Computing sustainable manufacturing index with fuzzy analytic hierarchy process

Abstract

The growing industrial interest in adopting sustainability programmes has ushered in studies regarding sustainability indicators which have continually flourished in current literature. However, limited attention is given to the development of priority ranking, which is an important input for any adopting firm. This paper presents a hybrid multi-criteria approach in determining priority areas in sustainable manufacturing (SM). Using fuzzy analytic hierarchy process to address uncertainty in hierarchical decision-making, this paper determines SM priority strategies and eventually identifies even lower level strategies. The computed sustainable manufacturing index is presented at both the organizational and operational levels for a real case study of an industrial plastic manufacturing firm. This work provides a detailed and transparent hierarchical decision-making approach based on SM framework, the use of which could be valuable to practicing managers across industries in their pursuit of greater sustainability.

Keywords:

• Sustainable manufacturing index
• analytic hierarchy process
• fuzzy set theory
• triple-bottom line
• environment

1. Introduction

The increasing global awareness on sustainability compels manufacturing industries to pursue objectives beyond the dictates of profit; but instead, adopts a more holistic approach that incorporates stakeholders’ well-being (Rosen and Kishawy 2013). This is in line with the triple-bottom line (Elkington 1997), i.e. environmental stewardship, economic growth and social well-being. Consequently, a sustainable manufacturing (SM) framework was elaborated as the “creation of manufactured products using processes that minimize negative environmental impact, conserve energy and natural resources, are safe for employees, communities and consumers, and are economically sound” (International Trade Administration 2007; Joung et al. 2013). Manufacturing sector has a higher potential capability of curbing energy demand and carbon emissions (Nezhad 2009; Mani et al. 2012); as such, it should assume an active role in creating and promoting initiatives that address the demands of sustainability.

Identifying relevant SM approaches that would eventually link manufacturing decisions remains a persistent issue (Ocampo and Clark 2014). A plausible, step-back approach of current literature is to define SM concepts using concrete and operational indicators and then to use these indicators in identifying relevant SM approaches or in developing new ones (Labuschagne, Brent, and van Erck 2005). Thus, the key approach is to determine the priority ranks of indicators that have a relatively high impact on SM. On this approach lies the direction of this work.

Various indicators have been developed by renowned institutions, international agencies and bodies, universities, government and industries (Joung et al. 2013). For example, a review of such indicator sets is carried out in several works, which include that of Böhringer and Jochem (2007), Mayer (2008), Singh et al. (2012) and Joung et al. (2013). These indicator sets or a hybrid of these sets have been used to assess and evaluate sustainability in different domains. To date, the most critical and comprehensive framework of SM indicators is presented in Joung et al. (2013) with results being adopted by the US National Institute of Standards and Technology (US NIST). Despite the use of these indicators, Ocampo and Clark (2014) found out that current strategies of manufacturing firms towards sustainability are somehow fragmented and lamely supportive of each other. This inadvertently leads to inefficient utilization of firms’ resources. Manufacturing industries are then left with the challenge of determining specific indicators that have relatively high impact on sustainability. Such priority identification process is complex as it involves tangible and intangible aspects. More so, the assessments obtained are based on differing value judgements, assumptions and scenarios (Heijungs, Huppes, and Guinee 2010). Due to these considerations, a multi-criteria decision-making (MCDM) approach was considered highly appropriate (Cho 2003; Herva and Roca 2013). Adding to this complexity is the uncertainty inherent in such decision-making process (Tseng, Chiang, and Lan 2009; Tseng, Divinagracia, and Divinagracia 2009). Thus, establishing sustainability priority areas using a holistic methodology is a relevant research question that needs to be addressed. This paper thus attempts to answer this question with the use of fuzzy analytic hierarchy process (FUZAHP) in identifying priority areas that have high relevance to SM. Saaty (1980) developed the analytic hierarchy process (AHP) as an MCDM approach that structures decision problems in a hierarchical structure and then measuring the priority weights of the decision elements based on the value judgement of the decision-maker (Promentilla et al. 2006). On the other hand, fuzzy set theory (FST) developed by Zadeh (1965) can be used to handle the vagueness in decision-making. The use of FST allows one to address judgemental uncertainties (Tseng 2013) brought about by lack of information, the impreciseness of preferred data collection and the inadequate understanding of the problem by decision-makers (Paulson and Zahir 1995).

