# A Sustainable Manufacturing Strategy from Different Strategic Responses under Uncertainty

Authors: Lanndon Ocampo; Eppie Clark; Kae Vines Tanudtanud
Journal: Journal of Industrial Engineering
 (2015)

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## Abstract

This paper presents a decision framework that highlights the integration of manufacturing strategy (MS) and sustainability along with strategic responses as a significant component. This integration raises  complexity and uncertainty in decision-making following the number of subjective components with their inherent relationships that must be brought into context and the huge amount of required information in eliciting judgments. Thus, a proposed hybrid multicriteria decision-making (MCDM) approach in 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 set theory (FST) is used to describe vagueness in decision-making which is carried out by eliciting judgments in pairwise comparisons using linguistic variables with corresponding triangular fuzzy numbers (TFNs). Probability theory is used to handle randomness in aggregating judgments of multiple decision-makers. Results show that a stakeholder-oriented approach is considered the most relevant strategic response in developing a sustainable manufacturing strategy. The contribution of this work lies in identifying the policies which constitute a sustainable manufacturing strategy using an integrated MCDM approach under uncertainty.


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## Body

## 1. Introduction

The work of Wickham Skinner in 1969 became the focal point of discussion regarding the role of manufacturing strategy in attaining corporate goals and objectives. Skinner [1] developed the hierarchical top-down strategy framework that links corporate strategy, business strategies, and functional strategies which include manufacturing strategy [2]. This framework eventually became the guidelines of later approaches in this research domain [3–5]. Scholars agree that manufacturing strategy could only support business strategy if a sequence of decisions over structural and infrastructural categories is consistent over a considerable amount of time [6]. Structural decision areas include process technology, facilities, capacity, and vertical integration while infrastructural decision areas contain organization, manufacturing planning 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 decision area requires careful attention and systems thinking due to the number of decision components that must be taken into consideration which make the decision-making a complex one. Manufacturing strategy has evolved as a diverse field covering theoretical and empirical works across various disciplines; however, the field is criticized over its lack of progress particularly on its integration with current approaches [5] with emphasis on sustainability.Emerging concerns on sustainability compel manufacturing firms to incorporate in their decision-making processes the interests of the triple-bottom line, that is, economic, environmental, and social issues [7], especially those related to material, energy, and wastes [8, 9] as the primary concerns of manufacturing. Manufacturing industry holds one-third of world energy consumption and CO2 emissions are increasing at a significant rate [10, 11]. Thus, an approach known as sustainable manufacturing was eventually coined by the U.S. Department of Commerce which promotes the idea of having products and processes which are not only profitable, but also safe to the environment and to the society [12]. This gains significant attention from both industry and academia following research agenda in developed economies working toward this direction [13].This work attempts to provide a framework that integrates manufacturing strategy and sustainable manufacturing with emphasis on considering strategic responses of firms toward sustainability. The proposed framework identifies the policies that must be placed in various manufacturing decision areas with the goal of promoting competitiveness and sustainability which were understated in previous literature in this field. The main departure of this work is the integrative decision-making framework that attempts to holistically capture various decision components with their intrinsic interrelationships. The emphasis on strategic responses was highly motivated by several works, for example, Sweeney [14], Miller and Roth [15], and Frohlich and Dixon [16] together with the work of de Ron [17] and Heikkurinen and Bonnedahl [18]. Sweeney [14] made the first attempt to comprehensively group manufacturing strategies into “generic manufacturing strategies” that include caretaker, marketeer, reorganizer, and innovator strategies. The main idea was that manufacturing organizations, as the result of the complex interactions of organizational culture and values with the options taken by business and manufacturing policies, tend to brand themselves into a specific stance on key decisions in developing manufacturing strategy. This claim was further elaborated by Miller and Roth [15] and was eventually supported by Frohlich and Dixon [16] using different empirical samples. However, with slight modifications, Frohlich and Dixon [16] identified three types of manufacturing strategies that include caretakers, marketeers, and innovators.Aside from classifying manufacturing strategy types, Sweeney [14] highlighted the notion of transition paths or routes for firms to achieve the most positive form of strategy. These transition paths served as guide for firms on manufacturing policies and competitive advantages they must place to support a particular route. This research domain became prominent following several published works which show consistency of the types of strategic responses. With the onset of growing interests in sustainability, former taxonomies were paralleled by the responses or stances of firms toward sustainability issues as described by the works of de Ron [17] and Heikkurinen and Bonnedahl [18]. Their works highlighted the three strategic responses that firms engage in embracing sustainability issues: stakeholder-oriented, market-oriented, and sustainability-oriented.This paper attempts to extend previous works on strategic responses by integrating manufacturing taxonomies of Sweeney [14], Miller and Roth [15], and Frohlich and Dixon [16] with the sustainability responses described by de Ron [17] and Heikkurinen and Bonnedahl [18]. This work highlights the framework which introduces two transition routes: first is the stakeholder-oriented→market-oriented→sustainability-oriented route and second is the stakeholder-oriented→sustainability-oriented route. These responses and transition routes are brought into the context of developing a sustainable manufacturing strategy. The goal is to identify the content strategy that addresses both competitiveness and sustainability as the result of these transition routes. Due to the complexity in decision-making, the use of multicriteria decision-making methods (MCDM) particularly analytic hierarchy process/analytic network process (AHP/ANP) becomes appropriate and fundamental. AHP/ANP is a theory of relative measurement that allows decision-makers to structure their decision problems into a hierarchy or a network with subjective components [19, 20]. A number of AHP/ANP applications include computing product sustainability index [21], computing sustainability index with time as an element [22], developing sustainability index for a manufacturing enterprise [23], developing multiactor multicriteria approach in complex sustainability project evaluation [24], evaluating industrial competitiveness [25], evaluating energy sources [26], and developing an AHP-based impact matrix and sustainability-cost benefit analysis [27]. A number of reviews of AHP/ANP application in operations management [28], its methodological development [29], dominant applications [30], and its integration with other operations research tools [31] were conducted.Following various arguments on the uncertainty of decision-making in the context of AHP/ANP ([32]; Tseng and Chiu [33]), this work adopted the proposed method of Ocampo and Clark [34] coined as PROFUZANP which is based on fuzzy set theory, probability theory, and ANP. In this approach, ANP is used to handle decision-making complexity, FST is used to address vagueness individual decision-maker’s judgment, and probability theory is used to handle randomness of aggregating judgments. The contribution of this work lies in identifying policy options of manufacturing strategy resulting from the strategic responses carried out by manufacturing organizations. This paper is organized as follows: Section 2 provides an introduction of ANP, fuzzy set theory, and the PROFUZANP approach, Section 3 describes the problem structure, Section 4 presents the results of this study, and finally Section 5 highlights a discussion and conclusion of this work.


