processes (p ¼ 6) are denoted as the processes with

the lowest organizational resilience potential. This means that thesupport processes should be improved in the scope of the proposedresilience factors. The two factors with the lowest score in thetreated organization are Planning Strategies (i ¼ 1) and the Safetymanagement system (i ¼ 11). Improvement of planning strategiesmay include a review of suppliers and key performance indicatorsby the introduction of a new key performance indicator whichshould measure the effectiveness of suppliers. The measure for theimprovement of the safety management system may be theimplementation of standard EN/ISO 12100eSafety of machineryePrinciples for risk assessment and risk reduction. This shouldensure that all works are carried out safely by minimizing oreliminating the risks of injuries and damage to equipment.

Improvement of the mentioned factors may have positive im-plications on the other processes. By using expressions (Step 4 andStep 5) of the proposed Algorithm, the overall organizationalresilience of the treated enterprise is calculated:

Rw

¼�0w

:179;0:328;0:582�;

and the representative scalar of overall organizational resilience isr ¼ 0.328.

es.

p ¼ 4 p ¼ 5 p ¼ 6

(0.29,0.56,0.74) (0.3,0.7,0.88) (0,0.0.2)(0.02,0.07,0.31) (0.3,0.7,0.88) (0.04,0.13,0.32)(0.09,0.32,0.54) (0.03,0.14,0.39) (0.05,0.25,0.47)(0.02,0.09,0.33) (0.06,0.22,0.53) (0,0,0.17)(0.09,0.32,0.64) (0.32,0.61,0.88) (0.005,0.02,0.21)(0.12,0.37,0.62) (0.01,0.08,0.28) (0.002,0.02,0.16)(0.13,0.35,0.62) (0.52,0.9,1) (0.19,0.49,0.72)(0.01,0.11,0.34) (0.3,0.66,0.88) (0.01,0.08,0.3)(0.45,0.79,0.95) (0.03,0.15,0.37) (0.007,0.03,0.2)(0.33,0.63,0.79) (0.03,0.16,0.37) (0.05,0.2,0.44)(0.34,0.49,0.88) (0.04,0.17,0.45) (0,0,0.17)

) (0.17,0.37,0.61) (0.17,0.39,0.61) (0.03,0.11,0.31)

A. Aleksi�c et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 1238e12451244

The region of organizational resilience potential is given ac-cording to the procedure which is developed in Algorithm(Step 6).

mSw

1ðx ¼ 0:328Þ ¼ 0:36;m

Sw

2ðx ¼ 0:367Þ ¼ 1

maxq¼1;2;

mSw

q

ð0:36;1Þ ¼ 10q* ¼ 2

The obtained result indicates the mid-point level of organiza-tional resilience potential in the treated enterprise. The measuresthat are proposed for the improvement of the lowest indicatorvalues should increase the level of support process resilience andthis would improve overall organizational resilience potential andsustainability in the treated enterprise.

7. Conclusion

Industrial management practice shows that in almost everyorganization, organizational resilience represents one of themost relevant issues of sustainability. In this paper, organiza-tional resilience potential is assessed by using fuzzy sets. Theorganizational reference model and proposed resilience factorsare presented as well as constraints and the steps for assess-ment. Organizational resilience has been presented through theinternal, external and resilience enabling factors. The proposedfuzzy model for assessment of organizational resilience is pre-sented and illustrated through an example from the processindustry.

The fuzzy set theory could be the most appropriate way formodeling linguistic expressions. The fuzzy approach is easy tounderstand and flexible as well as tolerant to imprecise data.

The main contribution of this paper is the development of amathematical model for evaluation and assessment of organiza-tional resilience potential. Finally, measuring the resilience po-tential enables an organization: to learn and improve, to reportexternally and demonstrate compliance or a specific levelof organizational performance, and to control and monitorprocesses.

The presented approach is novel and presents one of the firststeps in mathematical modeling of organizational resilience. Thegeneral limitations of the model are the need for well structuredbusiness processes. The main advantages of the presented modelare suitability for usage which is accomplished by the proposedlinguistic expressions. The proposed model clearly identifies theweakest organizational elements which should be the input forcreating an enhanced business strategy.