The objectives of this work are (1) to identify priority areas that have relatively high impact on SM, (2) to demonstrate using a case study the computations necessary to obtain the sustainable manufacturing index (SMI) and (3) to highlight the benefits for manufacturing firms to compute the SMI. The main contribution of this work is the application of a variant of FUZAHP in determining priority ranks of elements that have relatively high impact on SM. A case study of an industrial plastic manufacturing firm is shown in this work.

This paper is organized as follows. Section 2 provides a review of literature on sustainability indicators and current approaches in developing indices. Section 3 presents the proposed FUZAHP method and relevant information for the specific case study. Section 4 presents the results of the study with respect to priority areas and the computation of SMI for an industrial plastic manufacturing company. Section 5 presents the discussion and finally, Section 6 provides the conclusion and recommendations for future work.

2. Literature review

2.1. Sustainability indicator sets

SM indicators gained increasing attention in the literature due to the need to evaluate intangible aspects of a product/process (e.g. community satisfaction, customer welfare, product responsibility). All these indicator sets measure sustainability relevant to the interested user. Frequently used financial indicators are net sales, costs of purchased goods, materials, services, total payroll and benefits. Most environmental indicators include energy and water consumption, carbon dioxide emissions, internal initiatives to improve energy efficiency. Most social dimension indicators include workplace health and safety policies and measures, employee education and skill management and the benefits that employees receive from the organization beyond those that are legally mandated (Roca and Searcy 2012).

With these complex sets, the challenge is to identify indicators or indicator sets that effectively measure performance of SM. Rosen and Kishawy (2013) outlined characteristics of effective SM indicators: (1) relevance, i.e. revealing necessary information about a system or process (2) understandability, i.e. straightforward and readily understood by experts and non-experts and (3) reliability, i.e. providing information that is trustworthy and (4) assessable, i.e. based on available and accessible data. In this respect, Joung et al. (2013) developed a top-down sustainability indicators framework as an integration of 11 renowned indicators sets using a logical selection process. This framework became the “standard set” for the US National Institute for Standards and Technology (SMIR 2011).

The hierarchical top-down framework of Joung et al. (2013) defines SM in five dimensions namely, environmental stewardship, economic growth, social well-being, performance management and technology advancement management. This comprises 212 indicators of which 77 relate to environmental stewardship, 23 to economic growth and 70 to social well-being dimension, 30 to performance management and 12 to technological advancement management (Joung et al. 2013). Aside from the established triple-bottom line, the last two dimensions are included because of the presence of these indicators in the 11 indicator sets. However, Joung et al. (2013) maintained that these two dimensions are embedded in the triple-bottom line although they are not widely accepted in current literature (Jayal et al. 2010). The use of the framework has also been shown in Ocampo (2015) for index computation under certainty.

2.2 Sustainable manufacturing index (SMI)

Gaussin et al. (2013) proposed five major features of an index that include among others, its computational simplicity. There is no established consensus on index aggregation method but Singh et al. (2012) asserted that the method must undergo normalization, weighting and aggregation. These steps were adopted in Krajnc and Glavic (2005) and Jayal et al. (2010) within the context of the AHP to compute for the SMI and the product sustainability index (PSI), respectively. Singh et al. (2012) added that the choice of aggregation model, weighting rules and vagueness are vital in constructing index framework. However, Böhringer and Jochem (2007) contested that there is still no accepted procedure for normalization and weighting. Mayer (2008) critically reviewed common sustainability indices and identified significant issues such as system boundaries, data inclusion, standardization and weighting methods, aggregation methods and inconsistent comparisons across indices. Popp, Hoag, and Hyatt (2001) cautioned that there is a need for managers to study the interaction of these different indices.

