Advances in Production Engineering & Management
Volume 10 | Number 3 | September 2015 | pp 125-139 http://dx.doi.Org/10.14743/apem2015.3.197
ISSN 1854-6250
Journal home: apem-journal.org Original scientific paper
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
Ocampo, L.A.a*, Clark, E.E.b, Tanudtanud, K.V.G.c, Ocampo, C.O.V.d, Impas Sr., C.G.a, Vergara, V.G.a, Pastoril, J.a, Tordillo, J.A.S.a
aUniversity of San Carlos, Department of Mechanical Engineering, Cebu City, Cebu, Philippines bDe La Salle University, Department of Industrial Engineering, 2401 Taft Avenue, Manila, Philippines international Society for Business Innovation & Technology Management (ISBITM), Radium St., Manila, Philippines dUniversity of San Carlos, Department of Industrial Engineering, Cebu City, Cebu, Philippines
A B S T R A C T
A R T I C L E I N F O
This paper adopts a fuzzy analytic network process approach for developing a sustainable manufacturing strategy under the influence of stakeholders' interests. Frameworks developed in literature tend to structure manufacturing strategy in such a way that addresses market needs and expectations. As the move towards sustainability becomes highly pronounced, literature in domain manufacturing is developing approaches and initiatives that explore different facets of sustainability. However as this impetus becomes increasingly famous, manufacturing firms are faced with the challenge of integrating sustainability with the classical function of manufacturing, which is to support firms' competitive advantages. Thus, an inclusive approach would constitute a manufacturing strategy that would support not only sustainability but enhance the competitive strategy of a firm. In order to integrate these two objectives it is necessary to take into consideration the different stakeholders' interests as significant drivers towards sustainability. This work explores the significance of these interests when developing a manufacturing strategy using the proposed approach. In the proposed method, an analytic network process handles the complexity of the decision framework, and judgment elicitation during pairwise comparisons is described using linguistic variables with equivalent triangular fuzzy numbers. The proposed approach is useful when handling complexity and uncertainty especially in group decision-making. The content of the sustainable manufacturing strategy using a fuzzy analytic process is presented in this paper. © 2015 PEI, University of Maribor. All rights reserved.
Keywords:
Manufacturing strategy
Sustainability
Uncertainty
Analytic network process Fuzzy set theory
*Corresponding author: don_leafriser@yahoo.com (Ocampo, Lanndon A.)
Article history: Received 20 May 2015 Revised 12 August 2015 Accepted 19 August 2015
1. Introduction
The classical model of Skinner [1] and Wheelwright [2] on manufacturing strategy was highly motivated by market behavior and market requirements. Resulting from buying experiences, dynamic needs, etc., the market creates a priority set of the four widely accepted competitive priorities which are cost, quality, dependability and flexibility [2-4]. This prioritization process of the market motivates the priority set of competitive priorities of a business unit which eventually influences the manufacturing function. When manufacturing decisions are consistent over nine decision categories, manufacturing creates capabilities which must be positioned in line with the competitive priorities set up by the business unit. This network of influences seems to function only when the market is solely the focal point of interest. However, this network fails to
125
Ocampo, Clark, Tanudtanud, Ocampo, Impas, Vergara, Pastoril, Tordillo
address the conditions that demand simultaneous considerations of several stakeholders. The best example of these conditions is sustainability-related issues. Thus, an update of this network becomes necessary to address the complex interests of various stakeholders.
An emerging body of literature claims that the role of stakeholders in the sustainability efforts of firms is arguably significant [5-7]. Aside from exerting pressures on manufacturing firms which is the general claim [8], stakeholders could assist firms in deciding which environmental and social initiatives to adopt because stakeholders have already established some forms of perspectives, experiences and resources vital in addressing sustainability issues. Creating initiatives that enhance close relations with employees and suppliers advances the capability of firms in integrating environmental aspects into key organizational processes. With the emerging issues on sustainability encountered by manufacturing firms, manufacturing organizations must proac-tively create value through investment in customers, suppliers, employees, processes, technology and innovation [9]. Models developed by previous literature lack quantitative integration of manufacturing strategy and sustainable manufacturing into a framework that addresses both sustainability and competitiveness.
This paper aims to develop the content of a sustainable manufacturing strategy with the influence of different stakeholders' interests. This is significant as it provides possible direction for manufacturing industry on the policy options that must be made in order to address both competitiveness and sustainability of manufacturing. Due to the multi-criteria nature of the decision problem under vague decisions which are brought about by the subjective nature of most of the criteria, a fuzzy analytic network process is thus used. This approach was also used in identifying the structural decisions of sustainable manufacturing strategy under the relevance of firm sizes [10] and of the strategic responses of firms [11]. Fuzzy set theory handles the uncertainty of decision-making [12] while analytic network process is a multi-criteria decision making tool which is used to handle complex decision-making [13]. The use of analytic network process and its special case, the analytic hierarchy process, in strategy and sustainability research is rich in literature, e.g. Ocampo and Clark [14], Ocampo [15], Pan et al. [16]. The contribution of this work lies in developing a comprehensive framework in identifying specific decisions that comprise a sustainable manufacturing strategy with the influence of stakeholders' interests.
2. Literature review 2.1 Manufacturing strategy
Definitions of manufacturing strategy presented by previous studies can be summarized into few unifying concepts. First, manufacturing strategy represents a pattern of coordinated and consistent decisions over a relatively narrow area [17]. Second, manufacturing strategy determines the capabilities of the manufacturing function and provides its competitive advantage [18]. Lastly, manufacturing strategy is consistent with the objectives of the business strategy [17-19]. Inspired largely by the work of Skinner [1], subsequent works agreed that manufacturing function involves a number of decision categories which are shown in Table 1.
Depending on the decisions made within these categories, manufacturing strategy develops a set of capabilities [21]. Four competitive priorities were widely known in literature: cost, quality, dependability and flexibility [2, 3].
