Research ArticleA Sustainable Manufacturing Strategy from Different StrategicResponses under Uncertainty
Lanndon Ocampo,1 Eppie Clark,2 and Kae Vines Tanudtanud3
1Department of Mechanical Engineering, University of San Carlos, 6000 Cebu City, Philippines2Department of Industrial Engineering, De La Salle University, 2401 Taft Avenue, 1004 Manila, Philippines3International Society for Business Innovation & Technology Management, 2288 Radium Street, 1200 Manila, Philippines
Correspondence should be addressed to Lanndon Ocampo; don [email protected]
Received 12 September 2014; Revised 15 January 2015; Accepted 16 January 2015
This paper presents a decision framework that highlights the integration of manufacturing strategy (MS) and sustainabilityalong with strategic responses as a significant component. This integration raises complexity and uncertainty in decision-makingfollowing the number of subjective components with their inherent relationships that must be brought into context and the hugeamount of required information in eliciting judgments.Thus, a proposed hybridmulticriteria decision-making (MCDM) approachin the form of an integrated probabilistic fuzzy analytic network process (PROFUZANP) is adopted in this work. In this method,analytic network process (ANP) serves as the main framework in identifying policy options of manufacturing strategy. Fuzzy settheory (FST) is used to describe vagueness in decision-making which is carried out by eliciting judgments in pairwise comparisonsusing linguistic variables with corresponding triangular fuzzy numbers (TFNs). Probability theory is used to handle randomnessin aggregating judgments of multiple decision-makers. Results show that a stakeholder-oriented approach is considered the mostrelevant strategic response in developing a sustainable manufacturing strategy. The contribution of this work lies in identifying thepolicies which constitute a sustainable manufacturing strategy using an integrated MCDM approach under uncertainty.
1. Introduction
The work of Wickham Skinner in 1969 became the focalpoint of discussion regarding the role of manufacturingstrategy in attaining corporate goals and objectives. Skinner[1] developed the hierarchical top-down strategy frameworkthat links corporate strategy, business strategies, and func-tional strategies which include manufacturing strategy [2].This framework eventually became the guidelines of laterapproaches in this research domain [3–5]. Scholars agree thatmanufacturing strategy could only support business strategyif a sequence of decisions over structural and infrastructuralcategories is consistent over a considerable amount of time[6]. Structural decision areas include process technology,facilities, capacity, and vertical integration while infrastruc-tural decision areas contain organization, manufacturingplanning and control, quality, new product introduction,and human resources. Each of these decision areas involves
a finite number of policy options available to the decision-maker. Certainly, identifying the best policy for each decisionarea requires careful attention and systems thinking due tothe number of decision components that must be taken intoconsideration which make the decision-making a complexone. Manufacturing strategy has evolved as a diverse fieldcovering theoretical and empirical works across various disci-plines; however, the field is criticized over its lack of progressparticularly on its integration with current approaches [5]with emphasis on sustainability.
Emerging concerns on sustainability compelmanufactur-ing firms to incorporate in their decision-making processesthe interests of the triple-bottom line, that is, economic,environmental, and social issues [7], especially those relatedto material, energy, and wastes [8, 9] as the primary concernsofmanufacturing.Manufacturing industry holds one-third ofworld energy consumption and CO
2emissions are increasing
at a significant rate [10, 11]. Thus, an approach known as
Hindawi Publishing CorporationJournal of Industrial EngineeringVolume 2015, Article ID 210568, 11 pageshttp://dx.doi.org/10.1155/2015/210568
2 Journal of Industrial Engineering
sustainable manufacturing was eventually coined by the U.S.Department of Commercewhich promotes the idea of havingproducts and processes which are not only profitable, butalso safe to the environment and to the society [12]. Thisgains significant attention from both industry and academiafollowing research agenda in developed economies workingtoward this direction [13].
Thiswork attempts to provide a framework that integratesmanufacturing strategy and sustainable manufacturing withemphasis on considering strategic responses of firms towardsustainability.The proposed framework identifies the policiesthat must be placed in various manufacturing decision areaswith the goal of promoting competitiveness and sustain-ability which were understated in previous literature in thisfield. The main departure of this work is the integrativedecision-making framework that attempts to holisticallycapture various decision components with their intrinsicinterrelationships. The emphasis on strategic responses washighlymotivated by several works, for example, Sweeney [14],Miller and Roth [15], and Frohlich and Dixon [16] togetherwith thework of deRon [17] andHeikkurinen andBonnedahl[18]. Sweeney [14] made the first attempt to comprehensivelygroup manufacturing strategies into “generic manufacturingstrategies” that include caretaker, marketeer, reorganizer, andinnovator strategies. The main idea was that manufacturingorganizations, as the result of the complex interactions oforganizational culture and values with the options taken bybusiness and manufacturing policies, tend to brand them-selves into a specific stance on key decisions in developingmanufacturing strategy. This claim was further elaboratedby Miller and Roth [15] and was eventually supported byFrohlich and Dixon [16] using different empirical samples.However, with slight modifications, Frohlich and Dixon [16]identified three types ofmanufacturing strategies that includecaretakers, marketeers, and innovators.