## 2. Methodology

### 2.1. Analytic Network Process (ANP)

ANP is the general framework of analyzing complex decisions with qualitative and quantitative components and elements [19, 20]. ANP structures the decision problem as a network of decision components and elements with dependence relationships. Saaty [35] explained that pairwise comparison is central to the measurement of subjective or of intangible elements. Pairwise comparisons are done by comparing elements with respect to a parent element from the same or another component. These comparisons form a positive reciprocal square matrix. Saaty [19] proposed an eigenvalue problem which determines the local eigenvector (w) of the matrix:
(1)Aw=λmax⁡w,
where A is the positive reciprocal of the pairwise comparison matrix, λmax⁡ is the largest eigenvalue of matrix A, and w is the principal eigenvector associated with λmax⁡. For consistent judgment, λmax⁡=n; otherwise, λmax⁡>n. Consistency of judgment is measured using the consistency index (CI) and consistency ratio (CR). The consistency index (CI) is a measure of the degree of consistency and is represented by
(2)CI=λmax⁡-nn-1.The consistency ratio (CR) is computed as(3)CR=CIRI,
where RI is the mean random consistency index. CR≤0.10 is an acceptable degree of inconsistency [19]. Decision-makers would be asked to reconsider paired comparisons in case of CR>0.10.Local eigenvectors are plugged into the supermatrix. The numerical approach of computing the global priority vector is done by normalizing columns and then raising the supermatrix to large powers. This approach enables the supermatrix convergence to a limit value. Each column of the limit supermatrix is a unique positive column eigenvector associated with the principal eigenvalueλmax⁡ [36]. This principal column eigenvector resembles the stable priorities of the limit supermatrix and can be used to measure the overall relative dominance of one element over another element in a network structure [37].


### 2.2. Fuzzy Set Theory (FST)

Zadeh [38] introduced the fuzzy set theory (FST) as a mathematical way in handling imprecision and vagueness in decision-making. In particular, fuzzy numbers provide a way of expressing vagueness in FST. 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 uFx is a continuous mapping on the closed interval (0, 1). Various forms of a fuzzy number are available but the widely used one is the triangular fuzzy number (TFN) [36, 39]. TFN can be defined as a triplet A=l,m,u with a membership function. An introductory discussion of fuzzy numbers and their arithmetic operations can be found in Kaufmann and Gupta [40].FST is shown to enhance MCDM methods in handling complex and imprecise judgments. Since most evaluators find it hard to elicit numerical judgments, more realistic evaluations use linguistic variables to represent judgment [41]. Linguistic variables have the form of phrases or sentences in a natural language [42]. Tseng [39] argues that linguistic variables can be appropriately associated with corresponding TFNs.