Table 1 Manufacturing decision categories
Manufacturing decision categories
Source_Policy areas [2,20]
Process technology
Facilities
Capacity
Vertical integration Organization
Manufacturing planning and control Quality
New product introduction Human resources
[2, 3,20]	Process choice, technology, integration
[1-3, 20]	Size, location, focus
[2, 3,20]	Amount, timing, type
[2, 3,20]	Direction, extent, balance
[1, 3,20]	Structure, reporting levels, support groups
[1, 2,20]	System design, decision support, systems integration
[2, 3,20]	Defect prevention, monitoring, intervention
[1, 3,20]	Rate of innovation, product design, industrialization
[1-3]_Skill level, pay, security_
126
Advances in Production Engineering & Management 10(3) 2015
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
Competing on cost requires a manufacturing strategy that minimizes the inefficiencies of manufacturing operations so that products are offered at low costs. This is addressed by labor, materials, capital returns, inventory turnover and unit costs [3]. Manufacturing strategy that emphasizes quality as the dominant capability requires higher quality in standard product or one that offers broader features or performance characteristics compared to other competitors with similar products. Measurement could be percent defectives, frequency field failure, cost of quality and mean time between failures [3]. Dependability involves a manufacturing system that is able to do work as specified, delivered on time and the firm makes sure that its resources are ready so that any failures are corrected immediately. It could be achieved by dealing on product mix flexibility, volume flexibility and lead time for new products [3]. Measurement indicators could be percentage of on-time shipments, average delay and expediting response time [3]. A comprehensive discussion of these four capabilities was outlined by Ward et al. [22].
Note that the competitive strategy reinforced by the manufacturing strategy must support the competitive advantage defined by the business strategy as depicted by Skinner's [1] hierarchical framework. Moreover, aside from maintaining this competitive advantage, the strategy adopted must create and maintain the manufacturing competitive position in the market. Different manufacturing firms emphasize each of the four competitive capabilities in varying degrees [2]. To summarize, manufacturing strategy is derived from business and corporate strategies [1] which are largely driven by the market. Market establishes the requirements of the business unit and consequently identifies the set of competitive priorities. Manufacturing strategy provides the necessary policy to support the strategy of the business while at the same time creates capabilities in the long run. This framework generally addresses competitiveness of the business unit with limited information on how this works when sustainability is eventually placed into the context. One challenging issue that needs to be resolved in the framework is the presence of stakeholders' interests that must be considered when confronting sustainability agenda.
2.2 Sustainable manufacturing
While other economic sectors share responsibilities in addressing sustainability, manufacturing sector is undoubtedly an important piece of the puzzle [23]. With expected five-fold increase in GDP per capita over the next fifty years, a corresponding ten-fold increase in total impact in energy consumption, material usage and wastes generation is expected [24]. Hassine et al. [25] pointed out that the energy consumption of manufacturing industries account for 30 % of the global energy demand and 36 % of the global carbon dioxide emissions. This consumption implies adverse environmental impact and degradation of natural resources [25]. Being the leading employment sector and main contributor to the GDP, the manufacturing sector serves as the "backbone" to the well-being of nations and societies [24]. With this, sustainable manufacturing, as an approach, has emerged and is defined by the U.S. Department of Commerce as "the creation of manufactured products that use processes that minimize negative environmental impacts, conserve energy and natural resources, are safe for employees, communities and consumers and are economically sound" [26]. Sustainable manufacturing gained overwhelming interests both in industry and academia and inspired leading developed economies to design responsive policy platforms [27]. Nevertheless, this approach gained global momentum [28].
A concise framework on sustainability in general and on sustainable manufacturing in particular is the triple-bottom line approach [29, 30] which was introduced by Elkington [31]. This approach maintains that sustainable manufacturing is achieved by simultaneously considering environmental stewardship, economic growth, and social well-being [26]. This framework has been adopted by various operations management researches [32-35]. While this sounds impressive, it does not provide clear direction on the competitive function of manufacturing as described by Skinner's [1] framework. Conceptual frameworks are on sustainable manufacturing based on the TBL approach. These could be summarized as follows: (1) sustainability is further achieved through collaboration in the supply chain [36, 37], (2) a comprehensive approach to sustainability is through the life-cycle approach [38, 39], and (3) different stakeholders have significant roles in sustainability transformation [40, 41].
Advances in Production Engineering & Management 10(3) 2015
127
Ocampo, Clark, Tanudtanud, Ocampo, Impas, Vergara, Pastoril, Tordillo
2.3 Stakeholders' interests
Recent studies have placed high regard on the role of stakeholders in forging sustainability of manufacturing organizations, e.g. [42-44]. Stakeholders comprise those who are influenced, either directly or indirectly, by the actions carried out by the firm [9]. These include employees, suppliers, customers, industry associations, universities, consultants, governments, community organizations, and the media [44]. Pham and Thomas [9] argue that traditional organizations tend to focus only on a handful, limited number of stakeholders with special attention to shareholders such as board of directors and investors. Griffiths and Petrick [42] contend that such approach fails to develop stakeholder integration for firms. A widely accepted notion is that when stakeholders are managed well, they are capable in offering invaluable assistance and resources beyond simply exerting pressures on firms [45, 46]. For instance, customers can possibly exert pressure on suppliers to establish environmental programs as a precondition to supply [7]. On the other hand, employees can provide recommendations for advancing firm's responsibility to the community by pointing out inputs related to the current socio-economic conditions of the local community. Suppliers play a critical role in providing insights which are associated to technology, materials and processes that could be helpful in strengthening firm's environmental efforts [47, 48]. Harrison et al. [49] claim that manufacturing firms are likely to build trusting relations across several stakeholders when firms integrate them in their key decision-making processes. Having stronger relations with stakeholders, necessary insights for deciding how to allocate limited resources in order to satisfy stakeholders are certainly gained.
