Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP)
Ocampo L, Vergara VG, Impas C, Tordillo JA, Pastoril J
ISSN 1339-2972 (On-line)
Identifying Critical Indicators In Sustainable Manufacturing Using Analytic Hierarchy Process (AHP) Lanndon A. Ocampo1 - Van Gaitano N. Vergara2 – Carlito G. Impas, Sr.3 – Jose Arvin S. Tordillo4 – Joey S. Pastoril5 1
Department of Mechanical Engineering, University of San Carlos , Email:
[email protected] Department of Mechanical Engineering, University of San Carlos, Email:
[email protected] Department of Mechanical Engineering, University of San Carlos, Email:
[email protected] 4 Department of Mechanical Engineering, University of San Carlos, Email:
[email protected] 5 Department of Mechanical Engineering, University of San Carlos, Email:
[email protected] 2 3
Keywords
Abstract
sustainable manufacturing sustainability indicators sustainability assessment triple-bottom line Analytic Hierarchy Process
This paper identifies critical indicators for sustainable manufacturing that could be adopted in sustainability assessment at firm level. Previous works in literature suffered from either lack of being comprehensive or being operational or both. The manufacturing indicator set of the US NIST framework was used because of its detailed and wide-ranging exposition of the triple-bottom line. The proposed approach is to attach priorities on the elements of the NIST framework in order to prioritize sustainability indicators. Following the hierarchical structure of the framework, analytic hierarchy process (AHP) was adopted in this work. Three domain decision-makers who have sufficient international exposure on manufacturing policy development and experience in managing manufacturing firms were asked to elucidate judgments on the elements of the framework based on the context of the AHP. The most critical indicators in sustainable manufacturing are presented in this work. Results show that socio-economic indicators are highly relevant in sustainable manufacturing.
Article
History
Received 26 December 2014 | Revised 17 March 2015 | Accepted 27 April 2015
Category
Original Scientific Paper
Citation
Ocampo LA, Vergara VGN, Impas CG, Tordillo JAS, Pastoril JS (2015) Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP). Journal of Manufacturing and Industrial Engineering, 14(3-4):1-8, http://dx.doi.org/10.12776/mie.v14i3-4.444
INTRODUCTION Current and emerging issues of sustainability have become the motivations of various government institutions, legislative bodies, industries, corporations and businesses in policy-making and strategic decision-making. These issues have radically changed the way how decisions, which were previously focused on economic returns especially in developing countries [1], must be made such that environmental and social issues are simultaneously addressed [2]. However, the vagueness associated with these issues has been overwhelming not to mention the difficulty of developing their measurement systems. For instance, manufacturing industry, being a significant key to sustainable development [3], has coined the term ‘sustainable manufacturing’ to emphasize the manufacture of products with processes that conform to the demands of sustainability [4]. These prompted manufacturing firms to adopt initiatives such as cleaner production, life cycle assessment (LCA), design for environment, environmentally conscious manufacturing, and green technology toward approaches in larger scales such as sustainable supply chain and industrial symbiosis. Two important questions are relevant: (i) Given limited resources, what set of initiatives do manufacturing firms implement to maximize sustainability status? (ii) After adopting these approaches, how is sustainability evaluated? These require holistic sustainability assessment approaches. One particularly famous approach in literature is by the use of sustainability indicators. These indicators can capture the three dimensions of sustainability, i.e. environmental stewardship, economic growth and social well-being, on various levels and at different resolutions, e.g. companies, facilities, processes and products [5] as well as in larger spatial scales such as economic sectors, cities, etc. At the firm level, the use of sustainability indicators is directly associated with the practice http://dx.doi.org/10.12776/mie.v14i3-4.444
of sustainability reporting [6] following the GRI framework recently proposed in practice. Lodhia and Martin [7] argued that the vital aspect of initiating sustainability reporting process is a more focused set of indicators for organizations. It is also widely pronounced that the use of sustainability indicators is one of the main methods in sustainability evaluation [8] because they provide a quantifiable approach in measuring various aspects of sustainability issues. Various sustainability indicators have been developed by different institutions at different spatial resolutions. See Joung et al. [4] for a review of these indicator sets. Current literature on the other hand has given significant attention on the development of sustainability indicators at firm level in the context of sustainability assessment, e.g. corporate sustainability indicators [7,9], indicators for product service systems [10] and sustainability indicators appropriate at process level [5]. Two underlying criteria have to be balanced out in developing sustainability indicators