### 2.3. PROFUZANP Approach

PROFUZANP approach was detailed in the work of Ocampo and Clark [34]; important points are discussed in this section. This approach shares similarity with the works of Tseng [32] which transform TFNs into crisp values before raising the pairwise comparisons matrices to large powers. Since any fuzzy aggregation method requires defuzzification [39], the defuzzification process used by Tseng et al. [43] is derived from the algorithm proposed by Opricovic and Tzeng [44]. The linguistic variables are presented in Table 1 with equivalent TFNs adopted from Tseng et al. [45].Table 1
Linguistic variables adopted from Tseng et al. [45].
Linguistic scale
Code
Triangular fuzzy scale
Triangular fuzzy reciprocal scale
Just equal
(1, 1, 1)
(1, 1, 1)
Equal importance
EQ
(1/2, 1, 3/2)
(2/3, 1, 2)
Moderate importance
MO
(5/2, 3, 7/2)
(2/7, 1/3, 2/5)
Strong importance
ST
(9/2, 5, 11/2)
(2/11, 1/5, 2/9)
Demonstrated importance
DE
(13/2, 7, 15/2)
(2/15, 1/7, 2/13)
Extreme importance
EX
(17/2, 9, 9)
(1/9, 1/9, 2/17)The notations used in this paper were lifted from the notations used by works of Tseng [32]. Suppose a set of k decision-makers with w~ijk=(a1ijk,a2ijk,a3ijk) as the influence of ith criteria on jth criteria assessed by the kth evaluators.Normalization:(4)xa1ijk=a1ijk-min⁡⁡a1ijkΔmin⁡max⁡,(5)xa2ijk=a2ijk-min⁡⁡a1ijkΔmin⁡max⁡,(6)xa3ijk=a3ijk-min⁡⁡a1ijkΔmin⁡max⁡,
where
(7)Δmin⁡max⁡=max⁡⁡a3ijk-min⁡⁡a1ijk.Compute leftls and right rs normalized values:
(8)xlsijk=xa2ijk1+xa2ijk-xa1ijk,xrsijk=xa3ijk1+xa3ijk-xa2ijk.Compute total normalized crisp value:(9)xijk=xlsijk1-xlsijk+xrsijkxrsijk1-xlsijk+xrsijk.Compute crisp values:(10)wijk=min⁡⁡a1ijk+xijkΔmin⁡max⁡.In integrating judgments of decision-makers, however, Ocampo and Clark [34] introduced a probabilistic approach defined as
(11)w~ij=w-ij±1-αp,
where w~ij is an integrated judgment of decision-makers from a normal distribution that represents the influence of row element on column element, w-ij is the geometric mean of all judgment of decision-makers of i on j and is defined as
(12)w-ij=wij1wij2wij3…wijkk.1-α is the confidence level of the distribution, and p∈0,1 is proportion of perturbation about the geometric mean. The value of p denotes a range of judgmental uncertainties that usually range from 2% to 20% [46].


### 2.4. Procedure

The procedure implemented in this paper can be described as follows.(1)
The decision model was established from a rigorous literature review that integrates classical manufacturing strategy and sustainability into a single model. Figure1 shows the problem structure developed in this work.(2)
Respondents were selected to provide expert judgments of the problem structure.(3)
Following the AHP/ANP context, pairwise comparisons were performed based on the problem structure. Comparisons were elicited using the linguistic variables in Table1. Using (4) through (10), corresponding crisp values of the TFNs were computed.(4)
Local priority vectors and CI and CR values of pairwise comparisons matrices were computed using (1) through (3).(5)
Using (11) through (12) by assigning α=0.05 and with assigned p=0.25 which shows the upper limit perturbation of judgments [47], judgments of individual decision-makers were aggregated. Local aggregated priority vectors of these matrices were obtained using (1).(6)
An initial supermatrix from the decision model was constructed and then was populated with local eigenvectors obtained in step (4) for each value ofp. Normalizing columns and raising the supermatrix to large powers solve the global priority vector.Figure 1
Problem structure.


## 2.1. Analytic Network Process (ANP)

ANP is the general framework of analyzing complex decisions with qualitative and quantitative components and elements [19, 20]. ANP structures the decision problem as a network of decision components and elements with dependence relationships. Saaty [35] explained that pairwise comparison is central to the measurement of subjective or of intangible elements. Pairwise comparisons are done by comparing elements with respect to a parent element from the same or another component. These comparisons form a positive reciprocal square matrix. Saaty [19] proposed an eigenvalue problem which determines the local eigenvector (w) of the matrix:
(1)Aw=λmax⁡w,
where A is the positive reciprocal of the pairwise comparison matrix, λmax⁡ is the largest eigenvalue of matrix A, and w is the principal eigenvector associated with λmax⁡. For consistent judgment, λmax⁡=n; otherwise, λmax⁡>n. Consistency of judgment is measured using the consistency index (CI) and consistency ratio (CR). The consistency index (CI) is a measure of the degree of consistency and is represented by
(2)CI=λmax⁡-nn-1.The consistency ratio (CR) is computed as(3)CR=CIRI,
where RI is the mean random consistency index. CR≤0.10 is an acceptable degree of inconsistency [19]. Decision-makers would be asked to reconsider paired comparisons in case of CR>0.10.Local eigenvectors are plugged into the supermatrix. The numerical approach of computing the global priority vector is done by normalizing columns and then raising the supermatrix to large powers. This approach enables the supermatrix convergence to a limit value. Each column of the limit supermatrix is a unique positive column eigenvector associated with the principal eigenvalueλmax⁡ [36]. This principal column eigenvector resembles the stable priorities of the limit supermatrix and can be used to measure the overall relative dominance of one element over another element in a network structure [37].