Aside from classifying manufacturing strategy types,Sweeney [14] highlighted the notion of transition paths orroutes for firms to achieve the most positive form of strategy.These transition paths served as guide for firms on manufac-turing policies and competitive advantages they must placeto support a particular route. This research domain becameprominent following several published works which showconsistency of the types of strategic responses.With the onsetof growing interests in sustainability, former taxonomieswereparalleled by the responses or stances of firms toward sustain-ability issues as described by the works of de Ron [17] andHeikkurinen and Bonnedahl [18]. Their works highlightedthe three strategic responses that firms engage in embracingsustainability issues: stakeholder-oriented, market-oriented,and sustainability-oriented.
This paper attempts to extend previous works on strate-gic responses by integrating manufacturing taxonomies ofSweeney [14], Miller and Roth [15], and Frohlich andDixon [16] with the sustainability responses described byde Ron [17] and Heikkurinen and Bonnedahl [18]. Thiswork highlights the framework which introduces two tran-sition routes: first is the stakeholder-oriented→market-oriented→ sustainability-oriented route and second is thestakeholder-oriented→ sustainability-oriented route. These
responses and transition routes are brought into the contextof developing a sustainable manufacturing strategy. Thegoal is to identify the content strategy that addresses bothcompetitiveness and sustainability as the result of thesetransition routes. Due to the complexity in decision-making,the use of multicriteria decision-making methods (MCDM)particularly analytic hierarchy process/analytic network pro-cess (AHP/ANP) becomes appropriate and fundamental.AHP/ANP is a theory of relative measurement that allowsdecision-makers to structure their decision problems intoa hierarchy or a network with subjective components [19,20]. A number of AHP/ANP applications include computingproduct sustainability index [21], computing sustainabilityindex with time as an element [22], developing sustainabil-ity index for a manufacturing enterprise [23], developingmultiactor multicriteria approach in complex sustainabilityproject evaluation [24], evaluating industrial competitiveness[25], evaluating energy sources [26], and developing anAHP-based impact matrix and sustainability-cost benefit analysis[27]. A number of reviews of AHP/ANP application in oper-ations management [28], its methodological development[29], dominant applications [30], and its integration withother operations research tools [31] were conducted.
Following various arguments on the uncertainty ofdecision-making in the context of AHP/ANP ([32]; Tsengand Chiu [33]), this work adopted the proposed methodof Ocampo and Clark [34] coined as PROFUZANP whichis based on fuzzy set theory, probability theory, and ANP.In this approach, ANP is used to handle decision-makingcomplexity, FST is used to address vagueness individualdecision-maker’s judgment, and probability theory is usedto handle randomness of aggregating judgments. The con-tribution of this work lies in identifying policy options ofmanufacturing strategy resulting from the strategic responsescarried out by manufacturing organizations. This paper isorganized as follows: Section 2 provides an introduction ofANP, fuzzy set theory, and the PROFUZANP approach,Section 3 describes the problem structure, Section 4 presentsthe results of this study, and finally Section 5 highlights adiscussion and conclusion of this work.
2. Methodology
2.1. Analytic Network Process (ANP). ANP is the generalframework of analyzing complex decisions with qualitativeand quantitative components and elements [19, 20]. ANPstructures the decision problem as a network of decisioncomponents and elements with dependence relationships.Saaty [35] explained that pairwise comparison is central to themeasurement of subjective or of intangible elements. Pairwisecomparisons are done by comparing elements with respect toa parent element from the same or another component.Thesecomparisons form a positive reciprocal square matrix. Saaty[19] proposed an eigenvalue problem which determines thelocal eigenvector (𝑤) of the matrix:
𝐴𝑤 = 𝜆max𝑤, (1)where𝐴 is the positive reciprocal of the pairwise comparisonmatrix, 𝜆max is the largest eigenvalue of matrix 𝐴, and 𝑤 is
Journal of Industrial Engineering 3
the principal eigenvector associated with 𝜆max. For consistentjudgment, 𝜆max = 𝑛; otherwise, 𝜆max > 𝑛. Consistencyof judgment is measured using the consistency index (CI)and consistency ratio (CR). The consistency index (CI) is ameasure of the degree of consistency and is represented by
CI =𝜆max − 𝑛
𝑛 − 1
. (2)
The consistency ratio (CR) is computed as
CR = CIRI, (3)
where RI is themean random consistency index. CR ≤ 0.10 isan acceptable degree of inconsistency [19]. Decision-makerswould be asked to reconsider paired comparisons in case ofCR > 0.10.
Local eigenvectors are plugged into the supermatrix.The numerical approach of computing the global priorityvector is done by normalizing columns and then raisingthe supermatrix to large powers. This approach enables thesupermatrix convergence to a limit value. Each column ofthe limit supermatrix is a unique positive column eigenvectorassociated with the principal eigenvalue 𝜆max [36]. Thisprincipal column eigenvector resembles the stable prioritiesof the limit supermatrix and can be used to measure theoverall relative dominance of one element over anotherelement in a network structure [37].
