A Framework for Estimating the Sustainability Index of New Product based on AHP and BP Neural Network

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1 Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 6, 2012 A Framework for Estimating the Sustainability Index of New Product based on AHP and BP Neural Network Mohd Fahrul Hassan Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia Muhamad Zameri Mat Saman and Safian Sharif Department of Manufacturing and Industrial Engineering Faculty of Mechanical Engineering Universiti Teknologi Malaysia, Skudai, Johor, Malaysia Badrul Omar Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia Abstract Sustainability index is one of the indicators that allow company to design new products that cater to environment, economic and social aspects, which is main factor of sustainability requirements. In this paper, a framework for estimating the sustainability index of new product is presented, using three integrated approaches which are Morphological analysis, Analytical Hierarchy Process (AHP) technique and BP Neural Network model. As a result from the case study, the product sustainability index can be estimated in order to assist product designers to judge their product at the early design stage. Keywords Sustainability index, Morphological analysis, AHP, BP Neural Network, Sustainable product. 1. Introduction The evolution of product design has already moved from a production-oriented approach applied in the past to a marketing-oriented approach, a customer-oriented approach and finally to a sustainable-oriented approach. Sustainable products are defined as a product that provides environmental, social and economic benefits while protecting public health, welfare and environment over their full commercial cycle, from the extraction of raw materials to final disposition [1]. Modifying the design and material composition of a product is one of the successively approaches to achieve the sustainable products, so they generate less pollution and waste throughout their life cycle [2]. Indicators are a key tool for encouraging progress towards sustainable development (SD) due to the complicated definition of SD [3]. In general, indicators are designed to capture the ideas inherent in sustainability and transform them into a manageable set of quantitative measures and indices that are useful for communication and decision making [4]. Jawahir et al. [5] presented six major elements of product sustainability along with identified sub-elements as the basic for developing generic product sustainability indicators as shown in Figure 1. Evaluating product sustainability during conceptual design phase has become an imperative apprehension for most industries in relation to product design and development. Life cycle assessment (LCA) is an environmentally fundamental methodology which is used to comprehensively and quantitatively evaluate the significance of potential environmental impacts and to systematically identify hot spots incurring heavy environmental impacts [6]. However, LCA is often cumbersome, expensive to perform and designed to measure primarily resource use and environmental impact with no allowance for qualitative analysis [3]. Regarding to this matter, many sustainability assessment tools 1111

2 have been proposed extensively. The sustainability assessment tool is not designed to replace the LCA process but aims to be quicker by focusing on specific areas of concern through the product life cycle, and so it provides a more cost-effective, systems-based and pragmatic product assessment [3]. Vinodh and Rathod [7] and Wang et al. [8] proposed the integration of environmentally conscious quality function deployment (ECQFD) and LCA approaches at the early stage of conceptual design for ensuring sustainable product. Sheng and Soo [9] and Serban et al. [10] presented an integration of Theory of Inventive Problem-Solving (TRIZ) with eco-efficient elements and design for environment approaches. Vinodh [11] introduced the approach for designing a sustainable product by using Computer Aided Design (CAD) and Design Optimization Strategies. The approach begins with CAD modeling of product followed by sustainability analysis to determine environmental impacts. Hence, this paper presents a framework as a sustainability assessment tool, using three integrated approaches which are Morphological analysis, AHP technique, and BP neural network model. The integrated approach in this framework can be used to help product designers estimate the best combination of product design for achieving the desirable product in sustainable manner. The proposed framework is outlined in the next section. Figure 1: Product sustainability wheel [5] 2. Methodology This section outlines the proposed framework of the integrated approach for estimating the sustainability of new products, as shown in Figure 2 and a brief introduction to the proposed approaches of the framework. 2.1 Morphological Analysis Morphological analysis was introduced by Zwicky (1948) who had successfully used this method in the construction of reaction engines. The morphological analysis is mostly accepted in the textbooks [12, 13] and by researchers [14-16] as an effective tool for extracting and generating a new type of design element concept. The aim of morphological analysis is to widen the area of search for solutions to a design problem by decomposing a product into a number of independent form elements and then broken the form elements into variety of element types [14]. 1112