especially at the firm level: being comprehensive and operational. The works of Rahdari and Rostamy [9] and Lodhia and Martin [7] on sustainability indicators at the corporate level fall short of being comprehensive as social sustainability is scarcely addressed. On the other hand, indicators at the process level proposed by Sundin et al. [10] and Linke et al [11] are limited only in focus. Widely-known sustainability indicators reviewed by Joung et al. [4] were highly comprehensive but the issue of being operational was at stake. Bordt [12] provided a map on widelyknown indicators and argued that the National Institute of Standards and Technology (NIST) framework later presented by Joung et al. [4] is characterized by indicators that are able to address product, process and facilities level. These levels are inclined to address the issues of sustainability in manufacturing 1
Ocampo L, Vergara VG, Impas C, Tordillo JA, Pastoril J
at firm level. However, the large number of indicators listed in the NIST framework prompts difficulty for firms to monitor all of these indicators thus making it hardly operational. To address this issue, this paper aims to prioritize significant sustainability indicators in the NIST framework. This is relevant in offering a set of indicators which is comprehensive and operational. Due to the hierarchical structure of the NIST framework, this work adopts an analytic hierarchy process (AHP) approach proposed by Saaty [13] in attaching priorities to the elements in the structure necessary to identify highly relevant sustainability indicators. The contribution of this work in the literature of sustainable manufacturing is the identification of significant indicators that could be used in sustainability assessment at firm level.
LITERATURE REVIEW SUSTAINABLE MANUFACTURING The foundation of sustainable manufacturing is based from the definition of sustainable development provided by the report of Brundtland [14] claiming that sustainable development both meets the needs of the present and future generations. Formally, sustainable manufacturing is defined 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” [4]. This implies that the design of products and processes and their impact to the stakeholders along the products’ life cycle stages must be consistent with the issues associated with the economy, environment and society – widely-known as the triple-bottom line [15]. Sustainable manufacturing came into the limelight following the global awareness on potential role of manufacturing in sustainable development. It has become a very important issue among industries worldwide [16]. It has been recognized as a critical need because of the increasing consumption of nonrenewable resources, stricter regulations on key environmental issues and occupational health and safety, and the collective consumer preference for environmentally-benign products [17]. For instance, Hassine et al. [18] claimed that manufacturing industries consume around 30% of global energy demand while the same emit around 36% of global carbon dioxide emissions. This pattern of energy consumption entails adverse environmental impact and degradation of natural resources [18]. It is also expected that the five-fold increase of GDP per capita in the next fifty years would imply ten-fold increase in material usage, energy consumption and waste generation [19] and these impacts are closely associated with the manufacturing sector as main users of materials and energy and producers of wastes. Rashid et al. [19] highlighted that the manufacturing sector serves as the “backbone” of the well-being of nations being the leading employment sector and main contributor of global GDP. Sustainable manufacturing gained several interests both in industry and academia for a couple of decades and inspired leading developed economies in terms of research allocation and policy development [20]. Siemieniuch et al. [21] presented a high level discussion on the impact of global drivers in global sustainability and the roles of sustainable manufacturing in mitigating these impacts. The novel argument implies that sustainability requires significant input from ergonomics and human factors in some expansion in the level of thinking. 2
Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP)
To address various concerns on the sustainability of manufacturing, several approaches spanning management and design and engineering of manufactured products and manufacturing processes have emerged in literature such as environmental collaboration, life cycle assessment, sustainable product design, and techniques that address materials, energy and wastes. Development of these approaches has been regarded as a global concern [22]. See Ocampo and Ocampo [23] for this discussion. While these approaches were developed to address a specific aspect of sustainability, assessing their impact at firm level remains a significant issue in current literature. In fact, Ocampo and Clark [24] noted in a case study that the strategies adopted by a manufacturing firm lack a clear direction on which sustainability drivers they attempt to address in the context of the triple bottom-line. Nevertheless, it has been pointed out that firms adopting sustainability initiatives yield better product quality, higher market share, and increased profit and these initiatives are positively related with competitive outcomes [25].