2.2. Fuzzy Set Theory (FST). Zadeh [38] introduced thefuzzy set theory (FST) as a mathematical way in handlingimprecision and vagueness in decision-making. In particular,fuzzy numbers provide a way of expressing vagueness inFST. A fuzzy number can be represented by a fuzzy set 𝐹 ={(𝑥, 𝑢𝐹(𝑥)), 𝑥 ∈ 𝑅}, where 𝑥 takes on any value on the real
number line 𝑅 : −∞ < 𝑥 < +∞ and 𝑢𝐹(𝑥) is a continuous
mapping on the closed interval (0, 1). Various forms of a fuzzynumber are available but the widely used one is the triangularfuzzy number (TFN) [36, 39]. TFN can be defined as a triplet𝐴 = (𝑙, 𝑚, 𝑢) with a membership function. An introductorydiscussion of fuzzy numbers and their arithmetic operationscan be found in Kaufmann and Gupta [40].
FST is shown to enhance MCDM methods in handlingcomplex and imprecise judgments. Sincemost evaluators findit hard to elicit numerical judgments, more realistic eval-uations use linguistic variables to represent judgment [41].Linguistic variables have the form of phrases or sentencesin a natural language [42]. Tseng [39] argues that linguisticvariables can be appropriately associated with correspondingTFNs.
2.3. PROFUZANP Approach. PROFUZANP approach wasdetailed in the work of Ocampo and Clark [34]; importantpoints are discussed in this section. This approach sharessimilaritywith theworks of Tseng [32]which transformTFNsinto crisp values before raising the pairwise comparisonsmatrices to large powers. Since any fuzzy aggregationmethodrequires defuzzification [39], the defuzzification process usedby Tseng et al. [43] is derived from the algorithm proposed
by Opricovic and Tzeng [44]. The linguistic variables arepresented in Table 1 with equivalent TFNs adopted fromTseng et al. [45].
The notations used in this paper were lifted from thenotations used by works of Tseng [32]. Suppose a set of 𝑘decision-makers with 𝑤𝑘
𝑖𝑗= (𝑎𝑘
1𝑖𝑗, 𝑎𝑘
2𝑖𝑗, 𝑎𝑘
3𝑖𝑗) as the influence of
𝑖th criteria on 𝑗th criteria assessed by the 𝑘th evaluators.Normalization:
𝑥𝑎
𝑘
1𝑖𝑗=
𝑎𝑘
1𝑖𝑗−min 𝑎𝑘
1𝑖𝑗
Δmaxmin, (4)
𝑥𝑎
𝑘
2𝑖𝑗=
𝑎𝑘
2𝑖𝑗−min 𝑎𝑘
1𝑖𝑗
Δmaxmin, (5)
𝑥𝑎
𝑘
3𝑖𝑗=
𝑎𝑘
3𝑖𝑗−min 𝑎𝑘
1𝑖𝑗
Δmaxmin, (6)
where
Δ
maxmin = max 𝑎𝑘
3𝑖𝑗−min 𝑎𝑘
1𝑖𝑗. (7)
Compute left 𝑙𝑠 and right 𝑟𝑠 normalized values:
𝑥𝑙𝑠
𝑘
𝑖𝑗=
𝑥𝑎𝑘
2𝑖𝑗
1 + 𝑥𝑎𝑘
2𝑖𝑗− 𝑥𝑎𝑘
1𝑖𝑗
,
𝑥𝑟𝑠
𝑘
𝑖𝑗=
𝑥𝑎𝑘
3𝑖𝑗
1 + 𝑥𝑎𝑘
3𝑖𝑗− 𝑥𝑎𝑘
2𝑖𝑗
.
(8)
Compute total normalized crisp value:
𝑥
𝑘
𝑖𝑗=
𝑥𝑙𝑠𝑘
𝑖𝑗(1 − 𝑥𝑙𝑠
𝑘
𝑖𝑗) + 𝑥𝑟𝑠
𝑘
𝑖𝑗𝑥𝑟𝑠𝑘
𝑖𝑗
1 − 𝑥𝑙𝑠𝑘
𝑖𝑗+ 𝑥𝑟𝑠𝑘
𝑖𝑗
. (9)
Compute crisp values:
𝑤
𝑘
𝑖𝑗= min 𝑎𝑘
1𝑖𝑗+ 𝑥
𝑘
𝑖𝑗Δ
maxmin. (10)
In integrating judgments of decision-makers, however,Ocampo and Clark [34] introduced a probabilistic approachdefined as
𝑤𝑖𝑗= 𝑤𝑖𝑗± (1 − 𝛼) 𝑝, (11)
where𝑤𝑖𝑗is an integrated judgment of decision-makers from
a normal distribution that represents the influence of rowelement on column element, 𝑤
𝑖𝑗is the geometric mean of all
judgment of decision-makers of 𝑖 on 𝑗 and is defined as
𝑤𝑖𝑗=𝑘√𝑤1
𝑖𝑗𝑤2
𝑖𝑗𝑤3
𝑖𝑗. . . 𝑤𝑘
𝑖𝑗. (12)
(1−𝛼) is the confidence level of the distribution, and𝑝 ∈ [0, 1]is proportion of perturbation about the geometric mean.Thevalue of 𝑝 denotes a range of judgmental uncertainties thatusually range from 2% to 20% [46].
4 Journal of Industrial Engineering
Table 1: Linguistic variables adopted from Tseng et al. [45].
2.4. Procedure. Theprocedure implemented in this paper canbe described as follows.
(1) The decision model was established from a rigorousliterature review that integrates classical manufactur-ing strategy and sustainability into a single model.Figure 1 shows the problem structure developed inthis work.