There have been recent studies in the development of sustainability-related index. For example, Singh et al. (2007) evaluated the sustainability of steel industry using composite sustainability performance index. Vinodh, Prasanna, and Selvan (2013) proposed the combination of sustainability assessments such as life cycle assessment, risk/benefit worksheet and PSI in evaluating sustainability in all organizational levels, i.e. strategic, tactical and operational. Ngai et al. (2014) developed a platform for corporate sustainability index based on different organizational and behavioural theories. Vimal, Vinodh, and Muralidharan (2015) proposed a process sustainability index using multi-fuzzy grade method based on a comprehensive set of 30 different criteria. Husgafvel et al. (2015) emphasized the application of social sustainability metrics to measure sustainability performance at firm level. These researchers contend that indicators support informed decision-making including corporate-level assessments and reporting initiatives.

2.3 Summary and implications of the literature review

Prioritization of indicators is thus a significant attempt in providing a suitable groundwork in the assessment or development of SM initiatives. Related to this prioritization process is the development of an index that describes sustainability performance at firm level. Note that high-impact indicators were identified from a survey of literature in the areas of sustainability indicators and indices from top journals in the field including the International Journal of Sustainable Engineering. While index computation is widespread in the literature domain, previous works concentrated on PSI and little is known in assessing firm-level performance using a comprehensive model of sustainability indicators. In this work, a prioritization of relevant sustainability areas and an index computation are carried out using sustainability indicators proposed by Joung et al. (2013), and in consistent with the framework used by US NIST (SMIR 2011). The top-down approach developed in Joung et al. (2013) was adopted due to the following reasons: (1) it is a well-organized integration of 11 established indicator sets using a rigorous selection process and (2) the hierarchical structure of the framework provides a more comprehensive categorization of indicators in a top-down order of components.

Within this framework, FUZAHP was used to measure the sustainability manufacturing index and address the inherent uncertainty attributed to value judgement in prioritizing the sustainability indicators. AHP models a decision problem into a hierarchy of different levels and can be extended to a more complex network structure using analytic network process (ANP) (Saaty 2001; Promentilla et al. 2006). Herva and Roca (2013) conducted a survey of MCDM literature and highlighted that AHP/ANP and outranking methods are commonly used in industry-related applications. However, Garuti and Sandoval (2005) observed that as long as the decision model is comprehensive, AHP provides almost similar results with the ANP. The underlying mathematical framework of the AHP can be found in Saaty (1980) and in several published works that utilized AHP, e.g. in Ocampo (2015). FST, on the other hand, handles the uncertainty of judgement brought about by incomplete information and inadequate understanding of decision-makers, among others. FST is known to improve the capability of MCDM methods such as that of AHP particularly in representing vague judgements as fuzzy numbers (Tseng 2013). Details of mathematical structures of FST can be found in Zadeh (1965).

3. Methodology

The following steps describe the procedure carried out in this work.