SUSTAINABILITY ASSESSMENT AND INDICATORS Sustainability assessment is defined as “a generic term for a methodology that aims to assist decision-making by identifying, measuring and comparing the social, economic and environmental implications of a project, program, or policy option” [26]. The main goals of sustainability assessment as pointed out by Guijt and Moiseev [27] are the following: (1) an input to strategic planning and decision-making in various institutions, (2) information for monitoring, evaluation and impact analysis of any policy, strategy or initiative, (3) a source for reporting on the state of the environment, and (4) a process to raise awareness about sustainability issues. Several approaches in sustainability assessment are available in literature. The use of value stream mapping (VSM) which was previously used in lean manufacturing has been regarded as a viable tool in sustainability assessment. For instance, Faulkner and Baburdeen [28] introduced sustainable value stream mapping in assessing processes in terms of their environmental and societal impact along with the traditional metrics of lean manufacturing. Brown et al. [29] reported the application of sustainability VSM (Sus-VSM) in three different studies to assess its versatility of its use. Bilge et al. [30] on the other hand, presented an approach in measuring and monitoring a firm's success and sustainability performance by assessing value creation. Garbie [31] proposed an optimization model that minimizes sustainable time and sustainable cost in the context of sustainability indices. Taisch et al. [32] presented a reference model that attempts to support decision-makers in managing manufacturing improvement programs with environmental and social focus such that sustainability becomes a manufacturing strategy. Zhang and Haapala [33] performed sustainability assessment at the production work cell level using a set of measures. A thorough review of sustainability assessments was done by Moldavska and Welo [34] and Lee and Lee [35] proposed a depository of research articles in manufacturing sustainability assessment that allows easy access for decisionmakers. Dizdaroglu [36] presented three categories of sustainability assessment and noted that indicator-based sustainability assessment is increasingly recognized as a useful tool in the planning process by determining the state of local sustainability, thus making sustainability measurable and manageable, http://dx.doi.org/10.12776/mie.v14i3-4.444
Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP)
providing feedback on the progress during the implementation stage of sustainable development, and identifying the advantages and disadvantages of different alternatives to help determine win–win situations. Joung et al. [4] defined an indicator as “as a measure or an aggregation of measures from which conclusions on the phenomenon of interest can be inferred”. Use of indicators is one category of sustainability assessment tools and techniques [37]. They are useful in tracking progress of sustainability over time, identifying problems for improvement, and identifying issues that may be overlooked from previous analysis [12]. They are considered as more holistic measurement and important for comprehensive firm valuations [38]. Sustainability indicators have been widely used in sustainability assessment at different spatial resolutions. For instance, Zhou et al. [39] introduced a methodology in identifying sustainable urbanization indicators by determining departments in a city who assume responsibilities over key areas while reviews on urban sustainability indicators were performed separately by Braulio-Gonzalo [40] and by Michael et al. [41]. Domingues et al. [42] developed a conceptual framework to define a sustainability label for local public services by adopting the criteria of EU Ecolabel initiative for environmental aspects and the criteria of Global Reporting Initiative (GRI) for other sustainability aspects. Agol et al. [43] explored the challenges and opportunities in the application of sustainability indicators for impact evaluation of development and conservation projects in developing countries. Dizdaroglu [36] proposed micro-level urban ecosystem indicators for monitoring the sustainability of residential developments. Since development of these indicators are quite expensive, Rivera et al. [44] interestingly developed text mining framework to identify sustainability indicators from digital news articles with a baseline sustainability reports in a particular locality. Current literature on the other hand has given ample attention on the development of sustainability indicators at firm level in the context of sustainability assessment. With limited resources, sustainability indicators provide firms an approach for analyzing sustainability. Firms can assess their actual situation with the indicators, raise their awareness and set their goals [45]. Rahdari and Rostamy [9] developed a general set of sustainability indicators at the corporate level. Particularly applicable in small and medium enterprises (SMEs), Tan et al. [46] developed a 40-indicator framework from internationallyknown indicator frameworks. Lodhia and Martin [7] developed company specific corporate sustainability indicators (CSI) and investigated their practical application and appropriateness to a certain company and its stakeholders. Sundin et al. [10] identified a set of indicators appropriate in developing product service systems (PSS). These indicators were then used in sustainability assessment or as a part of firm’s measurement system. For example, Nappi and Rozenfeld [47] attempted to incorporate sustainability indicators in firms’ performance measurement system. On higher resolution, Linke et al. [11] identified relevant sustainability indicators and used them in assessing sustainability in process level and particularly in finishing operations based on process performance and part quality [5]. The strength of sustainability indicators lies in providing better simplification, quantification, analysis and communicating information from the perspectives of the triple bottom-line. Rahdari and Rostamy [9] emphasized that various studied have tried to develop industry specific sets of sustainability indicators. The general contention in current http://dx.doi.org/10.12776/mie.v14i3-4.444