(2) Respondents were selected to provide expert judg-ments of the problem structure.
(3) Following the AHP/ANP context, pairwise compar-isonswere performed based on the problem structure.Comparisons were elicited using the linguistic vari-ables in Table 1. Using (4) through (10), correspond-ing crisp values of the TFNs were computed.
(4) Local priority vectors and CI and CR values ofpairwise comparisons matrices were computed using(1) through (3).
(5) Using (11) through (12) by assigning 𝛼 = 0.05 andwith assigned 𝑝 = 0.25 which shows the upperlimit perturbation of judgments [47], judgments ofindividual decision-makers were aggregated. Localaggregated priority vectors of these matrices wereobtained using (1).
(6) An initial supermatrix from the decision modelwas constructed and then was populated with localeigenvectors obtained in step (4) for each value of 𝑝.Normalizing columns and raising the supermatrix tolarge powers solve the global priority vector.
3. Problem Structure
In reference to the discussion in Section 1, the problem struc-ture is described into two parts. The first part presents thehierarchical structure of corporate strategy, business strategy,and functional strategies. Each component is composed of anumber of elements defined in the literature.The second parthighlights the influence of strategic responses in developinga manufacturing strategy. This part is supported by thehierarchical structure of decision categories, policy areas, andpolicy options as described by Wheelwright [48]. A numberof policy options comprise a particular policy area and eachpolicy area is composed of a number of finite policy options.This part elucidates the departure of this work from thecurrent literature. Figure 1 presents the problem structuredeveloped in this work.
Strategic responses
Manufacturing decision categories
Policyareas
Policyoptions
Goal
Manufacturingstrategy
Business strategy
Corporate strategy
Figure 1: Problem structure.
The problem structure in Figure 1 is composed of eightcomponents which are the goal, corporate strategy, businessstrategy, manufacturing strategy, strategic responses, manu-facturing strategy decision categories, policy areas, and policyoptions. The model is largely motivated by the hierarchicalframework of Skinner [1] with the inclusion of strategicresponses as a mediating component that prescribes thecontent of the manufacturing strategy. Each componentcomprises a set of decision elements. The goal componentcontains a single element which is to develop a sustainablemanufacturing strategy. Corporate strategy component hastwo elements: sustainable businesses and commitment tosustainability. Business strategy has likewise two elements:market-oriented and technology-oriented. Manufacturingstrategy is a single-element component which is a sustainablemanufacturing strategy. Strategic responses component hasthree elements: stakeholder-oriented, market-oriented, andsustainability-oriented. Manufacturing decision categoriescomponent has nine elements and each element has its ownset of policy areas as proposed by Wheelwright [48] andHallgren and Olhager [4]. Likewise, each policy containspolicy options available to the manufacturing firm. Theobjective of this problem structure is to provide the contentof a sustainable manufacturing strategy as the result of the
Journal of Industrial Engineering 5
Table 2: Coding system of the elements of the problem structure.
Decisioncomponents Decision elements Code
Goal Develop sustainable manufacturingstrategy A
Corporatestrategy
Sustainable businesses D1Commitment to sustainability D2
Businessstrategy
Market-oriented E1Technology-oriented E2
Manufacturingstrategy Sustainable manufacturing strategy F
Flexible manufacturing system C122Computer-aided manufacturing C123Cellular C131Process C132Product C133One big plant C211Several smaller ones C212Close to market C221Close to supplier C222Close to technology C223Close to competitor C224Close to source of raw materials C225Product groups C231Process types C232Life cycle stages C233Fixed units per period C311Based on inputs C312Based on outputs C313Leading C321Chasing C322Following C323Potential C331Immediate C332Effective C333Forward C411Backward C412
Policy options Horizontal C413Sources of raw materials C421Distribution to final customers C422Low degree C431Medium degree C432High degree C433Functional C511Product groups C512Geographical C513Top C521Middle C522First line C523Large groups C531Small groups C532Make-to-order C611Make-to-stock C612Close support C621Loose support C622High degree C631Low degree C632High quality C711Low degree C712High frequency C721Low frequency C722High frequency C731Low frequency C732
6 Journal of Industrial Engineering
Table 2: Continued.
Decisioncomponents Decision elements Code
Slow C811Fast C812Standard C821Customized C822New processes C831Follow-the-leader policy C832Specialized C911Not specialized C912Based on hours worked C921Quantity/quality of output C922Seniority C923Training C931Recognition for achievement C932Promotion C933
overarching problem structure in Figure 1. Using the pro-posed PROFUZANP approach developed by Ocampo andClark [34], the proposed model will be able to determine thepolicy options for each policy area. These options constitutethe sustainable manufacturing strategy of the manufacturingfirm. In order to facilitate easy recall, a comprehensive codingsystem is shown in Table 2 to represent each element in thedecision model.
In the coding system shown in Table 2, the goal com-ponent is assigned as A and corporate strategy as D. Busi-ness strategy is denoted as E, manufacturing strategy isrepresented as F and strategic responses as G, and decisioncategory, policy areas, and policy options components aredesignated with appropriate alpha-numeric codes of C#, C##,and C###, respectively, with # representing an integer. Thecoding system in Table 2 is so structured to facilitate remem-bering of elements associated with their parent element. Forinstance, C111 represents job shop process and is listed downas the first policy option under C11 policy area which isprocess choice. C11, on the other hand, also defines the firstpolicy option under C1 manufacturing decision category,process technology.