(1)	A multi-criteria decision framework was defined as shown in Figure 1. The hierarchical framework is a four-level decision structure where level 1 denotes the goal which is coded as G. Level 2, coded A, B and C, represents the triple-bottom line. Level 3 and 4 elements are coded in reference to their parent elements. For instance, pollution is coded A1 in reference to environmental stewardship (A), its parent element. Thus, toxic substance attribute is coded A11 in reference to pollution (A1), its parent element. The indicators for a level 4 element are all separated from the decision model to allow equal importance of each indicator to its parent element. Table 1 presents the coding system used in this paper. An illustrative case study, Company K, was presented in this study to demonstrate the process of SMI computation. Company K is a multinational plastic manufacturing company that produces high-end binoculars, riflescopes, camera converter and industrial plastic parts. Situated in Mactan Export Processing Zone in Central Philippines, Company K, a strategic business unit, has its own Managing Director reporting to the Corporate Executive Officer of its parent corporation in Japan. Present in the Mactan plant are its Human Resource, Manufacturing, Research and Development, Engineering, Quality Assurance, Accounting and Finance Departments comprising more than 3,000 employees. In the Philippines, they sell their products to end-users. A Management representative of Company K was asked to rate the company’s performance with respect to Level 4 indicators in the questionnaire, developed from the framework of Joung et al. (2013). Level 4 elements of Figure 1 contain 170 indicators presented and each decision-maker was asked to rate each indicator on a scale of 1–10 (with 1 as the lowest and 10 as the highest) regarding the degree of usage, implementation and adoption of a particular indicator. Although authors have recommended that a desirable data acquisition method could be obtained from a focus group discussion comprising the firm’s middle and top managements, this was not conducted as the intention in this case study was merely to illustrate the computation of SMI.
(2)	A group of experts was invited to conduct pairwise comparisons in the context of the AHP methodology. In this work, researchers from De La Salle University Center for Engineering and Sustainable Development Research (DLSU-CESDR) were used as domain experts. DLSU-CESDR was formed as an international consortium that houses collaborative works of five universities in the UK, Sweden, Malaysia, Northern and Southern Philippines in sustainability-related research projects (Ghazali 2007; Culaba and Tan 2008). A search in SciVerse Scopus database yields over a 100 publications of DLSU-CSDR in high-impact journals spanning inter-disciplinary perspectives on sustainability such as life cycle assessment, biofuel production, renewable energy, industrial ecology, resource efficiency, sustainable supply chain, policy research in sustainability and process systems engineering. These experts are perceived to be reliable in eliciting valid judgements as they accumulated more than a decade of research and industry experience on sustainability research projects.
(3)	Following the AHP methodology, value judgements via pairwise comparisons were elicited from the experts to determine the importance of one component over the other with respect to the parent element. To capture uncertainty due to vagueness in this process, the Saaty’s Fundamental Scale was represented by fuzzy numbers. The corresponding triangular fuzzy numbers (TFNs) of Tseng et al. (2008) as shown in Figure 2 were used due to its close similarity to the original scale. Table 2 shows the scale and the corresponding TFNs. The TFN represents the fuzzy judgement aij = 〈l, m, u〉, i.e. the intensity of dominance of i row element over the j column element. Using n(n − 1)/2 pairwise questions, these judgements aij including its reciprocal aji = 〈1/u, 1/m, 1/l〉 were used to populate the pairwise comparison matrix A and compute the priority weights of n elements. Note that there are various variants of fuzzy AHP as discussed in Promentilla et al. (2008). Nevertheless, a plausible approach is to use fuzzy numbers in AHP without deviating much from the general framework proposed originally by Saaty (1980, 2001). Thus, defuzzification of TFNs was done to select appropriately the crisp element from output fuzzy set and populate the pairwise comparison matrix A. Crisp priority vector including the consistency of the judgements were then measured using the eigenvector method (Saaty 1980). In this study, the method by Opricovic and Tzeng (2003) was used for defuzzification since it was shown to be a more appropriate technique in comparison with other methods such as the centroid method and the method developed by Chen and Hwang (1992). Compared to the centroid method, the proposed defuzzification provides greater crisp value with greater membership function and it distinguishes two symmetrical fuzzy numbers with equal mean (Opricovic and Tzeng 2003). This method has been effectively used in various applications as reported in Tseng (2013). Following this method, crisp values were computed and were plugged in the pairwise comparisons matrices before solving the local eigenvectors; thus, providing a framework that does not deviate from the widely accepted eigenvector approach of Saaty (1980). For brevity, the steps of the algorithm proposed by Opricovic and Tzeng (2003) are not presented here.
(4)	The sustainability manufacturing index was then computed as follows. A priority weight represents the contribution of an element with respect to a parent element. When the corresponding performance of a given manufacturing firm is with , then this implies that the firm failed to attain the ideal score of that element; therefore, this implies that the firm has to exert effort and work towards closing the gap that is depicted by . When all differences are aggregated for all elements in all levels, then a performance index of the firm denoted by , can be computed. This computed performance index will be less than 1, as unity is the ideal, the highest attainable index.

Figure 1. Sustainable manufacturing hierarchical structure.

Table 1. Decision system components and their codes.