Ocampo L, Vergara VG, Impas C, Tordillo JA, Pastoril J
literature is that these indicators must be integrated in industryspecific processes order to provide an understanding of the linkage between the triple bottom-line such that a holistic evaluation of sustainability issues is achieved [7]. With this, a number of indicator sets of sustainability have been proposed by international institutions, individual companies and private institutions. A review of these indicator sets was done by Joung et al. [4]. However, identifying a suitable set of sustainability indicators remains a main challenge [46]. There are issues associated with its use. For example, Lehtonen et al. [48] presented trade-offs and ambiguities in relation to the use and development of sustainable development indicators in policy processes. With this number of indicator sets, Bordt [12] described a map of these sets in terms of their technical details and application domain as shown in Fig 1.
Figure 1 A map of different indicator sets showing technical details against application domains
Fig 1 shows that the US National Institute of Standards and Technology (NIST) framework has high technical details that are applicable to product and process applications. These domains constitute the core of sustainable manufacturing as described in its definition. This makes the framework more appealing and appropriate to manufacturing at firm level. The complete indicator set of the NIST framework was published by Joung et [4]. The strength of this framework is in its hierarchical representation of the triple bottom-line with significant breadth and depth. The framework is the result of a careful integration of eleven widely known indicators with a total of 212 indicators: 77 from environmental stewardship, 23 for economic growth, and 70 for social well-being dimension, 30 for performance management and 12 for technological advancement management. The repository of these indicators is found in NIST’s Sustainable Manufacturing Indicator Repository (SMIR) website. This framework is used in current literature [2,49-50]. However, Roca and Searcy [51] noted that few studies have focused on developing general sets of sustainability indicators. While generality of sets is important, Sundin et al. [10] emphasized that indicators must be adjusted to each company’s need and to limit the number of indicators as less as possible to minimize extra work required in evaluating and monitoring them. Thus, the challenge now is to identify critical indicators of sustainable manufacturing without losing too much on the generality of these indicators. This is significant as it determines the limited number of indicators that firms must monitor in order to promote better sustainability. This supports resource allocation decisions and policy development at firm level. The approach promoted in this paper is to identify the critical indicators identified by the NIST framework.
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Ocampo L, Vergara VG, Impas C, Tordillo JA, Pastoril J
SUSTAINABLE
INDICATORS HIERARCHY PROCESS (AHP)
PRIORITIZATION
Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP)
AND
ANALYTIC
Sustainability indicators are based on measured and/or estimated data that must undergo normalization, scaling and aggregation [52]. The aggregation process is usually carried out by computing an index – a single score that represents the aggregation of indicators using weight-based mathematical methods [4]. This is used as an overall performance of a process, product or a firm. Examples in literature include a product sustainability index [53], product sustainability assessment using a weight fuzzy assessment method [54], a sustainable manufacturing index at firm level [49]. This list is not intended to be comprehensive. The normalization and scaling processes involve placing a relative weight to each sustainability indicator. Following the hierarchical structure of the NIST framework, the use of analytic hierarchy process (AHP) is found to be more appropriate. AHP is a powerful tool in multi-criteria decision-making (MCDM) in which the decision problem is described in a hierarchy. Central to the theory of AHP is the pairwise comparisons approach [13] where decision-makers elicit judgment by comparing any two elements with respect to a higher, immediate element in the hierarchy. The strength of AHP is in capturing subjective judgments of decision-makers and integrating them in the decision-making process. AHP is widely used in sustainability assessment. Jawahir et al. [55] applied AHP to assess product sustainability using life cycle approach. Sudarsan et al. [56] emphasized the use AHP in manufacturing sustainability assessment. Ziout et al. [1] implemented a sustainability assessment of manufacturing system reuse from a point of view of the triple-bottom line using AHP. Amrina and Vilsi [16] proposed a set of key performance indicators and use AHP to prioritize these indicators. The gap identified in this study is the identification of sustainability indicators that are critical in sustainable manufacturing. This work attempts to apply AHP in normalizing and scaling sustainability indicators identified by Joung et al. [4]. The NIST framework of Joung et al. [4] provides a comprehensive set of sustainability indicators while AHP prioritizes critical indicators as input to product and process design, strategy development and policy-making.