Respondents were carefully selected with the goal of pro-viding well-informed and expert judgments of the problemstructure. Preselection of these respondents was based ontheir expertise in the manufacturing industry. All domainexperts are located in the Philippines who worked formultinational manufacturing firms and were exposed tointernational practices. In this work, ten experts were selectedto provide valid results. Background checks were done foreach respondent based on available public data. A thresholdcriterion is set at least 10-year managerial experience in man-ufacturing industries to ensure that they have the capabilityand previous knowledge in carrying out vital manufacturingdecisions. This choice of these respondents is consistent withthe MCDM studies published by Lin et al. [49], Tseng [32],and Tseng and Chiu [33].
The following steps were undertaken for data gatheringrequired for this work. Questionnaires containing pairwise
comparisons matrices were distributed to ten respondents.Theywere arranged for a supervised survey according to theirpreferred schedule. However, in a case of a respondent withno available time for a supervised survey or being out of thecountry, an email containing the survey questionnaire wassent. Respondents were given a maximum of two to fourweeks to answer the questionnaire. Within this time period,regular follow-ups were made to ensure timeliness of theiroutput. Upon receipt of their answers, judgment consistencyfor each pairwise comparisons matrix was checked. Wheninconsistency was observed, specific matrices were emailedback to the respondent with a note why the matrix isinconsistent. This means that the respondent was informedthat his or her judgment on elements a to b and b to c isinconsistent with his or her judgment on a to c. Respondentswere given another one or two weeks to reconsider theirjudgment on specific inconsistent matrices. At this period,regular follow-ups were undertaken.
4. Results
For brevity, the computations carried out in this work are notpresented in this paper. All computations were performedusing Microsoft Excel spreadsheet and a VBA add-on. Asample pairwise comparisons matrix in linguistic variablesfrom a single decision-maker is shown in Table 3.
Note that only the upper triangle of the matrix is filledout as the lower triangle represents the straightforward recip-rocal of the upper triangular matrix. This matrix comparesthe influence of strategic responses in addressing the goalof developing a sustainable manufacturing strategy. FromTable 3, corresponding TFNs and their respective reciprocalsare shown in Table 4.
From the TFNs shown in Table 4, the defuzzificationprocess proposed by Opricovic and Tzeng [44] is used toobtain appropriate crisp values of the TFNs. Crisp valueswerecomputed using (4) through (10). Table 5 shows the corre-sponding crisp values of the sample pairwise comparisonsmatrix under consideration.
Applying the same process to the rest of the decision-makers’ judgments and pairwise comparisons matrices, pair-wise comparisons matrices in crisp values are obtained.Aggregation of all judgments across decision-makers is thenprocessed at the pairwise comparisons matrix level. The goalof the aggregation process is to come up with a single matrixthat fairly represents the judgments of all decision-makers fora specific pairwise comparisons matrix. Using the proposedPROFUZANP approach of Ocampo and Clark [34] whichis also presented in Section 2.3, aggregating judgment ofdecisionmakers involves the application of (11) and (12).With𝛼 set at 0.05, which is widely used in statistical analysis, andthe value of𝑝 set at 0.25 following the argument ofHauser andTadikamalla [47], a sample aggregated matrix of the same setof comparisons is shown in Table 6.
From the aggregated matrix, local priority vectors, theprincipal eigenvalue, andCR valuewere then computed using(1), (2), and (3), respectively. Table 7 shows a sample of a localeigenvector of an aggregated pairwise comparisons matrix.
Journal of Industrial Engineering 7
Table 3: A sample pairwise comparisons matrix in linguistic variables.
Table 7: A sample local eigenvector of an aggregated pairwisecomparisons matrix.
Local eigenvectorStakeholder-oriented 0.391017Market-oriented 0.331778Sustainability-oriented 0.277206𝜆max = 3.094; CR = 0.090.
Each column of the limiting pairwise comparisonsmatrixin Table 8 is the principal eigenvector of the matrix asdiscussed by Saaty [19]. CR values of all pairwise comparisonsmatrix are below the 0.10 threshold value. The local priorityvectors of all aggregated pairwise comparisons matrices arepopulated in the supermatrix.The general supermatrix of thedecision model presented in Figure 1 is shown in Table 8.
The numerical supermatrix runs in the order of 116×116;thus it is difficult to present the results here as it consumeslarge amount of space. For brevity, the generalized superma-trix and the resulting global priority vector are only shown toelucidate the process of the ANP. In order to determine thepriority ranking of each policy choice in relation to the goal,Table 9 ranks policy choice according to decreasing globalpriority values.This ranking provides insights for firms on thesequence of implementation of policy choices. However, thismust be taken into context together with business conditions,individual corporate goals, and the propensity of investment
Table 8: Generalized supermatrix.
A D E F G C# C## C###A I 1 1 1 1 1 1 1D DA I 0 0 0 0 0 0E 0 ED I 0 0 0 0 0F 0 0 FE I 0 0 0 0G GA 0 0 GF GG 0 0 0C# 0 0 0 0 C#G C#C# 0 0C## 0 0 0 0 0 C##C# I 1C### 0 0 0 0 0 0 C###C## I
decisions of firms. Table 9 provides the ranking of the prioritypolicy choices.