Decision components and elements	Code	Decision components and elements	Code	Decision components and elements	Code
Evaluation of sustainable manufacturing	G
Environmental stewardship	A	Economic growth	B	Social well-being	C
Pollution	A1	Profit	B1	Employee	C1
Toxic substance	A11	Revenue	B11	Employees health and safety	C11
Greenhouse gas emissions	A12	Profit	B12	Employees career development	C12
Ozone depletion gas emissions	A13	Cost	B2	Employee satisfaction	C13
Noise	A14	Materials acquisition	B21	Customer	C2
Acidification substance	A15	Production	B22	Health and safety impacts from manufacturing and product use	C21
Emissions	A2	Product transfer to customer	B23	Customer satisfaction from operations and products	C22
Effluent	A21	End-of-service-life product handling	B24	Inclusion of specific rights to customer	C23
Air emissions	A22	Investment	B3	Community	C3
Solid waste emissions	A23	Research and development	B31	Product responsibility	C31
Waste energy emissions	A24	Community development	B32	Justice/equity	C32
Resource consumption	A3			Community development programmes	C33
Water consumption	A31			Sustainable manufacturing index	SMI
Material consumption	A32
Energy/electrical consumption	A33
Land use	A34
Natural habitat conservation	A4
Biodiversity management	A41
Natural habitat quality	A42
Habitat management	A43

Figure 2. A TFN .

Table 2. Fuzzy scale for pairwise comparisons (a_ij).

Linguistic scale	Triangular fuzzy scale
Equal (1)	(1,1,1)
More or less equal ()	(1/2,1,3/2)
Between equal and moderate	(3/2,2,5/2)
Moderately more ()	(5/2,3,7/2)
Between moderate and strong	(7/2,4,9/2)
Strongly more ()	(9/2,5,11/2)
Between strong and very strong	(11/2,6,13/2)
Very strongly more ()	(13/2,7,15/2)
Between very strong and extremely strong	(15/2,8,17/2)
Extremely more ()	(17/2,9,9)

4. Results

There were 14 pairwise matrix comparisons that were performed in this study. For the purpose of brevity, not all matrices but merely samples were presented in this study. Table 3 presents the pairwise comparisons matrix of the relative dominance of level 2 elements to level 1 element. The question asked is as such: “Comparing environmental stewardship (A) and economic growth (B), which has a greater impact on SM (G) and by how much?” Results show that economic growth has a priority of 0.402 while environmental stewardship (A) is less prioritized with priority of 0.204.

Table 3. Pairwise comparisons of the dominance of level 2 elements on level 1 parent element.

Sustainable manufacturing (G)	Environmental stewardship (A)	Economic growth (B)	Social well-being (C)	Priority vector
Environmental stewardship (A)	1			0.204
Economic growth (B)		1		0.402
Social well-being (C)			1	0.394

.

Table 4 shows a sample set of pairwise comparisons of level 3 elements to level 2 parent element. A sample question is as such: “Comparing pollution (A1) and emissions (A2), which one has a greater impact on environmental stewardship (A), and by how much?” Results are reported in Table 4.

Table 4. Pairwise comparisons of the dominance of level 3 elements on level 2 parent element.

Environmental stewardship (A)	Pollution (A1)	Emission (A2)	Resource consumption (A3)	Natural habitat conservation (A4)	Priority vector
Pollution (A1)	1				0.353
Emission (A2)		1			0.348
Resource consumption (A3)			1		0.162
Natural habitat conservation (A4)				1	0.137

.

Finally, a sample pairwise comparisons matrix is shown in Table 5 to present the relative dominance of level 4 elements to level 3 parent element. A sample question is as such: “Comparing toxic substances (A11) and greenhouse gas emissions (A12), which one has a greater impact on pollution (A1), and by how much?” Results are reported in Table 5.

Table 5. Pairwise comparisons of the dominance of level 4 elements on level 3 parent element.

Pollution (A1)	Toxic substances (A11)	Greenhouse gas emissions (A12)	Ozone depletion gas emissions (A13)	Noise (A14)	Acidification substance (A15)	Priority vector
Toxic substances (A11)	1					0.349
Greenhouse gas emissions (A12)		1				0.346
Ozone depletion gas emissions (A13)			1			0.121
Noise (A14)				1		0.065
Acidification substance (A15)					1	0.119

.

The complete list of local priority vectors of all elements in the hierarchical structure is presented in Table 6. The elements in the lower level are indented with respect to their parent element. These values are used to obtain global priority vector which represents the relative priority of an element with respect to the goal. Global priority vector is computed by a scalar multiplication of parent element with its respective lower level elements. The objective of this work was to determine the priority ranks of level 4 elements as precursors to their respective sustainability indicators. Ranking of global priorities is shown in Table 7.