METHODOLOGY ANALYTIC HIERARCHY PROCESS Generally, the procedure of AHP can be described as follows: 1. Structuring the decision problem In AHP, decision problems are structured hierarchically in a topdown approach [13]. Oftentimes in many selection problems, there is an explicit definition and representation of goal, criteria and alternatives components. In various cases, criteria component is described in more than two levels so that further details of criteria are explicitly presented in the decision structure. The decision of the inclusion of components and alternatives is usually carried out either through a critical review of literature with regard to the facets of the decision problem or through an expert or group of experts who have sufficient 4
knowledge and experience of the problem under consideration. Decision components and elements are usually a combination of both objective and subjective ones, with measurements in different and multiple dimensions. 2. Eliciting judgment in paired comparisons Through experts’ knowledge, pairwise comparisons of elements in the same level with respect to an element in the immediate higher level are carried out in the AHP. The generic question in making pairwise comparisons goes like this: “Given a parent element and given a pair of elements, how much more does a given member of the pair dominate other member of the pair with respect to a parent element?” [57]. To achieve a unidimensional scaling property of the comparisons, Saaty [13] established the famous Saaty fundamental 9-point scale as shown in Table 1. Table 1 Saaty fundamental scale Rate Definition Explanation 1 Equal importance Two elements contribute equally to the objective 2 Weak between equal and moderate 3 Moderate Experience and judgment slightly importance favor one element over another 4 Moderate plus between moderate and strong 5 Strong importance Experience and judgment strongly favor one element over another 6 Strong plus between strong and very strong 7 Very strong or An element is favored very strongly demonstrated over another; its dominance importance demonstrated in practice 8 Very, very strong between very strong and extreme 9 Extreme The evidence favoring one element importance over another is one of the highest possible order or affirmation
Suppose, aijk represents the decision of k th decision-maker on the influence of element i on j . To aggregate individual judgments, Saaty [13] proposed the weighted geometric mean method (WGMM) as shown in (1):
k
aij aijk k
(1)
wihere aij iss the aggregated judgment, k is the decisionmaker’s importance to the decision making process with k 0 and k 1 . The values of aij i , j form the pairwise k comparisons matrix. In pairwise comparisons, reciprocity is maintained. Priority vectors (w) are obtained from the pairwise comparison matrix (A) by solving an eigenvalue problem in the following equation: Awmax w
(2)
where max is the maximum eigenvalue of the positive reciprocal square matrix (A). The approach also provides a way to measure the consistency of judgments in the pairwise comparison matrix. When decision-making in the pairwise comparisons matrix is consistent max n ; otherwise, max n http://dx.doi.org/10.12776/mie.v14i3-4.444
Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP)
where n is the number of elements being compared. The Consistency Index (CI), as a measure of degree of consistency, was calculated using the formula
CI max n / n1
(3)
The consistency ratio (CR) is computed as
CRCI / RI
(4)
where RI is the mean random consistency. Acceptable CR values must be less than 0.1. Decision-makers were asked to repeat the pairwise comparisons for CR values greater than 0.1. 3.
Ocampo L, Vergara VG, Impas C, Tordillo JA, Pastoril J
all parents for each element in the lower level. This is referred to as the distributive mode of the AHP. This can be represented in the form for two levels in a hierarchy
T T T WT Xm 3 X m 2 I X m1I
(5)
where W is is the global (synthesized) weight vector of the elements in the lowest (or third level in this case), X m3 is the local priority vector of the third level elements (the lowest level), X m 2 is the local priority vector of the second level elements, X m1 is the local priority vector of the first level elements, and I is an identity matrix.