Table 10 provides the policy choice for each manufac-turing decision area. These policies constitute a sustainablemanufacturing strategy. Special attention is also regarded inthe prioritization of strategic responses. This sets the type ofmanufacturing strategy the business would engage. Priorityrankings of corporate strategy, business strategy, and strategicresponses are presented in Table 11.
Priority rankings of corporate strategy, business strat-egy, and strategic responses are presented in Table 11. Therankings show that sustainable businesses have the highestpriority in corporate strategy and a market-oriented businessstrategy is highly prioritized. Inconsistent with previous
8 Journal of Industrial Engineering
Table 9: Global priority vector and ranking.
Code Decision elements Globaleigenvector Rank
A Develop sustainablemanufacturing strategy 0.323232 1
D1 Sustainable businesses 0.113131 1D2 Commitment to sustainability 0.048485 2
C931 Training 0.000237 1C932 Recognition for achievement 0.000134 2
C933 Promotion 9.21𝐸 − 05 3
works, stakeholder-oriented approach posesmost inclinationtoward developing a sustainable manufacturing strategy.
5. Discussion and Conclusion
Sustainable businesses emerge as a more relevant corporatestrategy in addressing sustainability and competitiveness.Thedifference between sustainable businesses and commitmentto sustainability is that the former is a proactive approachwhere sustainability is embedded in corporate values andculture while the latter takes on a more reactive stance wheredecision-making depends on how a specific issue will appear.In the business strategy component, technology-orientation(E2) is considered a desirable approach compared to market-oriented approach (E1). The reason for this might be that
Table 10: Content of a sustainable manufacturing strategy consid-ering strategic responses.
Code Policy area Code Highest priority policychoice
C11 Process choice C112 Batch
C12 Technology C122 Flexible manufacturingsystem
C13 Process integration C132 ProcessC21 Facility size C211 One big plantC22 Facility location C221 Close to marketC23 Facility focus C231 Product groupsC31 Capacity amount C312 Based on inputsC32 Capacity timing C321 LeadingC33 Capacity type C333 EffectiveC41 Direction C411 ForwardC42 Extent C421 Sources of raw materialsC43 Balance C432 Medium degreeC51 Structure C511 FunctionalC52 Reporting levels C522 MiddleC53 Support groups C532 Small groupsC61 System design C611 Make-to-orderC62 Decision support C621 Close supportC63 Systems integration C631 High degreeC71 Defect prevention C711 High qualityC72 Monitoring C721 High frequencyC73 Intervention C731 High frequencyC81 Rate of innovation C812 FastC82 Product design C821 StandardC83 Industrialization C831 New processesC91 Skill level C911 Specialized
C92 Pay C922 Quantity/quality ofoutput
C93 Security C931 Training
Table 11: Ranking of other decision components.
Rank StrategyCorporate strategy
1 Sustainable businesses2 Commitment to sustainability
Business strategy1 Technology-oriented2 Market-oriented
experts consider technology-orientation as an opportunityto lead an industry into developing environmentally benigntechnologies with minimum environmental footprint andwith less impact on society’s health andwell-being. Amarket-oriented approach on the other hand is limited to the interestsof market which is a narrow approach toward sustainability.As opposed to the initial contention that the highest form
10 Journal of Industrial Engineering
of manufacturing strategy orientation is a sustainability-orientation [17, 18], results of this work suggest that astakeholder-orientation in manufacturing serves as a morepractical approach in developing a sustainablemanufacturingstrategy. This result could be viewed from two different per-spectives. First, experts likely believe that a better approachin addressing sustainability is hearing the “voice of the stake-holder.” Although it is a reactive stance, this approach ensuresthat the manufacturing firm addresses each issue of thestakeholders transparently without missing important pointsalong the process. It is particularly similar with the “qualityfunctional deployment” approach in the past; however, amore holistic “voice of the stakeholder” is presented. Whilea proactive stance in sustainability-orientation is obviously adesirable stance, experts might believe that, in the long run,the manufacturing firms may approach sustainability towarda “strong sustainability” framework where development isvery limited. Second, it could be that experts have impre-cise knowledge of the contested concepts of sustainability.Since sustainability is still a growing field in the literaturewith more contestations than a single meaning, expertsprefer to give higher priorities to stakeholder-orientationwhen competitiveness and sustainability are placed intocontext.
A total of 27 decisions constitute the sustainable manu-facturing strategy. Table 10 provides specific policy optionscorresponding to policy areas. Using the PROFUZANPapproach of Ocampo and Clark [34], the decisionmodel pro-vides the content of the sustainable manufacturing strategy.It shows that the content strategy is inclined toward process-centered technology with continuous processes, big, productlife cycle stages-focused facilities which are close to suppliersand customers, leading capacity strategy, a backward ver-tical integration toward sources of raw materials, first-linereporting with functional structure organization, a minimalinventory-focusedmanufacturing planning and control, highquality prevention, monitoring and intervention policies,fast product introduction with new processes, and highlyskilledworkers.The content of the sustainablemanufacturingstrategy is expected to address both competitiveness andsustainability in manufacturing.
Disclosure
An earlier version of this work was presented at the 7thIEEE International Conference on Humanoid, Nanotechnol-ogy, Information Technology, Communication and Control,Environment and Management (HNICEM).