## 2.2. Fuzzy Set Theory (FST)

Zadeh [38] introduced the fuzzy set theory (FST) as a mathematical way in handling imprecision and vagueness in decision-making. In particular, fuzzy numbers provide a way of expressing vagueness in FST. 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 uFx is a continuous mapping on the closed interval (0, 1). Various forms of a fuzzy number are available but the widely used one is the triangular fuzzy number (TFN) [36, 39]. TFN can be defined as a triplet A=l,m,u with a membership function. An introductory discussion of fuzzy numbers and their arithmetic operations can be found in Kaufmann and Gupta [40].FST is shown to enhance MCDM methods in handling complex and imprecise judgments. Since most evaluators find it hard to elicit numerical judgments, more realistic evaluations use linguistic variables to represent judgment [41]. Linguistic variables have the form of phrases or sentences in a natural language [42]. Tseng [39] argues that linguistic variables can be appropriately associated with corresponding TFNs.


## 2.3. PROFUZANP Approach

PROFUZANP approach was detailed in the work of Ocampo and Clark [34]; important points are discussed in this section. This approach shares similarity with the works of Tseng [32] which transform TFNs into crisp values before raising the pairwise comparisons matrices to large powers. Since any fuzzy aggregation method requires defuzzification [39], the defuzzification process used by Tseng et al. [43] is derived from the algorithm proposed by Opricovic and Tzeng [44]. The linguistic variables are presented in Table 1 with equivalent TFNs adopted from Tseng et al. [45].Table 1
Linguistic variables adopted from Tseng et al. [45].
Linguistic scale
Code
Triangular fuzzy scale
Triangular fuzzy reciprocal scale
Just equal
(1, 1, 1)
(1, 1, 1)
Equal importance
EQ
(1/2, 1, 3/2)
(2/3, 1, 2)
Moderate importance
MO
(5/2, 3, 7/2)
(2/7, 1/3, 2/5)
Strong importance
ST
(9/2, 5, 11/2)
(2/11, 1/5, 2/9)
Demonstrated importance
DE
(13/2, 7, 15/2)
(2/15, 1/7, 2/13)
Extreme importance
EX
(17/2, 9, 9)
(1/9, 1/9, 2/17)The notations used in this paper were lifted from the notations used by works of Tseng [32]. Suppose a set of k decision-makers with w~ijk=(a1ijk,a2ijk,a3ijk) as the influence of ith criteria on jth criteria assessed by the kth evaluators.Normalization:(4)xa1ijk=a1ijk-min⁡⁡a1ijkΔmin⁡max⁡,(5)xa2ijk=a2ijk-min⁡⁡a1ijkΔmin⁡max⁡,(6)xa3ijk=a3ijk-min⁡⁡a1ijkΔmin⁡max⁡,
where
(7)Δmin⁡max⁡=max⁡⁡a3ijk-min⁡⁡a1ijk.Compute leftls and right rs normalized values:
(8)xlsijk=xa2ijk1+xa2ijk-xa1ijk,xrsijk=xa3ijk1+xa3ijk-xa2ijk.Compute total normalized crisp value:(9)xijk=xlsijk1-xlsijk+xrsijkxrsijk1-xlsijk+xrsijk.Compute crisp values:(10)wijk=min⁡⁡a1ijk+xijkΔmin⁡max⁡.In integrating judgments of decision-makers, however, Ocampo and Clark [34] introduced a probabilistic approach defined as
(11)w~ij=w-ij±1-αp,
where w~ij is an integrated judgment of decision-makers from a normal distribution that represents the influence of row element on column element, w-ij is the geometric mean of all judgment of decision-makers of i on j and is defined as
(12)w-ij=wij1wij2wij3…wijkk.1-α is the confidence level of the distribution, and p∈0,1 is proportion of perturbation about the geometric mean. The value of p denotes a range of judgmental uncertainties that usually range from 2% to 20% [46].


## 2.4. Procedure

The procedure implemented in this paper can be described as follows.(1)
The decision model was established from a rigorous literature review that integrates classical manufacturing strategy and sustainability into a single model. Figure1 shows the problem structure developed in this work.(2)
Respondents were selected to provide expert judgments of the problem structure.(3)
Following the AHP/ANP context, pairwise comparisons were performed based on the problem structure. Comparisons were elicited using the linguistic variables in Table1. Using (4) through (10), corresponding crisp values of the TFNs were computed.(4)
Local priority vectors and CI and CR values of pairwise comparisons matrices were computed using (1) through (3).(5)
Using (11) through (12) by assigning α=0.05 and with assigned p=0.25 which shows the upper limit perturbation of judgments [47], judgments of individual decision-makers were aggregated. Local aggregated priority vectors of these matrices were obtained using (1).(6)
An initial supermatrix from the decision model was constructed and then was populated with local eigenvectors obtained in step (4) for each value ofp. Normalizing columns and raising the supermatrix to large powers solve the global priority vector.Figure 1
Problem structure.