3. Methodology
3.1 Fuzzy set theory
Fuzzy set theory was developed by Zadeh [50] as a mathematical approach of handling imprecision and vagueness in decision-making. A fuzzy number can be represented by a fuzzy set F = {(x, uF(x)),x e Ml where x e M and uF{x) ^ [0,1]. The binary set [0, 1] is a crisp set and any value that is represented between 0 and 1 indicates partial acceptance. Various types of fuzzy numbers emerge in literature but the widely used one is the triangular fuzzy number (TFN) [51, 52]. TFN can be defined as a triplet A = (l,m,u) and the membership function can be defined as
VA(x) =
and the representation of a TFN is
^00 1
0
(x- l)/(m — 0 \(u — l)/(u — m) 0
(1)
Fig. 1 A TFN A = (l,m,u) [10]
Suppose two TFNs A and B are defined by the triplet (a1,a2,a3) and (b1,b2,b3), respectively. The basic operations of these two TFNs are as follows:
128
Advances in Production Engineering & Management 10(3) 2015
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
Ä + B = (a1,a2,a3) + (b1,b2,b3) = (a1 + b1,a2 +b2,a3 + b3) Ä-B = (a1,a2,a3) - (b1,b2,b3) = -b1,a2 -b2,a3 -b3)
Ä = (a1,a2,a3) x (b1,b2,b3) = (a1b1,a2b2,a3b3) Ä + B = (a1,a2,a3) ^ (bt,b2,b3) = (at /b3,a2 /b2,a3 /b1)
(2)
(3)
(4)
(5)
FST enhances the capability of MCDM methods in handling complex and imprecise judgments [10]. Most evaluators could hardly elicit exact numerical values to represent opinions based on human judgment [52]. More realistic evaluations use linguistic variables to represent judgment rather than numerical values [53]. Linguistic variable represents linguistic values with form of phrases or sentences in a natural language [54]. Expressing judgment in linguistic variables is a useful method in dealing with situations that are described in quantitative expressions [53]. The integration of fuzzy set theory in the context of AHP/ANP draws several techniques. Refer to the work of Promentilla et al. [51], Wang et al. [55], Ocampo and Clark [56] for a review on these techniques. The approach adopted in this study shares similarity with the works of Tseng [12, 52] which transform TFNs into crisp values before raising the pairwise comparisons matrices to large powers. This method has been used because of the simplicity of the approach and the validity of previous works that embarked on it. Tseng [52] argued that any fuzzy aggregation method must contain defuzzification method. An algorithm in determining the crisp values was proposed by Opricovic and Tzeng [57]. The linguistic variables are presented in Table 2 with equivalent TFNs adopted from Tseng et al. [58].
Table 2 Linguistic variables adopted from Tseng et al. [58]
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)
Let wjj = (a,[ij,a2ij,a3ij) be the influence of ith criteria on jth criteria assessed by the kth evaluator. The defuzzification process proposed by Opricovic and Tzeng [57] is as follows:
Step 1: Normalization
xalij ~
k
xa2ij =
k
xa3ij =
alij ~ mln alij
	h max
	hmin
«2 ij	— min a^j
	\max
	min
ak u3lJ	— min a^ij
\max hmin
(6)
(7)
(8)
where
Arni£=max a3ij - min alij-
Step 2: Computation of left Is and right rs normalized values
,k
xlsfj =
xa
2ij
1+xa2ij -xa^j
(9)
Advances in Production Engineering & Management 10(3) 2015
129
Ocampo, Clark, Tanudtanud, Ocampo, Impas, Vergara, Pastoril, Tordillo
k
xr4 = T3¿i k	cío)
1 'XCl3ij Xa2ij
Step 3: Computation of total normalized crisp value
xrshxrs?
_ x^sij{1 xlsLj) +	(i.)
i - xls¡, + xrs¡,
LJ	LJ
Step 4: Computation of crisp values
w^minaZij+xfjÜZfä	(í2)
3.2 Analytic network process
ANP is the general theory of analysing complex decision problems where analytic hierarchy process (AHP) is a special case. Local priorities in ANP are obtained similar to how local priorities in AHP are computed; that is, by performing paired comparisons. In ANP, the decision problem is structured as a network of constructs that describes dependence relations of one component on another component. The advantage of using ANP in a wide array of decision problems is in capturing both qualitative and quantitative criteria in a model that attempts to resemble reality. The input of local priorities depends on the presence and type of dependence relations described in the network. The eigenvector method, as described in the theory of Oskar Perron which was discussed by Saaty [59], is referred to as the exact way of computing relative local priorities of these elements. Saaty [60] proposed an eigenvalue problem to obtain the desired ratio-scale priority vector (or weights) w of n elements:
= Amaxw	(13)
where A is the positive reciprocal pairwise comparisons matrix, Amax is the maximum (or principal) eigenvalue of matrix A. For consistent judgment, Amax = n, otherwise, Amax >n. The measure of judgment consistency 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
CI = Amax ~n	(14)
n — 1
The consistency ratio (CR) is computed as
™=f (15)
where RI is the mean random consistency index. CR < 0.10 is an acceptable degree of inconsistency. Decision-makers would be asked to reconsider the paired comparisons in case of CR > 0.10.
Global priority ratio scales or priorities can be computed based on the synthesizing principle of the supermatrix [51]. By raising the matrix to large powers, the transmission of influence along all possible paths in the network is captured in the process [13]. The convergence of initial priorities (stochastic matrix) to an equilibrium value in the limit supermatrix provides a set of meaningful synthesized priorities from the underlying decision network [51]. Saaty [13] assured that as long as the supermatrix representation is a primitive irreducible matrix in a strongly connected digraph, the initial supermatrix will eventually converge to a limit supermatrix. The numerical approach of solving the limit supermatrix denoted by L is by normalizing columns and then raising the supermatrix to p = 2k + 1 power where k is an arbitrary large number.
130
Advances in Production Engineering & Management 10(3) 2015
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
S
v
lim I —
= Um (S)p = L
(16)
■max
Each column of the limit supermatrix is a unique positive column eigenvector associated with the principal eigenvalue Amax [51]. This resembles the priorities of the limit supermatrix and can be used to measure the overall relative dominance of one element over another element in a network [51].