Synthesizing judgments
Saaty [13] described that synthesizing judgments in AHP is done by weighting the elements being compared in the lower level to an element in the next immediate level, referred to as the parent element, by the priority of that element and adding
Figure 2 Sustainable manufacturing hierarchal structure
PROCEDURE The procedure adopted in this paper is as follows: 1. Use the hierarchical structure of the NIST framework on sustainable manufacturing. Instead of five dimensions as shown in Joung et al. [4], this work adopts the first three dimensions which are consistent with the triple-bottom line. The hierarchical structure is presented in Fig. 2 which is composed of four levels denoted as level 0, 1, 2, 3. Each element of level 3 element is composed of sustainability indicators. See SMIR website for these indicators. 2. Construct the pairwise comparisons matrices of the level 3 elements with respect to their level 2 parent element, level 2 elements with respect to their level 1 parent element, and level 1 elements with respect to their level 0 parent element. See Saaty [13] for this discussion. 3. Decision-makers elicit judgments on the pairwise comparisons matrices. Judgments are based on the Saaty 9http://dx.doi.org/10.12776/mie.v14i3-4.444
4.
5. 6.
7.
point fundamental scale shown in Table 1. In this study, three experts were asked to perform pairwise comparisons. These experts were selected based on their expertise in manufacturing and sustainable development projects. Aggregate judgments from three decision-makers using (1). These form single decision-maker pairwise comparisons matrices. Compute for the local eigenvectors using (2) along with CR values using (4). Synthesize judgments using (5) and this yields a global priority vector of level 3 elements. Rank these elements in decreasing order and obtain the top 10 elements. List down the sustainability indicators associated with the top 10 level 3 elements. These are considered as critical elements in sustainable manufacturing. These sustainability indicators must be given high priority by manufacturing firms.
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Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP)
RESULTS AND DISCUSSION Three sets of pairwise comparisons were performed by decision-makers: (type 1) level 3 elements with respect to level 2 parent element, (type 2) level 2 elements with respect to level 1 parent element, and (type 3) level 1 elements with respect to level 0 parent element. These sum up to 14 pairwise comparisons matrices. Table 2 shows a sample of type 1 pairwise comparison matrices by a single decision-maker. Table 3 shows a sample of type 2 and Table 4 shows a sample of type 3. Table 2 Sample type 1 pairwise comparisons matrix Pollution (a1) (a2) (a1) Toxic substances 1 (a2) Greenhouse gas emissions (a3) Ozone depletion gas emissions (a4) Noise (a5) Acidification substance
(a3) 3 3
Table 3 Sample type 2 pairwise comparisons matrix Environmental stewardship (a1) (a2) (a1) Pollution 1 (a2) Emission (a3) Resource consumption (a4) Natural habitat conservation
(a4) 5 5 2
(a3)
(a4) 3 3
Table 4 Sample type 3 pairwise comparisons matrix Sustainable manufacturing (a1) (a2) (a1) Environmental stewardship 1/2 (a2) Economic growth (a3) Social well-being
(a5) 3 3 1 1/2
2 2 2
(a3) 1/2 1
In aggregating judgments of three decision-makers, (1) is used with 10.5 , 2 0.3 and 3 0.2 . When the aggregated pairwise comparisons matrix yield CR0.10 , then 10.7 ,
2 0.2 and 3 0.2 . Table 5 shows a sample of aggregated pairwise comparisons matrix of type 1. Table 6 shows a sample of type 2 and Table 7 shows a sample of type 3. Local priority vectors and CR values were presented in these samples. Table 5 Sample aggregated type 1 pairwise comparisons matrix Pollution
(a1)
(a1) Toxic substances (a2) Greenhouse gas emissions (a3) Ozone depletion gas emissions (a4) Noise (a5) Acidification substance
(a2)
(a3)
(a4)
(a5)
1.516
1.620
3.389
2.625
Priority vector 0.3294
1.407
1.816
4.899
0.2842
1.219
1.196
0.1624
0.922
0.1167 0.1072
CR0.036
Table 6 Sample aggregated type 2 pairwise comparisons matrix Environmental stewardship (a1) Pollution (a2) Emission (a3) Resource consumption (a4) Natural habitat conservation
(a1)
(a2)
(a3)
(a4)
1.817
2.269 2.269
1.853 1.853 1.288
Priority vector 0.3881 0.2875 0.1642 0.1603
Table 7 Sample aggregated type 3 pairwise comparisons matrix Sustainable manufacturing (a1) Environmental stewardship (a2) Economic growth (a3) Social well-being
(a1)
(a2)
(a3)
0.248
0.296 2.392
Priority vector 0.1140 0.5801 0.3059
CR 0.052 Using (5) to compute for the global priority vector of level 3 elements, Table 8 presents the list of level 3 elements with their corresponding global priorities.