Table 6. Local priority vectors from pairwise comparisons.

Elements	Priority vectors
Environmental stewardship			0.204
Pollution		0.353
Toxic substance	0.349
Greenhouse gas emissions	0.346
Ozone depletion gas emissions	0.121
Noise	0.065
Acidification substance	0.119
Emissions		0.348
Effluent	0.233
Air emissions	0.461
Solid waste emissions	0.229
Waste energy emissions	0.077
Resource consumption		0.162
Water consumption	0.304
Material consumption	0.101
Energy/electrical consumption	0.300
Land use	0.295
Natural habitat conservation		0.137
Biodiversity management	0.496
Natural habitat quality	0.254
Habitat management	0.250
Economic growth			0.402
Profit		0.402
Revenue	0.506
Profit	0.494
Cost		0.395
Materials acquisition	0.334
Production	0.329
Product transfer to customer	0.170
End-of-service-life product handling	0.167
Investment		0.203
Research and development	0.339
Community development	0.661
Social well-being			0.394
Employee		0.255
Employees health and safety	0.597
Employees career development	0.203
Employee satisfaction	0.200
Customer		0.495
Health and safety impacts from manufacturing and product use	0.204
Customer satisfaction from operations and products	0.402
Inclusion of specific rights to customer	0.394
Community		0.250
Product responsibility	0.339
Justice/equity	0.333
Community development programmes	0.328

Table 7. Global priorities of level 3 elements.

Level 3 elements	Global priorities	Rank
Revenue	0.08198	1
Profit	0.08003	2
Customer satisfaction from operations and products	0.07840	3
Inclusion of specific rights to customer	0.07684	4
Employees health and safety	0.05998	5
Community development	0.05394	6
Materials acquisition	0.05290	7
Production	0.05211	8
Health and safety impacts from manufacturing and product use	0.03979	9
Product responsibility	0.03339	10
Justice/equity	0.03280	11
Air emissions	0.03273	12
Community development programmes	0.03231	13
Research and development	0.02766	14
Product transfer to customer	0.02693	15
End-of-service-life product handling	0.02645	16
Toxic substance	0.02513	17
Greenhouse gas emissions	0.02492	18
Employees career development	0.02040	19
Employee satisfaction	0.02009	20
Effluent	0.01654	21
Solid waste emissions	0.01626	22
Biodiversity management	0.01386	23
Water consumption	0.01005	24
Energy/electrical consumption	0.00991	25
Land use	0.00975	26
Ozone depletion gas emissions	0.00871	27
Acidification substance	0.00857	28
Natural habitat quality	0.00710	29
Habitat management	0.00699	30
Waste energy emissions	0.00547	31
Noise	0.00468	32
Material consumption	0.00334	33

The computation of firm’s SMI is presented in Section 3 procedure 7. Table 8 provides a sample detail of such computation which represents environmental stewardship in level 1, with pollution in level 2 and toxic substance in level 3 with 11 indicators. Actual score column shows the scores provided by Company K regarding its level of performance in a specific indicator. For instance, lead-use indicator has a score of 1 which means that the company has relatively less amount of lead used. To do this, a method of translating the actual score to a normalized score was used. An increase from 0 to 10 in the rating means that the performance of toxic substance attribute is deteriorating. The rate of 1 in this indicator means a deficiency of 1 with respect to the performance of the firm on that level 3 element. To come up with a positive value which is consistent with the rest of the level 3 elements in the hierarchy, algebraic addition of actual rating was conducted to obtain the normalized score, which is 9 in this case. A value of 9 means that Company K has a relatively higher performance on the specific level 3 element. This value was multiplied with the weight of toxic substance element which was obtained from FUZAHP. Individual indicator indices that correspond to the same parent level 3 element were averaged to obtain the toxic substance index. A value of 2.031 (from 1 to 10 scale) implies that the company has a relatively lower performance in toxic substance level 3 element. This value was normalized with the parent level 2 and level 3 elements. After performing all computations, the SMI for Company K was determined. Results are shown in Table 9.

Table 8. Sample computation of toxic substance index of Company K.

