The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010
Sustainability Evaluation for Decision Making Abdul Rahman Hemdi † Faculty of Mechanical Engineering, Universiti Teknologi Mara, UiTM Pulau Pinang, MALAYSIA Email:
[email protected] Muhamad Zameri Mat Saman1 and Safian Sharif2 Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor, MALAYSIA Email1:
[email protected] Email2 :
[email protected]
Abstract - As the awareness of the impact of sustainability on the industrial sector arises, it promotes the development of new and practical methods and tools for sustainability evaluation. Various tools were developed to assist designers and decision makers for sustainability evaluation such as Eco Indicator 95 and 99, Life Cycle Index, Ten Golden Rules and etc. However, some of them only focus on one or two elements of sustainability whereas sustainability requires the consideration of the environment, economic and social elements. In addition to that, the challenging task in sustainable evaluation is how to deal with the uncertainty as it not only evaluates the current situation but also the prediction and strategic decision making for future. Consequently, a simplified and understandable methods and tools are required in guiding the designers and decision makers in sustainability evaluation of product and process. Hence, this paper aims to propose a sustainability evaluation framework by using fuzzy method in guiding the designers and decision makers to develop sustainable product and process with the consideration on environmental, economic and social aspect. The fuzzy logic approach was integrated into the evaluation process as it has a capability to handle severe uncertainty and ability to evaluate qualitative and quantitative data simultaneously. The example of selecting the alternatives of power generation sources was used as a preliminary case study. Keywords: Product design, sustainable product, sustainability evaluation, decision making, fuzzy method.
1. INTRODUCTION Sustainability can be defined as “a notion of viable futures” which includes aspects of environment, public health, social equity and justice (Blevis, E. 2007). It is greatly influenced by the number of population and impact (Keoleian, G.A. et al. 1994). As the population and impact keep increasing, the world will become less sustainable. It is difficult to increase or maintain sustainability because the number of population keeps rising every year. The situation creates a negative impact to our earth and mankind. As the human population increases, more domestic and non domestic products will be produced due to higher demand. Similarly in the mass production of products, a lot of energy and raw materials will be consumed. As a result, the natural resources for future generation are declining rapidly. At the same time, a lot of waste and emission are generated during manufacturing, use and end of life stage of product life cycle. The chemical and hazardous wastes from the
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industries are flowing out into our natural water stream such as river and sea creating harmful effect to human, plants and animals (Malaysia Department of Environment, 2006; Bates, B.T. et al. 2008). Subsequently, more land area is required to provide landfill of solid waste disposal, building and houses (Gitay, H. et al. 2002). This entire situation decreases the world’s sustainability. Other alternative for maintaining world’s sustainability is by reducing the number of impact. One of the best solutions in reducing the impact is to design and select the best sustainable products, process, services or systems which have less impact to the next generation (Keoleian, G.A. et al. 1994). Designing and selecting the product, process or system toward sustainable development is not an easy task as it involves many criteria to be considered such as environment, social and economic aspect (Howarth, G. and Hadfield, M. A. 2006; Al-Sarrah, G. et al. 2010). On the other hand, the organizational goals and objectives also
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010 have to be considered while fulfilling the sustainable development needs (De Silva, N. et al 2009). Lastly, any action taken has to be complied with the current national and international legislation such as ISO standard, EU directive and others. Consequently, these situations will lead to the complexity of designing and selecting the sustainable alternatives, product or process (Benetto, E. et al. 2008; Gehin, A. et al. 2008). As such, there is a need for methodology and tool in aiding designer and decision makers when developing the sustainable alternatives. In this paper, a comprehensive sustainability evaluation with an integration of fuzzy approach was proposed in guiding the designer and decision makers. The assessment has the ability to evaluate quantitative and qualitative data simultaneously and the results were presented in term of scoring point and graphs which are easily interpreted.
process, social, risk and etc. The grouping of the criteria or variable is based on the potential impact categories at which the criteria will be affected. In this study, criteria or variable was categorized into eight sub sustainability elements and then further reduced into three main sustainability elements and lastly aggregated to obtain a single index of sustainability as shown in Figure 1.
2. METHODOLOGY The methodology in determining the sustainability evaluation consists of four steps such as: 1. Data collection, classification and grouping of data towards their respective impact categories. 2. Calculation of sub element index using fuzzy inference system. 3. Calculation of main element (environment, economic and social) index. 4. Calculation of the sustainability index.