3.3 Procedure
To summarize, the research procedure implemented in this paper is as follows:
1.	Perform pairwise comparisons based from the decision network motivated from literature. The generic question that is asked in doing pairwise comparison is "Given a control element, a component (element) of a given network, and given a pair of component (or element), how much more does a given member of the pair dominate other member of the pair with respect to a control element?" [51]. Instead of using the Saaty's fundamental scale, comparisons are made using linguistic scales as shown in Table 2.
2.	Transform linguistic variables into corresponding TFNs in Table 2. Using Eq. 6 through Eq. 12, compute corresponding crisp values of the TFNs.
3.	Compute local priority vectors, CI and CR values of pairwise comparisons matrices using Eq. 13 through Eq. 15. If CR > 0.10, decision-makers should be asked to reconsider judgments in paired comparisons.
4.	Aggregate the pairwise comparisons matrices of decision-makers using Eq. 17. After constructing aggregated pairwise comparisons matrices, compute local priority vectors of these matrices using Eq. 13.
5. Construct an initial supermatrix from the decision network developed in step 1. Then, populate this initial supermatrix with the local priority vectors obtained in step 4. Normalize the columns of the initial supermatrix in order to attain a stochastic matrix. Then raise the stochastic matrix to large powers Eq. 16 to compute for the final priority vector.
4. Decision model
Following the literature review in Section 2, the decision model can be described into two parts. The first part presents the hierarchical structure of decision categories, policy areas and policy options. This shows that each decision category is composed of policy areas and each policy area has policy options or choices. This part is largely influenced by the second part of the model. The second part illustrates the relationships of stakeholders' interests, competitive priorities and strategic responses. Stakeholders' interests dominate competitive priorities which is vital in sustainability. Instead of the market exclusively setting up the competitive priorities consistent with the former arguments of Wheelwright [2], the model holistically considers the interests of different stakeholders in determining competitive priorities. These priorities influence the strategic responses of firms toward sustainability. In effect, these responses influence the decisions which would eventually comprise the sustainable manufacturing strategy. Fig. 2 shows the decision model developed in this work.
(17)
Advances in Production Engineering & Management 10(3) 2015
131
Ocampo, Clark, Tanudtanud, Ocampo, Impas, Vergara, Pastoril, Tordillo
-jatV
Strategic
responses
Fig. 1 Proposed decision model
The decision model in Fig. 2 has six components which are composed of the goal, stakeholders' interests, competitive priorities, strategic responses, manufacturing strategy decision categories, policy areas and policy options. These components are linked together in a network of dependence relations. Each component of the model comprises respective decision elements. The goal component contains a single element which is to develop SMS. Stakeholders' interests have two sub-components: stakeholders' component which has eight decision elements and stakeholders' interests' component with 28 children elements. Competitive priorities have four elements as discussed in the previous section. Strategic responses have three elements which are 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 described in Table 1. Furthermore, each policy contains policy options which a manufacturing firm could deliberately choose from. The objective of this work is to analytically choose a particular set of options that comprise SMS which best addresses the goal resulting from the interrelationships of the components and elements described in Fig. 2. In order to facilitate easier computations, a comprehensive coding system is shown in Table 3 to represent each element in the decision model. The coding system is so structured to facilitate remembering of elements associated with their parent element.
Table 3 Coding system of the stakeholder-motivated competitive priority decision model Decision components Decision elements
Goal
Stakeholders
Stakeholders' sustainability interests
develop sustainable manufacturing strategy government suppliers shareholders business customers
consumers
community
employees competitors
government's increased taxes
government's environmental protection
government's health & safety suppliers' compliance with international standards suppliers' quality suppliers' cost suppliers' delivery
Code	Decision components	Decision elements	Code
A	Policy options	job shop	C111
H1 H2 H3 H4 H5		batch continuous project robotics flexible manufacturing system computer-aided manufacturing cellular process	C112 C113 C114 C121 C122
H6 H7 H8			C123 C131 C132
H11		product	C133
H12		one big plant	C211
H13		several smaller ones	C212
H21		close to market	C221
H22 H23 H24		close to supplier close to technology close to competitor	C222 C223 C224
132
Advances in Production Engineering & Management 10(3) 2015
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
Table 3 Coding system of the stakeholder-motivated competitive priority decision model (continuation)
Competitive priorities
Strategic responses
Manufacturing decision categories
Policy areas
shareholders' profitability
shareholders' environmental equity
shareholders' social equity business customers' quality business customers' cost business customers' delivery business customers' international certifications consumers' quality consumers' cost consumers' delivery community's environmental effect
community's employment community's health & safety employees' health & safety employees' benefits employees' salaries & wages employees' career development
competitors' complying international standards competitors' quality competitors' cost competitors' delivery cost quality dependability flexibility
stakeholder-oriented
market-oriented
sustainability-oriented
process technology
facilities capacity
vertical integration organization
manufacturing planning &
control
quality
new product introduction
human resources
process choice
technology
process integration
facility size
facility location
facility focus
capacity amount
capacity timing
capacity type
direction
extent
balance
structure
reporting levels
support groups
system design
decision support
systems integration
defect prevention
monitoring
intervention
rate of innovation
product design
industrialization
skill level
pay
security_
H31
H32
H33 H41 H42 H43
H44
H51 H52 H53
H61
H62 H63 H71 H72 H73
H74
H81
H82 H83 H84
11
12
13
14 G1 G2 G3
C1
C2 C3 C4 C5
C6
C7
C8
C9
C11
C12
C13
C21
C22
C23
C31
C32
C33
C41
C42
C43
C51
C52
C53
C61
C62
C63
C71
C72
C73
C81
C82
C83
C91
C92
C93
close to source of raw mate- C225 rials
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
horizontal	C413
sources of raw materials	C421
distribution to final custo-
C42 2
mers
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	C933
Advances in Production Engineering & Management 10(3) 2015
133
Ocampo, Clark, Tanudtanud, Ocampo, Impas, Vergara, Pastoril, Tordillo
Respondents were carefully selected to provide expert judgment of the decision problem raised from this work. Initially, respondents were selected in advance and selection was based on their expertise in the manufacturing industry. This choice of respondents is consistent with the MCDM studies published by Tseng and Chiu [12]. All experts are located in the Philippines who worked for multinational manufacturing firms and were exposed to international practices. In this work, ten expert respondents were selected to provide meaningful results.