Toxic substance	Actual score	Normalized score	Priority	Computed index
Lead (Pb) used	1	9	0.349	3.141
Mercury (Hg) used	1	9	0.349	3.141
Hexavalent chromium (Cr6+) used	1	9	0.349	3.141
Cadmium (Cd) used	1	9	0.349	3.141
Polybrominated biphenyl flame retardants (PBB) used	7	3	0.349	1.047
Polybrominated diphenyl ether flame retardants (PBDE) used	7	3	0.349	1.047
Eco-toxic substance effluent	6	4	0.349	1.396
Eco-toxic waste produced	6	4	0.349	1.396
Number of WEEE-related registrations	5	5	0.349	1.745
Chemical spills	6	4	0.349	1.396
Eco-toxic substances emissions	5	5	0.349	1.745
				2.031

Table 9. Company K performance indices.

Elements	Local priority vectors			Company K index
Environmental stewardship			0.204			0.094
Pollution		0.353			0.186
Toxic substance	0.349			0.203
Greenhouse gas emissions	0.346			0.242
Ozone depletion gas emissions	0.121			0.061
Noise	0.065			0.019
Acidification substance	0.119			0.063
Emissions		0.348			0.095
Effluent	0.233			0.058
Air emissions	0.461			0.138
Solid waste emissions	0.229			0.069
Waste energy emissions	0.077			0.077
Resource consumption		0.147			0.080
Water consumption	0.304			0.175
Material consumption	0.101			0.027
Energy/electrical consumption	0.300			0.094
Land use	0.295			0.024
Natural habitat conservation		0.162			0.096
Biodiversity management	0.496			0.347
Natural habitat quality	0.254			0.178
Habitat management	0.25			0.175
Economic growth			0.402			0.214
Profit		0.403			0.281
Revenue	0.506			0.354
Profit	0.494			0.346
Cost		0.394			0.153
Materials acquisition	0.334			0.100
Production	0.329			0.105
Product transfer to customer	0.170			0.082
End-of-service-life product handling	0.167			0.100
Investment		0.203			0.098
Research and development	0.339			0.226
Community development	0.661			0.264
Social well-being			0.394			0.201
Employee		0.255			0.124
Employees health and safety	0.597			0.319
Employees career development	0.203			0.118
Employee satisfaction	0.200			0.050
Customer		0.495			0.281
Health and safety impacts from manufacturing and product use	0.204			0.109
Customer satisfaction from operations and products	0.402			0.241
Inclusion of specific rights to customer	0.394			0.217
Community		0.250			0.105
Product responsibility	0.339			0.102
Justice/equity	0.333			0.206
Community development programmes	0.328			0.113
Sustainable manufacturing index (SMI)	1.000			0.415

The two rightmost columns in Table 9 are Company K indices in relation to the highest possible indices (as shown in the corresponding local priority vectors columns). For instance, toxic substance, greenhouse gas emissions, ozone depletion gas emissions, noise and acidification substance have local priorities 0.349, 0.346, 0.121, 0.065 and 0.119, respectively. Correspondingly, Company K has 0.203, 0.242, 0.065, 0.019 and 0.063 indices of these elements; the resulting differences imply the shortcomings of Company K on these areas. The SMI for Company K is shown to be 0.415. Basing on the taxonomy proposed by Ghadimi et al. (2012) which states that an index with range from 0 to 0.33 is for firms with sustainable programmes which are classified as ‘low sustainable’, 0.34 to 0.66 as ‘medium sustainable’ and 0.67 to 1 as ‘high sustainable’. The case study, Company K sustainable approach could therefore be categorized as ‘medium sustainable’. Future research work could therefore, be directed on exploring the characteristics, challenges, opportunities and potentials of a ‘medium sustainable’ manufacturing firm.