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper.
Acknowledgment
L. Ocampo is grateful to the Ph.D. financial support ofthe Engineering Research and Development for Technology
(ERDT) Program of the Department of Science and Technol-ogy, Philippines.
References
[1] W. Skinner, “Manufacturing-missing link in corporate strategy,”Harvard Business Review, vol. 47, pp. 136–145, 1969.
[2] R. H. Hayes and S. C. Wheelwright, Restoring Our CompetitiveEdge: Competing through Manufacturing, John Wiley & Sons,New York, NY, USA, 1984.
[3] C. A. Voss, “Alternative paradigms for manufacturing strategy,”International Journal of Operations and Production Manage-ment, vol. 15, no. 4, pp. 5–16, 1995.
[4] M. Hallgren and J. Olhager, “Quantification in manufacturingstrategy: a methodology and illustration,” International Journalof Production Economics, vol. 104, no. 1, pp. 113–124, 2006.
[5] M. E. Gonzalez, G. Quesada, and C. Mora-Monge, “An interna-tional study on manufacturing competitive priorities,” Journalof Management Policy and Practice, vol. 13, no. 3, pp. 116–128,2012.
[6] S. C. Wheelwright, “Reflecting corporate strategy in manufac-turing decisions,” Business Horizons, vol. 21, no. 1, pp. 57–66,1978.
[7] J. Elkington,Cannibals with Forks:The Triple Bottom Line of 21stCentury Business, Capstone, Oxford, UK, 1997.
[8] M. Despeisse, P. D. Ball, S. Evans, and A. Levers, “Indus-trial ecology at factory level—a conceptual model,” Journal ofCleaner Production, vol. 31, pp. 30–39, 2012.
[9] L. Smith and P. Ball, “Steps towards sustainable manufacturingthrough modelling material, energy and waste flows,” Interna-tional Journal of Production Economics, vol. 140, no. 1, pp. 227–238, 2012.
[10] H. Nezhad, “World energy scenarios to 2050: issues andoptions,” White Paper, Metropolitan State University, Min-neapolis, Minn, USA, 2009.
[11] M. Mani, J. Madan, J. H. Lee, K. Lyons, and S. K. Gupta,“Characterizing sustainability for manufacturing performanceassessment,” in Proceedings of the ASME International DesignEngineering Technical Conferences and Computers and Informa-tion in Engineering Conference, pp. 1153–1162, Chicago, Ill, USA,August 2012.
[12] C. B. Joung, J. Carrell, P. Sarkar, and S. C. Feng, “Categorizationof indicators for sustainable manufacturing,” Ecological Indica-tors, vol. 24, pp. 148–157, 2013.
[13] M. Kovac, “Comparison of foresights in the manufacturingresearch,” Transfer Inovaciı, vol. 23, pp. 284–288, 2012.
[14] M. T. Sweeney, “Towards a unified theory of strategic manu-facturingmanagement,” International Journal of Operations andProduction Management, vol. 11, no. 8, pp. 6–23, 1991.
[15] J. G. Miller and A. V. Roth, “A taxonomy of manufacturingstrategies,” Management Science, vol. 40, no. 3, pp. 285–304,1994.
[16] M. T. Frohlich and J. R. Dixon, “A taxonomy of manufacturingstrategies revisited,” Journal of Operations Management, vol. 19,no. 5, pp. 541–558, 2001.
[17] A. J. de Ron, “Sustainable production: the ultimate result of acontinuous improvement,” International Journal of ProductionEconomics, vol. 56-57, pp. 99–110, 1998.
Journal of Industrial Engineering 11
[18] P. Heikkurinen and K. J. Bonnedahl, “Corporate responsibilityfor sustainable development: a review and conceptual compar-ison of market- and stakeholder-oriented strategies,” Journal ofCleaner Production, vol. 43, pp. 191–198, 2013.
[19] T. L. Saaty, The Analytic Hierarchy Process, McGraw-Hill, NewYork, NY, USA, 1980.
[20] T. L. Saaty, Decision Making with Dependence and Feedback:TheAnalytic Network Process, RWSPublications, Pittsburgh, Pa,USA, 2nd edition, 2001.
[21] A. Gupta, R. Vangari, A. D. Jayal, and I. S. Jawahir, “Priorityevaluation of product metrics for sustainable manufacturing,”in Global Product Development, A. Bernard, Ed., pp. 631–641,Springer, Berlin, Germany, 2011.
[22] D. Krajnc and P. Glavic, “A model for integrated assessmentof sustainable development,” Resources, Conservation and Recy-cling, vol. 43, no. 2, pp. 189–208, 2005.
[23] I. H. Garbie, “Framework of manufacturing enterprises sus-tainability incorporating globalization issues,” in Proceedings ofthe 41st International Conference on Computers and IndustrialEngineering, Los Angeles, Calif, USA, 2011.
[24] K. de Brucker, C. MacHaris, and A. Verbeke, “Multi-criteriaanalysis and the resolution of sustainable development dilem-mas: a stakeholder management approach,” European Journal ofOperational Research, vol. 224, no. 1, pp. 122–131, 2013.