## 3. Problem Structure

In reference to the discussion in Section1, the problem structure is described into two parts. The first part presents the hierarchical structure of corporate strategy, business strategy, and functional strategies. Each component is composed of a number of elements defined in the literature. The second part highlights the influence of strategic responses in developing a manufacturing strategy. This part is supported by the hierarchical structure of decision categories, policy areas, and policy options as described by Wheelwright [48]. A number of policy options comprise a particular policy area and each policy area is composed of a number of finite policy options. This part elucidates the departure of this work from the current literature. Figure 1 presents the problem structure developed in this work.The problem structure in Figure1 is composed of eight components which are the goal, corporate strategy, business strategy, manufacturing strategy, strategic responses, manufacturing strategy decision categories, policy areas, and policy options. The model is largely motivated by the hierarchical framework of Skinner [1] with the inclusion of strategic responses as a mediating component that prescribes the content of the manufacturing strategy. Each component comprises a set of decision elements. The goal component contains a single element which is to develop a sustainable manufacturing strategy. Corporate strategy component has two elements: sustainable businesses and commitment to sustainability. Business strategy has likewise two elements: market-oriented and technology-oriented. Manufacturing strategy is a single-element component which is a sustainable manufacturing strategy. Strategic responses component has three elements: stakeholder-oriented, market-oriented, and sustainability-oriented. Manufacturing decision categories component has nine elements and each element has its own set of policy areas as proposed by Wheelwright [48] and Hallgren and Olhager [4]. Likewise, each policy contains policy options available to the manufacturing firm. The objective of this problem structure is to provide the content of a sustainable manufacturing strategy as the result of the overarching problem structure in Figure 1. Using the proposed PROFUZANP approach developed by Ocampo and Clark [34], the proposed model will be able to determine the policy options for each policy area. These options constitute the sustainable manufacturing strategy of the manufacturing firm. In order to facilitate easy recall, a comprehensive coding system is shown in Table 2 to represent each element in the decision model.Table 2
Coding system of the elements of the problem structure.
Decision components
Decision elements
Code
Goal
Develop sustainable manufacturing strategy
A
Corporate strategy
Sustainable businesses
D1
Commitment to sustainability
D2
Business strategy
Market-oriented
E1
Technology-oriented
E2
Manufacturing strategy
Sustainable manufacturing strategy
F
Strategic responses
Stakeholder-oriented
G1
Market-oriented
G2
Sustainability-oriented
G3
Manufacturing decision categories
Process technology
C1
Facilities
C2
Capacity
C3
Vertical integration
C4
Organization
C5
Manufacturing planning and control
C6
Quality
C7
New product introduction
C8
Human resources
C9
Policy areas
Process choice
C11
Technology
C12
Process integration
C13
Facility size
C21
Facility location
C22
Facility focus
C23
Capacity amount
C31
Capacity timing
C32
Capacity type
C33
Direction
C41
Extent
C42
Balance
C43
Structure
C51
Reporting levels
C52
Support groups
C53
System design
C61
Decision support
C62
Systems integration
C63
Defect prevention
C71
Monitoring
C72
Intervention
C73
Rate of innovation
C81
Product design
C82
Industrialization
C83
Skill level
C91
Pay
C92
Security
C93
Job shop
C111
Batch
C112
Continuous
C113
Project
C114
Robotics
C121
Flexible manufacturing system
C122
Computer-aided manufacturing
C123
Cellular
C131
Process
C132
Product
C133
One big plant
C211
Several smaller ones
C212
Close to market
C221
Close to supplier
C222
Close to technology
C223
Close to competitor
C224
Close to source of raw materials
C225
Product groups
C231
Process types
C232
Life cycle stages
C233
Fixed units per period
C311
Based on inputs
C312
Based on outputs
C313
Leading
C321
Chasing
C322
Following
C323
Potential
C331
Immediate
C332
Effective
C333
Forward
C411
Backward
C412
Policy options
Horizontal
C413
Sources of raw materials
C421
Distribution to final customers
C422
Low degree
C431
Medium degree
C432
High degree
C433
Functional
C511
Product groups
C512
Geographical
C513
Top
C521
Middle
C522
First line
C523
Large groups
C531
Small groups
C532
Make-to-order
C611
Make-to-stock
C612
Close support
C621
Loose support
C622
High degree
C631
Low degree
C632
High quality
C711
Low degree
C712
High frequency
C721
Low frequency
C722
High frequency
C731
Low frequency
C732
Slow
C811
Fast
C812
Standard
C821
Customized
C822
New processes
C831
Follow-the-leader policy
C832
Specialized
C911
Not specialized
C912
Based on hours worked
C921
Quantity/quality of output
C922
Seniority
C923
Training
C931
Recognition for achievement
C932
Promotion
C933In the coding system shown in Table2, the goal component is assigned as A and corporate strategy as D. Business strategy is denoted as E, manufacturing strategy is represented as F and strategic responses as G, and decision category, policy areas, and policy options components are designated with appropriate alpha-numeric codes of C#, C##, and C###, respectively, with # representing an integer. The coding system in Table 2 is so structured to facilitate remembering of elements associated with their parent element. For instance, C111 represents job shop process and is listed down as the first policy option under C11 policy area which is process choice. C11, on the other hand, also defines the first policy option under C1 manufacturing decision category, process technology.Respondents were carefully selected with the goal of providing well-informed and expert judgments of the problem structure. Preselection of these respondents was based on their expertise in the manufacturing industry. All domain experts are located in the Philippines who worked for multinational manufacturing firms and were exposed to international practices. In this work, ten experts were selected to provide valid results. Background checks were done for each respondent based on available public data. A threshold criterion is set at least 10-year managerial experience in manufacturing industries to ensure that they have the capability and previous knowledge in carrying out vital manufacturing decisions. This choice of these respondents is consistent with the MCDM studies published by Lin et al. [49], Tseng [32], and Tseng and Chiu [33].The following steps were undertaken for data gathering required for this work. Questionnaires containing pairwise comparisons matrices were distributed to ten respondents. They were arranged for a supervised survey according to their preferred schedule. However, in a case of a respondent with no available time for a supervised survey or being out of the country, an email containing the survey questionnaire was sent. Respondents were given a maximum of two to four weeks to answer the questionnaire. Within this time period, regular follow-ups were made to ensure timeliness of their output. Upon receipt of their answers, judgment consistency for each pairwise comparisons matrix was checked. When inconsistency was observed, specific matrices were emailed back to the respondent with a note why the matrix is inconsistent. This means that the respondent was informed that his or her judgment on elements a to b and b to c is inconsistent with his or her judgment on a to c. Respondents were given another one or two weeks to reconsider their judgment on specific inconsistent matrices. At this period, regular follow-ups were undertaken.