5. Results and discussion
For brevity, a sample pairwise comparisons matrix in linguistic variables from a single decision-maker is shown in Table 4.
Note that only the upper triangle of the matrix is filled out as the lower triangle represents straightforward reciprocal value of the upper triangular. This matrix describes the comparisons of stakeholders with their significance in addressing the goal of developing a sustainable manufacturing strategy. From Table 4, corresponding TFNs are shown in Table 5.
Table 4 A sample pairwise comparisons matrix in linguistic variables
Government	1/MO	1/MO	1/MO	1/MO	1/MO	1/MO	1/MO
Suppliers		1/MO	1/MO	1/MO	1/MO	MO	MO
Shareholders			1/MO	1/MO	MO	MO	MO
Business customers				1/MO	MO	MO	MO
Consumers					MO	MO	MO
Community						MO	MO
Employees							MO
Competitors
Table 5 A sample pairwise comparisons matrix in TFNs
3
m
Government	(1,1,1)	(2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5) (2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5)
Suppliers	(5/2,3,7/2) (1,1,1)	(2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5) (2/7,1/3,2/5)	(5/2,3,7/2)	(5/2,3,7/2)
Shareholders	(5/2,3,7/2) (5/2,3,7/2)	(1,1,1)	(2/7,1/3,2/5)	(2/7,1/3,2/5) (5/2,3,7/2)	(5/2,3,7/2)	(5/2,3,7/2)
Business customers	(5/2,3,7/2) (5/2,3,7/2)	(5/2,3,7/2)	(1,1,1)	(2/7,1/3,2/5) (5/2,3,7/2)	(5/2,3,7/2)	(5/2,3,7/2)
Consumers	(5/2,3,7/2) (5/2,3,7/2)	(5/2,3,7/2)	(5/2,3,7/2)	(1,1,1)	(5/2,3,7/2)	(5/2,3,7/2)	(5/2,3,7/2)
Community	(5/2,3,7/2) (5/2,3,7/2)	(2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5)	(1,1,1)	(5/2,3,7/2)	(5/2,3,7/2)
Employees	(5/2,3,7/2) (2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5) (2/7,1/3,2/5)	(1,1,1)	(5/2,3,7/2)
Competitors_(5/2,3,7/2) (2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5)	(2/7,1/3,2/5) (2/7,1/3,2/5)	(2/7,1/3,2/5)	(1,1,1)_
Table 6 shows the corresponding crisp values of the sample pairwise comparisons matrix obtained from a single decision-maker.
From the aggregated matrix, local priority vectors, the principal eigenvalue and CR value were then computed. 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 Fig. 2 is shown in Table 7.
134	Advances in Production Engineering & Management 10(3) 2015
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
Table 6 A sample pairwise comparisons matrix in crisp values
		Government	Suppliers		Shareholder	Business customers	Consumers	Community	Employees	Competitors	Eigenvector
Government		1	0.3349		0.3349	0.3349	0.3349	0.3349	0.3349	0.3349	0.0398
Suppliers		2.9863	1		0.3349	0.3349	0.3349	0.3349	2.9646	2.9646	0.0902
Shareholders		2.9863	2.9863		1	0.3349	0.3349	2.9646	2.9646	2.9646	0.1557
Business customers		2.9863	2.9863		2.9863	1	0.3349	2.9646	2.9646	2.9646	0.2048
Consumers		2.9863	2.9863		2.9863	2.9863	1	2.9646	2.9646	2.9646	0.2692
Community		2.9863	2.9863		0.3373	0.3373	0.3373	1	2.9646	2.9646	0.1188
Employees		2.9863	0.3373		0.3373	0.3373	0.3373	0.3373	1	2.9646	0.0689
Competitors		2.9863	0.3373		0.3373	0.3373	0.3373	0.3373	0.3373	1	0.0525
Vax = 9.086, C.R.= 0.1											
			Table 7 The generalized supermatrix								
	A	H#	H##			I	G	C#		C##	C###
A	I	1		1		1	1	1		1	1
H#	H#A	I		0		H#I	0	0		0	0
H##	0	H##H#		I		0	0	0		0	0
I	IA	0		0		I	IG	0		0	0
G	GA	0		0		0	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##		I
Because the numerical supermatrix runs in the order 151x151, it is highly difficult to present it here as it requires 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. Shown in Table 8 are the decision elements with corresponding codes, the global priority vector and ranking of each element per decision component.
Table 8 Priority ranking across content of sustainable manufacturing strategy
Rank	Code	Priority policy choice	Code	Policy area
1	C711	high quality	C71	defect prevention
2	C122	flexible manufacturing system	C12	technology
3	C321	leading	C32	capacity timing
4	C812	fast	C81	rate of innovation
5	C611	make-to-order	C61	system design
6	C631	high degree	C63	systems integration
7	C821	standard	C82	product design
8	C511	functional	C51	structure
9	C831	new processes	C83	industrialization
10	C911	specialized	C91	skill level
11	C132	process	C13	process integration
12	C221	close to market	C22	facility location
13	C411	forward	C41	direction
14	C312	based on inputs	C31	capacity amount
15	C211	one big plant	C21	facility size
16	C731	high frequency	C73	intervention
17	C721	high frequency	C72	monitoring
18	C421	sources of raw materials	C42	extent
19	C621	close support	C62	decision support
20	C532	small groups	C53	support groups
21	C333	effective	C33	capacity type
22	C522	middle	C52	reporting levels
23	C112	batch	C11	process choice
24	C231	product groups	C23	facility focus
25	C432	medium degree	C43	balance
26	C931	training	C93	security
27	C922	quantity/quality of output	C92	pay
Advances in Production Engineering & Management 10(3) 2015
135
Ocampo, Clark, Tanudtanud, Ocampo, Impas, Vergara, Pastoril, Tordillo
Based on Table 8, high quality defect prevention has the highest priority with respect to the goal. The 'Priority policy choice' column in Table 8 shows the content of the sustainable manufacturing strategy following stakeholders' interests. Process technology decision area is ranked first in the manufacturing strategy decision category and closely followed by capacity. Continuous consideration in material, energy and wastes flows in the production of manufacturing products highlights improvement in developing environmentally-benign technologies [38, 61]. Creation of highly energy-efficient technologies such as new machineries, new processes, new packaging, new material that produce less wastes increase the capability of manufacturing industry in supporting the triple-bottom line [62]. Process technology serves as an interesting focal point in sustainability-related advancements. In each of the manufacturing decision category, priority policy areas are: technology in process technology decision, facility location in facilities decision, capacity timing in capacity decision, direction in vertical integration decision, structure in organization decision, system design in manufacturing planning and control decision, defect prevention in quality decision, rate of innovation in new product introduction, and skill level in human resources decision. Having this prioritization enables practitioners to further focus on more important area within a decision category.