In the illustrative example, the treated organization belongs to agroup of SMEs in the process industry. By applying the proposedalgorithm, the proposed model was tested and the obtained resultswere presented. As the processes of support have shown the lowestlevel of organizational resilience potential, measures for itsimprovement have been proposed.

Further research will be directed to the optimization of resil-ience factors’ values, so the organization should then be in the re-gion of high resilience potential with minimized costs, and whileachieving this have maximized process effectivity.

Acknowledgment

The research presented in this paper was supported by theMinistry of Science and Technological Development of the Republicof Serbia, Grant III-44010, Title: Intelligent Systems for SoftwareProduct Development and Business Support based on Models.

References

Adger, W. N. (2000). Social and ecological resilience: are they related? Progress inHuman Geography, 24(3), 347e364.

Afgan, N. H., Hovanov, N., & Andre, P. M. (2009). Sustainable management organi-zation with example of passenger car sustainability assessment. InternationalJournal for Quality Research, 3(2), 159e169.

Ates, A., & Bitici, U. (2011). Change process: a key enabler for building resilientSMEs. International Journal of Production Research, 49(18), 5601e5618.

Bass, M. S., & Kwakernaak, H. (1977). Rating and ranking of multiple-aspect alter-natives using fuzzy sets. Automatica, 3, 47e58.

Berkan, R. C., & Trubatch, S. L. (1997). Fuzzy variables design in fuzzy systems designprinciples: Building fuzzy IF-THEN rules bases. NJ., USA: Wiley-IEEE Press.

Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: the concept, a literature reviewand future directions. International Journal of Production Research, 49(18), 5375e5393.

Carvalho, P., dos Santos, I., Gomes, J., & Borges, M. (2008). Micro incident analysisframework to assess safety and resilience in the operation of safe critical sys-tems: a case study in a nuclear power plant. Journal of Loss Prevention in theProcess Industries, 21, 277e286.

Chan, J. (2011). Enhancing organisational resilience: application of viable systemmodel and MCDA in a small Hong Kong company. International Journal of Pro-duction Research, 49(18), 5545e5563.

Chen, S. J., & Chen, S. M. (2003). A new method for handling multi-criteria fuzzydecision-making problems using FNIOWA operators. Cybernetics and Systems,34, 109e137.

Crichton, M. T., Ramsay, C. G., & Kelly, T. (2009). Enhancing organizational resiliencethrough emergency planning: learnings from cross-sectoral lessons. Journal ofContingencies and Crisis Management, 17(1), 24e37.

Demmer, W. A., Vickery, S. K., & Calantone, R. (2011). Engendering resilience insmall-and medium-sized enterprises (SMEs): a case study of Demmer Corpo-ration. International Journal of Production Research, 49(18), 5395e5413.

Deros, B. M., Yusof, S. M., & Salleh, A. M. (2006). A benchmarking implementationframework for automotive manufacturing SMEs. Benchmarking: An InternationalJournal, 13(4), 396e430.

Dinh, L., Pasman, H., Gao, X., & Mannan, M. S. (2012). Resilience engineering ofindustrial processes: principles and contributing factors. Journal of Loss Pre-vention in the Process Industries, 25, 233e241.

Dubois, D., & Prade, H. (1980). Fuzzy sets and systems: Theory and applications. Ac-ademic press, Inc. (London) Ltd.

Folke, C. (2006). Resilience: the emergence of a perspective for, socialeecologicalsystems analyses. Global Environmental Change, 16, 253e267.

Gibson, C. A., & Tarrant, M. (2010). A conceptual models’ approach to organisationresilience. Australian Journal of Emergency Management, 25(2), 6e12.

Gunasekaran, A., Bharatendra, K. R., & Griffin, M. (2011). Resilience and competi-tiveness of small and medium size enterprises: an empirical research. Inter-national Journal of Production Research, 49(18), 5489e5509.

Hamel, G., & Valikangas, L. (2003). The quest for resilience. Harvard Business Review,81(9), 52e65.

Hollnagel, E., Woods, D. D., & Leveson, N. (2006). Resilience engineering: concepts andprecepts. Aldershot: Ashgate.

Klir, G. J., & Folger, T. A. (1988). Fuzzy sets, uncertainty and informations. NJ: Prentice-Hall, Englewood Cliffs.

Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic, theory and applications. NewJersey: Prentice Hall.

Kwang, H. L. (2005). First course on fuzzy theory and applications (pp. 137e144).Berlin-Heidelberg/New York: Springer-Vergal.

Lee, Y., Shin, J., & Park, Y. (2012). The changing pattern of SME’s innovativenessthrough business model globalization. Technological Forecasting & Social Change,79, 832e842.

Lengnick-Hall, C. A., Beck, T. E., & Lengnick-Hall, M. L. (2011). Developing a capacityfor organizational resilience through strategic human resource management.Human Resource Management Review, 21, 243e255.

Lootsma, F. A. (1997). Fuzzy logic for planning and decision-making. Boston, MA, USA:Kluwer Academic.

Lukacs, E. (2005). The economic role of SMEs in world economy, especially inEurope. European Integration Studies, 4(1), 3e12.

Markowski, S. A., Mannan, S. M., & Bigoszewska, A. (2009). Fuzzy logic for processsafety analysis. Journal of Loss Prevention in the Process Industries, 22, 695e702.

Merigó, J. M., & Casonovas, M. (2008). Using fuzzy numbers in heavy aggregationoperators. International Journal of Information Technology, 4, 177e182.

Möller, K., & Rajala, A. (2007). Rise of strategic nets e new modes of value creation.Industrial Marketing Management, 36, 895e908.

Oakland, J. (2004). Oakland on quality management. Elsevier Butterworth eHeinemann.

Office for National Statistics. (2013) [Online] URL http://www.ons.gov.uk/ons/index.html Accessed 26.05.13.

Pendall, R., Foster, K., & Cowell, M. (2010). Resilience and regions: building under-standing of the metaphor. Cambridge Journal of Regions, Economy and Society, 3,71e84.

Petrovi�c, D. (1997). Modelling and analysis of serial supply chains in uncertain envi-ronments. PhD Disertation. University of Warwick, Department of Engineering.

Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience:development of a conceptual framework. Journal of Business Logistics, 31(1),1e21.

A. Aleksi�c et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 1238e1245 1245

Somers, S. (2009). Measuring resilience potential: an adaptive strategy for organi-zational crisis planning. Journal of Contingencies and Crisis Management, 17(1),12e23.

Spekman, R., & Davis, E. (2004). Risky business: expanding the discussion on riskand the extended enterprise. International Journal of Physical Distribution andLogistics Management, 34(5), 414e433.

Starr, R., Newfrock, J., & Delurey, M. (2003). Enterprise resilience: managing riskin the networked economy. International Journal of Production Research, 30,1e10.

Statistical Office of the Republic of Serbia. (2010) [Online] URL http://webrzs.stat.gov.rs/WebSite/Default.aspx Accessed 26.05.13.

Stephenson, A., Vargo, J., & Seville, E. (2010). Measuring and comparing organisa-tional resilience in Auckland. The Australian Journal of Emergency Management,25(2), 27e32.

Vidal, M., Carvalho, P., Santos, M., & dos Santos, I. (2009). Collective work andresilience of complex systems. Journal of Loss Prevention in the Process Industries,22, 516e527.

Vogus, T. J., & Sutcliffe, K. M. (2007). Organizational resilience: towards a theory andresearch agenda. In IEEE international conference on systems, man and cyber-netics, Montreal (pp. 3418e3422).

Vossen, R. W. (1998). Relative strengths and weaknesses of small firms in innova-tion. International Small Business Journal, 16, 88e94.

Wymenga, P., Spanikova, V., Barker, A., Konings, J., & Canton, A. (2012). EU SMEs in2012: at the crossroads annual report on small and medium-sized enterprises inthe EU, 2011/12. Rotterdam: Ecorys.

Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Transactions on Systems, Man, and Cybernetic, B,18, 183e190.

Zimmermann, H. J. (1997). Intelligent decision supporty system. In Europeansymposium on intelligent techniques. No. 23924, Aachen, Germany.

Zimmermann,H. J. (1978).Resultsofempiricalstudies in fuzzysettheory. InG. J.Klir (Ed.),Applied general systems research (pp. 303e311). Plenum Publishing Corporation.

Zimmermann, H. J. (2001). Fuzzy set theory and its applications. Boston: KluwerNijhoff Publishing.