## 4. Results

For brevity, the computations carried out in this work are not presented in this paper. All computations were performed using Microsoft Excel spreadsheet and a VBA add-on. A sample pairwise comparisons matrix in linguistic variables from a single decision-maker is shown in Table3.Table 3
A sample pairwise comparisons matrix in linguistic variables.
Stakeholder-oriented
Market-oriented
Sustainability-oriented
Stakeholder-oriented
1/MO
1/MO
Market-oriented
MO
Sustainability-orientedNote that only the upper triangle of the matrix is filled out as the lower triangle represents the straightforward reciprocal of the upper triangular matrix. This matrix compares the influence of strategic responses in addressing the goal of developing a sustainable manufacturing strategy. From Table3, corresponding TFNs and their respective reciprocals are shown in Table 4.Table 4
A sample pairwise comparisons matrix in TFNs.
Stakeholder-oriented
Market-oriented
Sustainability-oriented
Stakeholder-oriented
(1, 1, 1)
(2/7, 1/3, 2/5)
(2/7, 1/3, 2/5)
Market-oriented
(5/2, 3, 7/2)
(1, 1, 1)
(5/2, 3, 7/2)
Sustainability-oriented
(5/2, 3, 7/2)
(2/7, 1/3, 2/5)
(1, 1, 1)From the TFNs shown in Table4, the defuzzification process proposed by Opricovic and Tzeng [44] is used to obtain appropriate crisp values of the TFNs. Crisp values were computed using (4) through (10). Table 5 shows the corresponding crisp values of the sample pairwise comparisons matrix under consideration.Table 5
A sample pairwise comparisons matrix in crisp values.
Stakeholder-oriented
Market-oriented
Sustainability-oriented
Stakeholder-oriented
1.0000
0.3349
0.3349
Market-oriented
2.9863
1.0000
2.9646
Sustainability-oriented
2.9863
0.3373
1.0000Applying the same process to the rest of the decision-makers’ judgments and pairwise comparisons matrices, pairwise comparisons matrices in crisp values are obtained. Aggregation of all judgments across decision-makers is then processed at the pairwise comparisons matrix level. The goal of the aggregation process is to come up with a single matrix that fairly represents the judgments of all decision-makers for a specific pairwise comparisons matrix. Using the proposed PROFUZANP approach of Ocampo and Clark [34] which is also presented in Section 2.3, aggregating judgment of decision makers involves the application of (11) and (12). With α set at 0.05, which is widely used in statistical analysis, and the value of p set at 0.25 following the argument of Hauser and Tadikamalla [47], a sample aggregated matrix of the same set of comparisons is shown in Table 6.Table 6
A sample aggregated pairwise comparisons matrix withα=0.05,p=0.25.
Stakeholder-oriented
Market-oriented
Sustainability-oriented
Stakeholder-oriented
1
1.521558
1.062221
Market-oriented
0.657221
1
1.71808
Sustainability-oriented
0.941424
0.582045
1From the aggregated matrix, local priority vectors, the principal eigenvalue, and CR value were then computed using (1), (2), and (3), respectively. Table 7 shows a sample of a local eigenvector of an aggregated pairwise comparisons matrix.Table 7
A sample local eigenvector of an aggregated pairwise comparisons matrix.
Local eigenvector
Stakeholder-oriented
0.391017
Market-oriented
0.331778
Sustainability-oriented
0.277206
λ
max
⁡ = 3.094; CR = 0.090.Each column of the limiting pairwise comparisons matrix in Table8 is the principal eigenvector of the matrix as discussed by Saaty [19]. CR values of all pairwise comparisons matrix are below the 0.10 threshold value. The local priority vectors of all aggregated pairwise comparisons matrices are populated in the supermatrix. The general supermatrix of the decision model presented in Figure 1 is shown in Table 8.Table 8
Generalized supermatrix.
A
D
E
F
G
C#
C##
C###
A
I
1
1
1
1
1
1
1
D
DA
I
0
0
0
0
0
0
E
0
ED
I
0
0
0
0
0
F
0
0
FE
I
0
0
0
0
G
GA
0
0
GF
GG
0
0
0
C#
0
0
0
0
C#G
C#C#
0
0
C##
0
0
0
0
0
C##C#
I
1
C###
0
0
0
0
0
0
C###C##
IThe numerical supermatrix runs in the order of116×116; thus it is difficult to present the results here as it consumes large amount of space. For brevity, the generalized supermatrix and the resulting global priority vector are only shown to elucidate the process of the ANP. In order to determine the priority ranking of each policy choice in relation to the goal, Table 9 ranks policy choice according to decreasing global priority values. This ranking provides insights for firms on the sequence of implementation of policy choices. However, this must be taken into context together with business conditions, individual corporate goals, and the propensity of investment decisions of firms. Table 9 provides the ranking of the priority policy choices.Table 9
Global priority vector and ranking.
Code
Decision elements
Global eigenvector
Rank
A
Develop sustainable manufacturing strategy
0.323232
1
D1
Sustainable businesses
0.113131
1
D2
Commitment to sustainability
0.048485
2
E1
Market-oriented
0.035479
2
E2
Technology-oriented
0.045329
1
F
Sustainable manufacturing strategy
0.040404
1
G1
Stakeholder-oriented
0.085396
1
G2
Market-oriented
0.05754
2
G3
Sustainability-oriented
0.038882
3
C1
Process technology
0.021136
1
C2
Facilities
0.012683
6
C3
Capacity
0.017592
2
C4
Vertical integration
0.009193
8
C5
Organization