6. Conclusion
The main contribution of this work is on the development of a sustainable manufacturing strategy decision model that incorporates the interests of different stakeholders. The proposed model highlights the integration of sustainability consideration with competitive function of manufacturing. Since the model illustrates a complex decision-making under uncertainty, this paper proposed the combination of fuzzy set theory and analytic network process. Analytic network process handles the complex dependence relationships among constructs in the decision problem while fuzzy set theory addresses the uncertainty of individual judgment. Although the proposed methodological approach addresses uncertainty and vagueness in complex decision-making, performing a large number of pairwise comparisons may be cumbersome to decision-makers and may require significant amount of time. Alternatively, further simplification of the proposed decision model such that a decision hierarchy is achieved could be handled by Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) and other multi-criteria decision-making tools. However, such simplification process may oversimplify the decision problem which may lead to counterintuitive results. Statistical tools such as structural decision modelling (SEM) could be possibly used to address the same research question but may require huge amount of data.
Nevertheless, using the proposed approach, the decision model provides the content of the sustainable manufacturing strategy. It shows that the content strategy is inclined toward process centred technology, big, product life cycle stages-focused facilities which are close to suppliers, following capacity strategy, a horizontal integration, first-line reporting with functional or geographical organizational structure, 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 with pay based on seniority of quality/quantity of output and security focused on training or promotion. These results could guide practitioners in high level policy-making, resource allocation, strategic goal setting, process and product development, prioritization-related decision-making and in the development of programs and initiatives that address the triple-bottom line, i.e. economic, environmental and social dimensions. The content of the sustainable manufacturing strategy is expected to address both competitiveness and sustainability in manufacturing.
136
Advances in Production Engineering & Management 10(3) 2015
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
Acknowledgement
L. Ocampo recognizes the Ph.D. dissertation financial support from the Engineering Research and Development for
Technology (ERDT) scholarship of the Department of Science and Technology, Philippines which made this work
possible.
References
[1]	Skinner, W. (1969). Manufacturing - missing link in corporate strategy, Harvard Business Review, Vol. 47, No. 3, 136-145.
[2]	Wheelwright, S.C. (1984). Manufacturing strategy: Defining the missing link, Strategic Management Journal, Vol. 5, No. 1, 77-91, doi: 10.1002/smi.4250050106.
[3]	Fine, C.H., Hax, A.C. (1985). Manufacturing strategy: A methodology and an illustration, Interfaces, Vol. 15, No. 6, 28-46, doi: 10.1287/inte.15.6.28.
[4]	Hayes, R.H., Pisano, G.P. (1996). Manufacturing strategy: At the intersection of two paradigm shifts, Production and Operations Management, Vol. 5, No. 1, 25-41, doi: 10.1111/i.1937-5956.1996.tb00383.x.
[5]	Paloviita, A., Luoma-aho, V. (2010). Recognizing definitive stakeholders in corporate environmental management, Management Research Review, Vol. 33, No. 4, 306-316, doi: 10.1108/01409171011030435.
[6]	Kassinis, G., Vafeas, N. (2006). Stakeholder pressures and environmental performance, Academy of Management Journal, Vol. 49, No. 1, 145-159, doi: 10.5465/AMI.2006.20785799.
[7]	Baden, D.A., Harwood, I.A., Woodward, D.G. (2009). The effect of buyer pressure on suppliers in SMEs to demonstrate CSR practices: An added incentive or counter productive?, European Management Journal, Vol. 27, No. 6, 429-441, doi: 10.1016/i.emi.2008.10.004.
[8]	Ocampo, L.A., Clark, E.E. (2014). Developing a framework for sustainable manufacturing strategies selection, DLSU Business & Economics Review, Vol. 23, No. 2, 115-131.
[9]	Pham, D.T., Thomas, A.J. (2012). Fit manufacturing: A framework for sustainability, Journal of Manufacturing Technology Management, Vol. 23, No. 1, 103-123, doi: 10.1108/17410381211196311.
[10]	Ocampo, L.A., Clark, E.E., Tanudtanud, K.V.G. (2015). Structural decisions of sustainable manufacturing strategy with fuzzy analytic network process (FANP), International Journal of Strategic Decision Sciences, Vol. 6, No. 2, 1227, doi: 10.4018/iisds.2015040102.
[11]	Ocampo, L.A., Clark, E.E., Tanudtanud, K.V.G. (2015). A sustainable manufacturing strategy from different strategic responses under uncertainty, Journal of Industrial Engineering, Vol. 2015, 1-11, doi: 10.1155/2015/210568.
[12]	Tseng, M.L., Chiu, A.S.F. (2013). Evaluating firm's green supply chain management in linguistic preference,Journal of Cleaner Production, Vol. 40, 22-31, doi: 10.1016/i.iclepro.2010.08.007.