5. Discussion

Sustainability indicators for each level 3 element could have been placed in the hierarchical decision structure to portray a more comprehensive decision structure, but was done otherwise due to the following arguments. First, sustainability indicators for each level 3 element in SMIR (2011) represent different functions of SM such that pairwise comparisons among them would lead to varying interpretations. Second, selection of relevant indicators for a particular manufacturing firm varies with firm’s manufactured products and manufacturing processes. The contribution of this work lies in identifying priority areas that have relatively high impact on SM and in demonstrating a platform in gauging a firm’s SM performance at the organizational level. Table 7 serves as an aid in the selection decision of relevant sustainability approaches or in the creation or development of efficient strategies to enhance sustainability status. Results show that top elements include revenue, profit, customer satisfaction from operations and products, inclusion of specific rights to customer and employees’ health and safety which represent combinations of economic and social aspects. In sustaining manufacturing, strategies must be engaged in improving economic performance in terms of higher revenues and profit. This idea is both theoretical and practical which is in line with traditional approaches of maintaining a profit-centred manufacturing. However, along with programmes directed at improving economic performance, manufacturing companies must keep their customers at the forefront of their agenda. Among important indicators that must be measured are customer satisfaction assessment, customer complaints, product and service information required by procedures and breaches of customer privacy. Programmes must be focused on how to keep these indicators intact along with programmes relating to economic performance. Upstream supply chain is considered lifeline of every manufacturing firm as this has been shown in this work. Note further that top five elements relate to socio-economic aspects that extend from profit- and customer-centred approaches to cost, employee and customer welfare. One-third of the top five elements represent economic aspects while two-thirds of them consider social aspects. In the top 10 elements, 37% of them are economic, 23% are environmental and 40% are social. It is worth noting that socio-economic issues arising from manufactured products and manufacturing processes play a highly significant role in the sustainability of manufacturing firms.

It is also noted that Company K sustainability status is half way of the ideal level. Although economic status is preferred, its difference from environmental and social dimensions is not significant. It is shown in this paper that computing for SMI helps assess the performance of the firm on each sustainability aspect and the firm’s overall sustainability status. This directs decision-makers’ efforts on the most relevant areas on which they could focus their strategies. Based on the computed difference between the status quo and the ideal level, decision-makers have a metric that may give them a handle in ascertaining an estimate of the required resources in achieving the desired level.

6. Conclusions and future work

With the use of the hybrid FUZAHP in addressing a comprehensive SM framework, this paper was able to identify priority areas that bear relatively high impact on SM. Addressing socio-economic issues is a vital priority pivot of manufacturing firms. Such approach comprises developing initiatives that both enhance profit and customer welfare through customer satisfaction from manufactured products and inclusion of customer-specific rights. High-impact areas also include cost reduction initiatives and engaging in programmes that enhance employee and community welfare. Creation and development of programmes that highlight revenue, profit and cost, together with customer, employee and community is a rich area for future research.

This work also highlights the use of SMI that attempts to describe the sustainability status of the firm following the priorities obtained from FUZAHP methodology. A case study of an industrial plastic manufacturing firm was shown to demonstrate the computations necessary to obtain a SMI. The use of SMI enables decision-makers to trace company-wide and lower level performance in various sustainability areas. It integrates objective and subjective performance indicators through an analytical procedure that provides formal numerical representations which practicing managers could easily understand due to its simplicity and great deal of tractability. SMI also aids decision-makers in resource allocation to achieve a specific sustainability level. Despite these interesting applications of the SMI even in areas of supplier selection, cautions must be taken. It is suggested that a longitudinal, cross-country work be done to verify the priorities obtained in this work as bases in the computation of the SMI. The figures of the SMI have to be correlated with other vital measures of the economy.

Future works could be directed on the following areas. First, it would be relevant to explore the effects of interrelationships of the components and elements in the hierarchical structure which exist in practical applications. Second, a multivariate study is likewise relevant in assessing how environmental aspects play a role in the socio-economic issues being noted in this paper. Third, exploring the interactions of stakeholders such as the government, customers, suppliers and community in the decision problem is also an interesting work that could lead to useful insights. Lastly, an optimization modelling of resource allocation in the priority areas of SM would also be a relevant work in the future.

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgement

We are grateful with the insightful comments of two anonymous reviewers which helped us improve the quality of this paper. L. Ocampo acknowledges Engr. Kae Vines Tanudtanud, M.Sc. for the computational support rendered in this study. Also, L. Ocampo gratefully acknowledges the support from the University of San Carlos in terms of resource use.