2.1 Data Collection The first step in data collection is to define the boundary of analysis. There are two common types of system boundary which are cradle to gate and cradle to grave. According to Giudice, F. et al. (2006) cradle to gate can be defined as the analysis of the portion of life cycle upstream from the gate. The assessment is inclusive from the raw material acquisition until processing and manufacturing stages until the product or service comes out from the factory or production plant. Whereas the cradle to grave boundary system is evaluate the upstream and downstream from the gate. The analysis must be conducted from material acquisition, manufacturing, distribution, use and until end of life stages (Sadiq, R. and Khan, F.I. 2006). After the boundary has been defined, the input and output or elementary flow in and out from the boundary is collected for evaluation. For example, the input criteria for raw material consist of amount and type of material used, energy required and etc while output criteria may include of amount of waste (solid or liquid), toxic substance and etc. The collected data is then grouped or categorized accordingly to the sub element of sustainability such as pollution, global warming, resource, cost, technology,
Figure 1. Classification and grouping of sustainability parameter into sub element, main element and sustainability index.
2.2 Determination of Sub element Index Using Fuzzy Method This is the stage where the collected data either quantitative or qualitative are evaluated for sustainability assessment. The eight sub elements of sustainability will be determined in term of index value in the range from zero to one. The fuzzy evaluation method was applied due to its ability to handle mix of qualitative (linguistic variable) and quantitative (numbers) data. Beside that, it is also capable in reducing data uncertainty which is essential in the
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010 sustainability evaluation for forecasting the future. The fuzzy model for sustainability evaluation is shown in Figure 2. It consists of three main sections which are input, fuzzy evaluation and output. The input variables for fuzzy evaluation model, (Sk) are known as sustainability parameters which include amount of water pollution, air pollution, solid waste, energy and chemical used, operating
index using equation (1). n
Ij
wij I ij
i 1
(1)
n
Where Ij = index of sustainability element for j-th categories wij = weight of sub sustainability element of j-th categories Iij = sub sustainability element index of i-th elements of j-th categories i = sub sustainability element (pollution, global warming, resource, cost, technology, process, social performance and risk) j = sustainability element (environment, economy and social) n = number of element Then, the sustainability element index will be further aggregated into a single index value which is known as sustainability index. It can be determined by averaging the three main sustainability element indices which are environmental, economic and social indices as in equation (2). The index value will be in the range between zero (low sustainability) to one (high sustainability). n
I sustainability
Ij
j 1
n
( I envi
I eco
I soc ) / 3
(2)
Where, Ienvi = environment index Ieco = economic index Isoc = social index
3.0 EXAMPLE OF CASE STUDY
Figure 2. Steps in fuzzy model for sustainability evaluation. cost and etc. In order to evaluate, the value of each sustainability parameter, (Xk) must be first determined. The data can be obtained from the design specification, production report, sales report and etc.
2.3 Determination of Sustainability Index The index value of sub sustainability element which is obtained from the fuzzy evaluation process will be aggregated into three main sustainability element index such as environment index, economic index and social
The fuzzy method for sustainability evaluation was tested using a case study of selecting the electrical power generation sources. The five types of power generation plants such as: 1. Average – a coal-fired power plant which represents the average emission and efficiency. 2. NSPS – a new coal-fired power plant that meets the New Source Performance Standards. 3. LEBS – a highly advanced coal-fired power plant using low emission boiler system. 4. Natural gas power plant. 5. Biomass power plant. The raw data for each power plant is presented in Table 1 which is obtained from Khan, F.I et al. (2004).
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010 Table 1: The basic raw data for evaluation of power generation alternatives.
1
Sustainability Parameter
Unit
Energy consumed Chemical consumed Renewable energy consumed Green house gases Ozone depletion Acid Potential Oxidation potential Water pollution Air pollution Solid waste Human health risk Ecological risk Safety risk Technology status Technology verification Process temperature Pressure Quantity involve Phase of chemical Human machine interaction Energy efficiency Fixed cost Operating cost HSE1 cost Socio-politic Earthquake Riot Social impact
kg/kWh kg/kWh kg/kWh kg CO2 eq/kWh kg CFC12 eq/kWh pH kg O3 eq/kWh kg/kWh kg/kWh kg/kWh dimensionless dimensionles fatality/year dimensionles dimensionles temp ( C ) psi unit dimensionles % kWh/kWh consumed $/kWh $/kWh $ dimensionless ritcher scale no/year dimensionless
Average 0.48 0.104 5 1.06 0.004 6.5 0.0054 0.00016 1.05 0.112 2.52 0.0268 0.00001 old tested 300 72.5 30000 solid 60 0.29 1129 22.85 1000000 accepted 2 0 moderate
Value of input parameter NSPS LEBS Natural gas 0.488 0.11 3.5 0.96 0.003 6.2 0.0034 0.00017 0.956 0.113 2.29 0.027 0.00001 new tested 300 72.5 30000 solid 60 0.31 1173 23.46 1000000 accepted 2 0 moderate
0.362 0.0002 2 0.741 0.0007 6.24 0.000014 0.000074 0.743 0.0206 1.78 0.0049 0.00001 new not well tested 700 58 30000 solid 60 0.38 1170 23.45 1000000 accepted 2 0 moderate
0.171 0.002 1 0.499 0.004 6.5 0.0034 0.00001 0.444 0.133 1.065 0.0319 0.00001 old tested 500 72.5 16730 LG 75 0.4 524 10.5 1000000 accepted 2 0 moderate
Biomass 0.005 0.001 0.89 0.049 0.001 6 0.022 0.000006 0.069 0.00063 0.1656 0.00028 0.00001 new not well tested 980 250 133400 solid 60 15.6 1187 18.57 1000000 accepted 2 0 moderate
HSE = health, safety and environment
4.0 RESULT Results in determining the sub sustainability index is presented using a spiderweb graph shown in Figure 3. It shows that the biomass power plant has the higher sustainability index in resources consumption, pollution, risk and technology. This is because biomass is a one of the new and high efficiency technology which consumes low energy and chemical, produces less water, air and solid wastes and also having low risk to human health and ecology. With regard to global warming, the highly advanced coal-fired power plant (LEBS) have a better sustainability value because of its low emission of ground
level ozone (O3) and also CFC12. In addition, the lowest amount of fixed and operation cost of natural gas power plant contribute to the highest value of cost index. As for the social performance, the index value of 0.75 was generated from the analysis and it was the same for each type of power plant. This is because the evaluation is conducted at the same location or region.