[13]	Saaty, T.L. (2001). Decision making with dependence and feedback: The Analytic Network Process, RWS Publications, Pittsburgh.
[14]	Ocampo, L.A., Clark, E.E. (2014). A comprehensive evaluation of sustainable manufacturing programs using Analytic Network Process (ANP), Multiple Criteria Decision Making, Vol. 9, 101-122.
[15]	Ocampo, L.A. (2015). A hierarchical framework for index computation in sustainable manufacturing, Advances in Production Engineering & Management, Vol. 10, No. 1, 40-50, doi: 10.14743/apem2015.1.191.
[16]	Pan, R., Zhang, W., Yang, S., Xiao, Y. (2014). A state entropy model integrated with BSC and ANP for supplier evaluation and selection, International Journal of Simulation Modelling, Vol. 13, No. 3, 348-363, doi: 10.2507/ iisimm13(3)co13.
[17]	Hayes, R.H., Wheelwright, S.C. (1984). Restoring our competitive edge: Competing through manufacturing, John Wiley & Sons, New York.
[18]	Marucheck, A., Pannesi, R., Anderson, C. (1990). An exploratory study of the manufacturing strategy process in practice, Journal of Operations Management, Vol. 9, No. 1, 101-123, doi: 10.1016/0272-6963(90)90148-7.
[19]	Platts, K.W., Mills, J.F., Bourne, M.C., Neely, A.D., Richards, A.H., Gregory, M.J. (1998). Testing manufacturing strategy formulation processes, International Journal of Production Economics, Vol. 56-57, 517-523, doi: 10.1016/ S0925-5273(97)00134-5.
[20]	Hallgren, M., Olhager, J. (2006). Quantification in manufacturing strategy: A methodology and illustration, International Journal of Production Economics, Vol. 104, No. 1, 113-124, doi: 10.1016/i.iipe.2005.09.004.
[21]	Hayes, R.H., Pisano, G.P. (1994). Beyond world-class: The new manufacturing strategy, Harvard Business Review, Vol. 72, No. 1, 77-86.
[22]	Ward, P.T., Bickford, D.J., Leong, G.K. (1996). Configurations of manufacturing strategy, business strategy, environment and structure, Journal of Management, Vol. 22, No. 4, 597-626, doi: 10.1177/014920639602200404.
[23]	Mani, M., Madan, J., Lee, J.H., Lyons, K., Gupta, S.K. (2012). Characterizing sustainability for manufacturing performance assessment, In: Proceedings of the ASME 2012 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Chicago, Illinois, USA, 1-10.
[24]	Rashid, A., Asif, F.M.A., Krainik, P., Nicolescu, C.M. (2013). Resource conservative manufacturing: An essential change in business and technology paradigm for sustainable manufacturing, Journal on Cleaner Production, Vol. 57, 166-177, doi: 10.1016/i.iclepro.2013.06.012.
[25]	Hassine, H., Barkallah, M., Bellacicco, A., Louati, J., Riviere, A., Haddar, M. (2015). Multi objective optimization for sustainable manufacturing, application in turning, International Journal of Simulation Modelling, Vol. 14, No. 1, 98-109, doi: 10.2507/iisimm14(1)9.292.
Advances in Production Engineering & Management 10(3) 2015
137
Ocampo, Clark, Tanudtanud, Ocampo, Impas, Vergara, Pastoril, Tordillo
[26]	Joung, C.B., Carrell, J., Sarkar, P., Feng, S.C. (2013). Categorization of indicators for sustainable manufacturing, Ecological Indicators, Vol. 24, 148-157, doi: 10.1016/j.ecolind.2012.05.030.
[27]	Kovac, M. (2012). Comparison of foresights in the manufacturing research, TransferInovácií, Vol. 23, 284-288.
[28]	Tsai, W.H., Chou, W.C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP, Expert Systems with Applications, Vol. 36, No. 2, Part 1, 14441458, doi: 10.1016/i.eswa.2007.11.058.
[29]	Seuring, S., Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management, Journal of Cleaner Production, Vol. 16, No. 15, 1699-1710, doi: 10.1016/i.iclepro.2008.04.020.
[30]	Adams, C.A., Frost, G.R. (2008). Integrating sustainability reporting into management practices, Accounting Forum, Vol. 32, No. 4, 288-302, doi: 10.1016/i.accfor.2008.05.002.
[31]	Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business, Capstone Publishing, Oxford.
[32]	Placet, M., Anderson, R., Fowler, K.M. (2005). Strategies for sustainability, Research Technology Management, Vol. 48, No. 5, 32-41.
[33]	Baumgartner, R.J., Ebner, D. (2010). Corporate sustainability strategies: Sustainability profiles and maturity levels, Sustainable Development, Vol. 18, No. 2, 76-89, doi: 10.1002/sd.447.
[34]	Kashmanian, R.M., Wells, R.P., Keenan, C. (2011). Corporate environmental sustainability strategy: Key elements, Journal of Corporate Citizenship, Vol. 2011, No. 44, 107-130, doi: 10.9774/GLEAF.4700.2011.wi.00008.
[35]	Danciu, V. (2013). The sustainable company: New challenges and strategies for more sustainability, Theoretical and Applied Economics, Vol. 20, No. 9, 7-26.
[36]	Ageron, B., Gunasekaran, A., Spalanzani, A. (2012). Sustainable supply chain management: An empirical study, International Journal of Production Economics, Vol. 140, No. 1, 168-182, doi: 10.1016/i.iipe.2011.04.007.
[37]	Gimenez, C., Sierra, V., Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line, International Journal of Production Economics, Vol. 140, No. 1, 149-159, doi: 10.1016/i.iipe.2012.01.035.
[38]	Yuan, C., Zhai, Q., Dornfeld, D. (2012). A three dimensional system approach for environmentally sustainable manufacturing, CIRP Annals - Manufacturing Technology, Vol. 61, No. 1, 39-42, doi: 10.1016/i.cirp.2012.03.105.