0.011126
7
C6
Manufacturing planning and control
0.013316
5
C7
Quality
0.016043
3
C8
New product introduction
0.013573
4
C9
Human resources
0.00655
9
C11
Process choice
0.002177
3
C12
Technology
0.005874
1
C13
Process integration
0.002517
2
C21
Facility size
0.001338
3
C22
Facility location
0.003449
1
C23
Facility focus
0.001554
2
C31
Capacity amount
0.003014
2
C32
Capacity timing
0.004287
1
C33
Capacity type
0.001495
3
C41
Direction
0.002395
1
C42
Extent
0.001002
3
C43
Balance
0.0012
2
C51
Structure
0.0025
1
C52
Reporting levels
0.0017
2
C53
Support groups
0.001363
3
C61
System design
0.003618
1
C62
Decision support
0.000991
3
C63
Systems integration
0.002049
2
C71
Defect prevention
0.005918
1
C72
Monitoring
0.000949
3
C73
Intervention
0.001155
2
C81
Rate of innovation
0.002823
1
C82
Product design
0.002372
2
C83
Industrialization
0.001591
3
C91
Skill level
0.001533
1
C92
Pay
0.000814
3
C93
Security
0.000928
2
C111
Job shop
0.000308
2
C112
Batch
0.000314
1
C113
Continuous
0.000264
3
C114
Project
0.000202
4
C121
Robotics
0.001173
2
C122
Flexible manufacturing system
0.00139
1
C123
Computer-aided manufacturing
0.000374
3
C131
Cellular
0.000479
2
C132
Process
0.000583
1
C133
Product
0.000197
3
C211
One big plant
0.000484
1
C212
Several smaller ones
0.000185
2
C221
Close to market
0.000541
1
C222
Close to supplier
0.000486
2
C223
Close to technology
0.000272
4
C224
Close to competitor
0.000109
5
C225
Close to source of raw materials
0.000316
3
C231
Product groups
0.000315
1
C232
Process types
0.000211
3
C233
Life cycle stages
0.000251
2
C311
Fixed units per period
0.000504
2
C312
Based on inputs
0.000521
1
C313
Based on outputs
0.000482
3
C321
Leading
0.001199
1
C322
Chasing
0.000502
2
C323
Following
0.000443
3
C331
Potential
0.000211
2
C332
Immediate
0.000175
3
C333
Effective
0.000362
1
C411
Forward
0.000524
1
C412
Backward
0.000413
2
C413
Horizontal
0.00026
3
C421
Sources of raw materials
0.000385
1
C422
Distribution to final customers
0.000116
2
C431
Low degree
8.45
E
-
05
3
C432
Medium degree
0.000264
1
C433
High degree
0.000252
2
C511
Functional
0.000708
1
C512
Product groups
0.000372
2
C513
Geographical
0.00017
3
C521
Top
0.000244
3
C522
Middle
0.000331
1
C523
First line
0.000275
2
C531
Large groups
0.000313
2
C532
Small groups
0.000368
1
C611
Make-to-order
0.001068
1
C612
Make-to-stock
0.000741
2
C621
Close support
0.000378
1
C622
Loose support
0.000118
2
C631
High degree
0.00085
1
C632
Low degree
0.000174
2
C711
High quality
0.002623
1
C712
Low degree
0.000336
2
C721
High frequency
0.000417
1
C722
Low frequency
5.75
E
-
05
2
C731
High frequency
0.000432
1
C732
Low frequency
0.000145
2
C811
Slow
0.000297
2
C812
Fast
0.001115
1
C821
Standard
0.000751
1
C822
Customized
0.000435
2
C831
New processes
0.000665
1
C832
Follow-the-leader policy
0.00013
2
C911
Specialized
0.000625
1
C912
Not specialized
0.000141
2
C921
Based on hours worked
0.000149
2
C922
Quantity/quality of output
0.000194
1
C923
Seniority
6.43
E
-
05
3
C931
Training
0.000237
1
C932
Recognition for achievement
0.000134
2
C933
Promotion
9.21
E
-
05
3Table10 provides the policy choice for each manufacturing decision area. These policies constitute a sustainable manufacturing strategy. Special attention is also regarded in the prioritization of strategic responses. This sets the type of manufacturing strategy the business would engage. Priority rankings of corporate strategy, business strategy, and strategic responses are presented in Table 11.Table 10
Content of a sustainable manufacturing strategy considering strategic responses.
Code
Policy area
Code
Highest priority policy choice
C11
Process choice
C112
Batch
C12
Technology
C122
Flexible manufacturing system
C13
Process integration
C132
Process
C21
Facility size
C211
One big plant
C22
Facility location
C221
Close to market
C23
Facility focus
C231
Product groups
C31
Capacity amount
C312
Based on inputs
C32
Capacity timing
C321
Leading
C33
Capacity type
C333
Effective
C41
Direction
C411
Forward
C42
Extent
C421
Sources of raw materials
C43
Balance
C432
Medium degree
C51
Structure
C511
Functional
C52
Reporting levels
C522
Middle
C53
Support groups
C532
Small groups
C61
System design
C611
Make-to-order
C62
Decision support
C621
Close support
C63
Systems integration
C631
High degree
C71
Defect prevention
C711
High quality
C72
Monitoring
C721
High frequency
C73
Intervention
C731
High frequency
C81
Rate of innovation
C812
Fast
C82
Product design
C821
Standard
C83
Industrialization
C831
New processes
C91
Skill level
C911
Specialized
C92
Pay
C922
Quantity/quality of output
C93
Security
C931
TrainingTable 11
Ranking of other decision components.
Rank
Strategy
Corporate strategy
1
Sustainable businesses
2
Commitment to sustainability
Business strategy
1
Technology-oriented
2
Market-oriented
Strategic responses
1
Stakeholder-oriented
2
Market-oriented
3
Sustainability-orientedPriority rankings of corporate strategy, business strategy, and strategic responses are presented in Table11. The rankings show that sustainable businesses have the highest priority in corporate strategy and a market-oriented business strategy is highly prioritized. Inconsistent with previous works, stakeholder-oriented approach poses most inclination toward developing a sustainable manufacturing strategy.