Table 8 Global priority vector of level 3 elements Level 3 elements
Priorities
Rank
Toxic substance Greenhouse gas emissions Ozone depletion gas emissions Noise Acidification substance Effluent Air emissions Solid waste emissions Waste energy emissions Water consumption Material consumption Energy/electrical consumption Land use Biodiversity management Natural habitat quality Habitat management Revenue Profit Materials acquisition Production Product transfer to customer End-of-service-life product handling Research and development Community development Employees health and safety Employees career development Employee satisfaction Health and safety impacts from manufacturing/product use Customer satisfaction from operations and products Inclusion of specific rights to customer Product responsibility Justice/equity Community development programs
0.014578 0.012578 0.007187 0.005165 0.004741 0.009651 0.013685 0.007213 0.002234 0.003960 0.003036 0.006350 0.005373 0.006713 0.006156 0.005405 0.123208 0.112194 0.098543 0.058263 0.032955 0.050722 0.040135 0.064100 0.033757 0.034856 0.021455 0.045944 0.046978 0.027960 0.027508 0.029352 0.038046
18 20 23 29 30 21 19 22 33 31 32 25 28 24 26 27 1 2 3 5 13 6 9 4 12 11 17 8 7 15 16 14 10
Table 8 shows that the top 10 level 3 elements are concentrated in economic growth and social well-being dimensions. This shows that socio-economic issues are highly relevant in sustainable manufacturing. Environmental stewardship elements can be treated as consequences in addressing socio-economic issues. For instance, addressing health and safety impacts from manufacturing/product use in the social well-being dimension implies eliminating or reducing the use of toxic substance in the environmental stewardship dimension. From this priority listing of level 3 elements, top 10 elements were obtained and their indicators were listed in Table 8. These indicators were based from the work of Joung et al. (2013). Table 9 presents the critical sustainability indicators firms must monitor in order to promote sustainability efficiently and comprehensively.
CR 0.027
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Identifying critical indicators in sustainable manufacturing using analytic hierarchy process (AHP)
Ocampo L, Vergara VG, Impas C, Tordillo JA, Pastoril J
[2] Table 9 Critical sustainability indicators Level 3 elements Revenue Profit Materials acquisition Community development
Production
Rank 1 2 3 4
5
End-of-service-life product handling
6
Customer satisfaction from operations and products
7
Health and safety impacts from manufacturing/product use
8
Research and development
Community development programs
9
10
Sustainability indicators Revenue Profit Material acquisition costs Charitable investments Investments and impacts of community development Energy costs Tooling costs Labor costs Waste treatment costs Packaging costs Recycling costs for WEEE (labor) productivity Customer satisfaction assessment Customer complaints Life cycle assessment for health and safety impacts Incidents of non-compliance with voluntary codes Product quality assurance and management Innovation and research and development investments Renewable energies investment Energy efficiency investments Programs for adherence to laws Violations of human rights Public service management Participation in public policy development Political contributions Responsible care program participation Sustainability report publishing Population density Population growth
[3]
[4] [5] [6] [7] [8] [9] [10]
[11]
[12] [13] [14] [15]
CONCLUSION AND FUTURE WORK With analytic hierarchy process, priorities were attached to the elements of the US NIST indicator framework on sustainable manufacturing. From these priorities, critical indicators were identified. These indicators are the most prioritized indicators that may serve as guide in the implementation and evaluation of sustainable manufacturing projects, initiatives and strategies. Practitioners can easily access the method due to its simple analytical procedure. Thus, this paper has provided a methodology in identifying critical elements in sustainable manufacturing. Further work can be extended on this paper by determining appropriate measurement systems for each indicator. Causal relationships of these indicators may be performed to further narrow down the scope of the monitoring list.
[16] [17]
[18]
[19]
[20] [21]
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