[39]	De Brucker, K., Macharis, C., Verbeke, A. (2013). Multi-criteria analysis and the resolution of sustainable development dilemmas: A stakeholder management approach, European Journal of Operational Research, Vol. 224, No. 1, 122-131, doi: 10.1016/i.eior.2012.02.021.
[40]	Kronenberg, J., Bergier, T. (2012). Sustainable development in a transition economy: Business case studies from Poland, Journal of Cleaner Production, Vol. 26, 18-27, doi: 10.1016/i.iclepro.2011.12.010.
[41]	Matos, S., Silvestre, B.S. (2013). Managing stakeholder relations when developing sustainable business models: The case of the Brazilian energy sector, Journal of Cleaner Production, Vol. 45, 61-73, doi: 10.1016/i.iclepro. 2012.04.023.
[42]	Griffiths, A., Petrick, J.A. (2001). Corporate architectures for sustainability, International Journal of Operations & Production Management, Vol. 21, No. 12, 1573-1585, doi: 10.1108/01443570110410919.
[43]	Johansson, G., Winroth, M. (2010). Introducing environmental concern in manufacturing strategies: Implications for the decision criteria, Management Research Review, Vol. 33, No. 9, 877-899, doi: 10.1108/01409171011 070305.
[44]	Theyel, G., Hofmann, K. (2012). Stakeholder relations and sustainability practices of US small and medium-sized manufacturers, Management Research Review, Vol. 35, No. 12, 1110-1133, doi: 10.1108/01409171211281255.
[45]	Perrini, F., Tencati, A. (2006). Sustainability and stakeholder management: The need for new corporate performance evaluation and reporting systems, Business Strategy and the Environment, Vol. 15, No. 5, 296-308, doi: 10.1002/bse.538.
[46]	Clemens, B., Bakstran, L. (2010). A framework of theoretical lenses and strategic purposes to describe relationships among firm environmental strategy, financial performance, and environmental performance, Management Research Review, Vol. 33, No. 4, 393-405, doi: 10.1108/01409171011030480.
[47]	De Brito, M.P., Carbone, V., Blanquart, C.M. (2008). Towards a sustainable fashion retail supply chain in Europe: Organization and performance, International Journal of Production Economics, Vol. 114, No. 2, 534-553, doi: 10.1016/i.iipe.2007.06.012.
[48]	Cheung, M.S., Myers, M.B., Mentzer, J.T. (2011). The value of relational learning in global buyer-supplier exchanges: A dyadic perspective and test of the pie-sharing premise, Strategic Management Journal, Vol. 32, No. 10, 1061-1082, doi: 10.1002/smi.926.
[49]	Harrison, J.S., Bosse, D.A., Phillips, R.A. (2010). Managing for stakeholders, stakeholder utility functions, and competitive advantage, Strategic Management Journal, Vol. 31, No. 1, 58-74, doi: 10.1002/smi.801.
[50]	Zadeh, L.A. (1965). Fuzzy set, Information and Control, Vol. 18, No. 3, 338-353, doi: 10.1016/S0019-9958(65) 90241-X.
[51]	Promentilla, M.A.B., Furuichi, T., Ishii, K., Tanikawa, N. (2008). A fuzzy analytic network process for multi-criteria evaluation of contaminated site remedial countermeasures, Journal of Environmental Management, Vol. 88, No. 3, 479-495, doi: 10.1016/i.ienvman.2007.03.013.
[52]	Tseng, M.L. (2009). A causal and effect decision-making model of service quality expectation using grey-fuzzy DEMATEL approach, Expert Systems with Applications, Vol. 36, No. 4, 7738-7748., doi: 10.1016/i.eswa.2008. 09.011.
[53]	Wang, R.C., Chuu, S.J. (2004). Group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a manufacturing system, European Journal of Operational Research, Vol. 154, No. 3, 563-572, doi: 10.1016/S0377-2217(02)00729-4.
138
Advances in Production Engineering & Management 10(3) 2015
An integrated sustainable manufacturing strategy framework using fuzzy analytic network process
[54]	Von Altrock, C. (1996). Practical fuzzy-logic design, The Computer Applications Journal, Vol. 75, 1-5.
[55]	Wang, C.H., Lu, I.Y., Chen, C.B. (2008). Evaluating firm technological innovation capability under uncertainty, Technovation, Vol. 28, No. 6, 349-363, doi: 10.1016/i.technovation.2007.10.007.
[56]	Ocampo, L.A., Clark, E.E. (2014). A framework for capturing uncertainty of group decision-making in the context of the AHP/ANP, Advances in Industrial Engineering and Management, Vol. 3, No. 3, 7-16, doi: 10.7508/AIEM-V3-N3-7-16.
[57]	Opricovic, S., Tzeng, G.H. (2003). Defuzzification within a multi-criteria decision model, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 11, No. 5, 635-652, doi: 10.1142/S0218488503 002387.
[58]	Tseng, M.L., Lin, Y.H., Chiu, A.S.F., Liao, J.C.H. (2008). Using FANP approach on selection of competitive priorities based on cleaner production implementation: A case study in PCB manufacturer, Taiwan, Clean Technologies and Environmental Policy, Vol. 10, No. 1, 17-29, doi: 10.1007/s10098-007-0109-4.
[59]	Saaty, T.L. (2013). The analytic hierarchy process without the theory of Oskar Perron, International Journal of the Analytic Hierarchy Process, Vol. 5, No. 2, 268-293, doi: 10.13033/iiahp.v5i2.191.
[60]	Saaty, T.L. (1980). The Analytic Hierarchy Process, McGraw-Hill, New York, USA.
[61]	Smith, L., Ball, P. (2012). Steps towards sustainable manufacturing through modelling material, energy and waste flows, International Journal of Production Economics, Vol. 140, No. 1, 227-238, doi: 10.1016/i.iipe.2012. 01.036.
[62]	Chiacchio, M.S. (2011). Early impact assessment for sustainable development of enabling technologies, Total Quality Management and Excellence, Vol. 39, No. 3, 1-6.
Advances in Production Engineering & Management 10(3) 2015
139