[25] S. B. Sirikrai and J. C. S. Tang, “Industrial competitivenessanalysis: using the analytic hierarchy process,” Journal of HighTechnology Management Research, vol. 17, no. 1, pp. 71–83, 2006.
[26] A. I. Chatzimouratidis and P. A. Pilavachi, “Technological,economic and sustainability evaluation of power plants usingthe analytic hierarchy process,” Energy Policy, vol. 37, no. 3, pp.778–787, 2009.
[27] M. S. Chiacchio, “Early impact assessment for sustainable devel-opment of enabling technologies,” Total Quality Managementand Excellence, vol. 39, no. 3, pp. 1–6, 2011.
[28] N. Subramanian and R. Ramanathan, “A review of applicationsof analytic hierarchy process in operations management,” Inter-national Journal of Production Economics, vol. 138, no. 2, pp.215–241, 2012.
[29] A. Ishizaka and A. Labib, “Selection of new production facilitieswith the Group Analytic Hierarchy Process Ordering method,”Expert Systems with Applications, vol. 38, no. 6, pp. 7317–7325,2011.
[30] O. S. Vaidya and S. Kumar, “Analytic hierarchy process: anoverview of applications,” European Journal of OperationalResearch, vol. 169, no. 1, pp. 1–29, 2006.
[31] W. Ho, “Integrated analytic hierarchy process and itsapplications—a literature review,” European Journal ofOperational Research, vol. 186, no. 1, pp. 211–228, 2008.
[32] M.-L. Tseng, “Modeling sustainable production indicators withlinguistic preferences,” Journal of Cleaner Production, vol. 40,pp. 46–56, 2013.
[33] M. L. Tseng and A. S. F. Chiu, “Evaluating firm’s green supplychain management in linguistic preferences,” Journal of CleanerProduction, vol. 40, pp. 22–31, 2013.
[34] L. Ocampo and E. Clark, “A framework for capturing uncer-tainty of group decision-making in the context of the analytichierarchy/network process,” Advances in Industrial Engineeringand Management, vol. 3, no. 3, pp. 7–16, 2014.
[35] T. L. Saaty, “The analytic hierarchy and analytic networkmeasurement processes: applications to decisions under risk,”European Journal of Pure and Applied Mathematics, vol. 1, no. 1,pp. 122–196, 2008.
[36] M. A. B. Promentilla, T. Furuichi, K. Ishii, and N. Tanikawa,“A fuzzy analytic network process for multi-criteria evaluationof contaminated site remedial countermeasures,” Journal ofEnvironmental Management, vol. 88, no. 3, pp. 479–495, 2008.
[37] M. A. B. Promentilla, T. Furuichi, K. Ishii, and N. Tanikawa,“Evaluation of remedial countermeasures using the analyticnetwork process,”Waste Management, vol. 26, no. 12, pp. 1410–1421, 2006.
[38] L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8,pp. 338–353, 1965.
[39] M.-L. Tseng, “A causal and effect decision making modelof service quality expectation using grey-fuzzy DEMATELapproach,” Expert Systems with Applications, vol. 36, no. 4, pp.7738–7748, 2009.
[40] A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arith-metic: Theory and Applications, Van Nostrand-Reinhold, NewYork, NY, USA, 1985.
[41] R.-C. Wang and S.-J. Chuu, “Group decision-making usinga fuzzy linguistic approach for evaluating the flexibility ina manufacturing system,” European Journal of OperationalResearch, vol. 154, no. 3, pp. 563–572, 2004.
[42] C. Von Altrock, “Practical fuzzy-logic design,” Circuit CellarINK: The Computer Applications Journal, vol. 75, pp. 1–5, 1996.
[43] M.-L. Tseng, R. Wang, A. S. F. Chiu, Y. Geng, and Y. H. Lin,“Improving performance of green innovation practices underuncertainty,” Journal of Cleaner Production, vol. 40, pp. 71–82,2013.
[44] S. Opricovic and G.-H. Tzeng, “Defuzzification within a multi-criteria decision model,” International Journal of Uncertainty,Fuzziness and Knowlege-Based Systems, vol. 11, no. 5, pp. 635–652, 2003.
[45] M. L. Tseng, Y. H. Lin, A. S. F. Chiu, and J. C. H. Liao, “UsingFANP approach on selection of competitive priorities basedon cleaner production implementation: a case study in PCBmanufacturer, Taiwan,” Clean Technologies and EnvironmentalPolicy, vol. 10, no. 1, pp. 17–29, 2008.
[46] D. Paulson and S. Zahir, “Consequences of uncertainty in theanalytic hierarchy process: a simulation approach,” EuropeanJournal of Operational Research, vol. 87, no. 1, pp. 45–56, 1995.
[47] D. Hauser and P. Tadikamalla, “The analytic hierarchy processin an uncertain environment: a simulation approach,” EuropeanJournal of Operational Research, vol. 91, no. 1, pp. 27–37, 1996.
[48] S. C. Wheelwright, “Manufacturing strategy: defining the miss-ing link,” Strategic Management Journal, vol. 5, no. 1, pp. 77–91,1984.
[49] Y. H. Lin, H.-P. Cheng, M.-L. Tseng, and J. C. C. Tsai,“UsingQFD andANP to analyze the environmental productionrequirements in linguistic preferences,” Expert Systems withApplications, vol. 37, no. 3, pp. 2186–2196, 2010.