## 5. Discussion and Conclusion

Sustainable businesses emerge as a more relevant corporate strategy in addressing sustainability and competitiveness. The difference between sustainable businesses and commitment to sustainability is that the former is a proactive approach where sustainability is embedded in corporate values and culture while the latter takes on a more reactive stance where decision-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 experts consider technology-orientation as an opportunity to lead an industry into developing environmentally benign technologies with minimum environmental footprint and with less impact on society’s health and well-being. A market-oriented approach on the other hand is limited to the interests of market which is a narrow approach toward sustainability. As opposed to the initial contention that the highest form of manufacturing strategy orientation is a sustainability-orientation [17, 18], results of this work suggest that a stakeholder-orientation in manufacturing serves as a more practical approach in developing a sustainable manufacturing strategy. This result could be viewed from two different perspectives. First, experts likely believe that a better approach in addressing sustainability is hearing the “voice of the stakeholder.” Although it is a reactive stance, this approach ensures that the manufacturing firm addresses each issue of the stakeholders transparently without missing important points along the process. It is particularly similar with the “quality functional deployment” approach in the past; however, a more holistic “voice of the stakeholder” is presented. While a proactive stance in sustainability-orientation is obviously a desirable stance, experts might believe that, in the long run, the manufacturing firms may approach sustainability toward a “strong sustainability” framework where development is very limited. Second, it could be that experts have imprecise knowledge of the contested concepts of sustainability. Since sustainability is still a growing field in the literature with more contestations than a single meaning, experts prefer to give higher priorities to stakeholder-orientation when competitiveness and sustainability are placed into context.A total of 27 decisions constitute the sustainable manufacturing strategy. Table10 provides specific policy options corresponding to policy areas. Using the PROFUZANP approach of Ocampo and Clark [34], the decision model provides the content of the sustainable manufacturing strategy. It shows that the content strategy is inclined toward process-centered technology with continuous processes, big, product life cycle stages-focused facilities which are close to suppliers and customers, leading capacity strategy, a backward vertical integration toward sources of raw materials, first-line reporting with functional structure organization, a minimal inventory-focused manufacturing planning and control, high quality prevention, monitoring and intervention policies, fast product introduction with new processes, and highly skilled workers. The content of the sustainable manufacturing strategy is expected to address both competitiveness and sustainability in manufacturing.



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*Source: 210568-2015-02-10.xml*