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010
6. CONCLUSION
Figure 3. Graph of sub sustainability element index for each electrical power generation. In order to assist the decision making process, the index values are further reduced to a single index known as the sustainability index. The sustainability index is determined using equation (2) and results are tabulated in Table 2 and the most sustainable alternative is ranked accordingly. Results show that biomass power plant has the highest sustainability index of 0.68 which is considered the most sustainable electrical power generation plant than the others. This is then followed by LEBS power plant with value of 0.627. It is also evident that the average coal based power plant is the most unsustainable alternative for generating the electricity. Table 2. The sustainability index and the ranking of the
electric power generation alternative Alternatives Average NSPS LEBS Natural gas Biomass
Sustainability index 0.50 0.53 0.63 0.56 0.68
Ranking 5 4 2 3 1
5. DISCUSSION From the results, it is clear that the sustainability evaluation using fuzzy model is able to provide a comprehensive approach in decision making. The transformation of quantitative and qualitative of sub sustainability element into an index value contributes to the reduction of the complexity in decision making process. From the above example, it shows that biomass power plant is the best selection because of its high level of sustainability value as compared to other alternatives. The index value also represents the ranking of each alternative which can assist designers in deciding the best selection.
This paper presents a comprehensive approach to evaluate sustainability by applying fuzzy interference system. The advantages of this proposed methodology is its ability to assess the qualitative and quantitative data simultaneously. In addition, the strength of fuzzy technique which has the ability to reduce data uncertainty is an added advantage of this proposed method. The method can be treated as a interactive tool in which the target value or reference value can be changed according to the corporate objective or policy decision. Besides that, the input variable (sub sustainability element) can be added as required based on the increase of the data availability. This flexibility helps to continuously evaluate the system, process or product towards better sustainability result.
ACKNOWLEDGMENT The authors would like to express their thanks to Ministry of Higher Education of Malaysia, Universiti Teknologi Malaysia and Universiti Teknologi Mara for financial support of this research.
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The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research development stage of consumer electronic products, Int. Journal Sustainable Manufacturing, Vol. 1, No. 3, 251-264. Gehin, A., Zwolinski, P. and Brissaud, D. (2008) A tool to implement sustainable end-of-life strategies in the product development phase, Journal of Cleaner Production, 16, 566-576. Gitay, H, Suarez, A., Watson, R.T., Dokken, D.J., (Eds.) (2002) Climate change and biodiversity, Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva. Giudice, F., Rosa, G.L., and Risitano, A. (2006) Product Design for the Environment, CRC Press, USA.
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AUTHOR BIOGRAPHIES Howarth, G. and Hadfield, M. (2006) A sustainable product design model, Material and Design, 27, 1128-1133. Keoleian, G.A., Menery, D., Vigon, B.W., Tolle, D.A., Cornaby, B.W., Latham, H.C., Harisson, C.L., Boguski, T.L., Hunt, R.G. and Sellers, J.D. (1994) Product Life Cycle Assessment to Reduce Health Risks and Environmental Impacts, Noyes Data Corporation, USA. Khan, F.I., Sadiq, R. and Veitch, B. (2004) Life cycle
Abdul Rahman Hemdi is a Lecturer at the Department of Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Mara, Malaysia. He received a Master Degree from the Graduate School of Engineering at Universiti Teknologi Malaysia, Malaysia in 2006. His teaching and research interests include product design, manufacturing and product sustainability. He can be reached at