3 2.2 Analytic Hierarchy Process (AHP) AHP is one of the techniques that assist decision makers in solving complex problems by organizing thoughts, experiences, knowledge and judgments into a hierarchical framework, and guiding them through a sequence of pairwise comparison judgments [17]. The AHP technique was developed by Saaty in the 1970s and has been widely used since then. It has been applied in different fields in order to incorporate both qualitative and quantitative considerations of human perception [17-19]. The AHP methodology begins with pairwise comparison of the selected criteria to others criteria to convert the human perception of importance into a numerical value. To do so, the AHP uses a fundamental scale of absolute numbers as shown in Figure 3 to capture the human perceptions with respect to quantitative and qualitative attributes [18]. 2.3 Back Propagation (BP) Neural Network BP Neural Network is not only the most widely used, but also one of the most maturely developed neural networks that have been applied in many areas [20-22]. Back propagation was created by generalizing the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. Input vectors are used to train a network until it can approximate a function, associate input vectors with specific output vectors [22]. The BP Neural Network usually consists of input layer, hidden layer and output layer. The training of the BP Neural Network involves three stages; the feedforward of the input training pattern, the calculation and backpropagation of the associated error, and the adjustment of the weights [23]. Figure 2: The proposed approach for estimating product sustainability index As the brief introduction of the approaches in the presented framework, the sustainability index of new products can be estimated. The integrated approaches are correlated to each other, where at first, Morphological analysis is used to extract design elements of selected product, searching a new alternative of design elements, and generate a variety form of a new product. Secondly, AHP is applied to analyze the influencing factor of product sustainability on the generated product designs, calculating the influencing factor weights, and calculating product sustainability index. With the identification of design elements and influencing factors of product sustainability, the relationship between 1113

4 them can be established. Finally, the BP Neural Network model is proposed to recognize patterns, analyzing the selected data and constructing trained network structure. For implementing the described of the proposed framework, a case study is outlined in the next section. Intensity of importance Definition 1 Equal importance 3 Moderate importance 5 Strong importance 7 Very strong importance 9 Extreme importance Note: 2, 4, 6, 8 are the used to compromise between the above value Figure 3: AHP fundamental scales for pairwise comparison 3. Case Study A life cycle case for personal digital assistant (PDA) was conducted in this study. Lin et al. [21] has identified design elements of PDA from the consumers perceptions. The four identified design elements were used in this study for generating a variety design of PDA. Table 1 shows the morphological table of the PDA, with the four identified design elements (i.e. top shape, bottom shape, function-keys arrangements, and color treatment) and ten associated design element types. Table 1: Morphological table of PDA design elements Design element Type of design element Top shape (0.1) Line (0.11) Chamfer (0.12) Fillet (0.13) Bottom shape (0.2) Fillet (0.21) Chamfer (0.22) Arc (0.23) Function-keys arrangement (0.3) Line (0.31) Symmetry (0.32) Color treatment (0.4) Non- color (0.41) Color (0.42) Ten PDAs were developed randomly by combining each design element as representatives of the total combination, as displayed in Figure 4. To obtain the product sustainability index for the ten representative PDAs, the AHP technique was used in the next step. The influencing factors of product sustainability were analyzed. Since the key tool for encouraging progress toward sustainable development is indicators, the influencing factors were referred to product sustainability hierarchy which was developed by Gupta et al [1]. 1114

5 Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 Product 9 Product 10 Figure 4: The combination models of ten representative PDAs Considering the evaluation of five design-related workers, each of whom had at least two years of relevant experience, the judgment matrix of the indexes were completed. Global priority of the influencing factors that obtained from Gupta et al. [1] were referred and multiplied the weights with the individual product scores to evaluate the total product sustainability index, as shown in Table 2. Table 2: Influencing factors and sustainability evaluation for the ten representative PDAs Influencing factors Global priority Prod 1 Prod 2 Prod 3 Prod 4 Prod 5 Prod 6 Prod 7 Prod 8 Prod 9 Prod 10 I1Pm1: Material extraction I1Pm2: Design for environment I1Pm3: Material processing I1Mn1: Production energy used I1Mn2: Hazardous waste I1Mn3: Renewable energy used I1Us1: Emission I1Us2: Functionality I1Us3: Hazardous waste I1Pu1: Recyclability I1Pu2: Remanufacturability I1Pu3: Redesign I1Pu4: Landfill contribution I2Pm1: Recovery cost I2Pm2: Potential for next life I2Pm3: Raw material cost I2Pm4: Labor cost I2Pm5: Storage cost I2Mn1: Production cost I2Mn2: Packaging cost I2Mn3: Energy cost I2Mn4: Transport cost I2Us1: Modularity I2Us2: Maintenance cost I2Us3: Repair cost I2Us4: Consumer injury cost I2Us5: Consumer warranty cost I2Pu1: Recycling cost I2Pu2: Disassembly cost I2Pu3: Disposal cost I2Pu4: Remanufacturing cost I2Pu5: Recycled material value I3Pm1: Worker health I3Pm2: Worker safety I3Pm3: Ergonomics I3Mn1: Work ethics I3Mn2: Ergonomics I3Mn3: Worker safety I3Us1: Product pricing I3Us2: Human safety I3Us3: Upgradability I3Us4: Complaints I3Pu1: Quality of life I3Pu2: Take back options I3Pu3: Reuse I3Pu4: Recovery Total product sustainability index

6 Table 3 shows the sustainability index of the ten representative PDAs. BP Neural Network model needs a certain number of known samples training in order to use the trained network and estimate the sustainability index. Data of the six products of PDA from the top were used in the training samples and the last four products regarded as the test samples testing the effect of sustainability index through BP Neural Network model. Product ID Table 3: Product sustainability index of the ten representative PDAs Top shape (0.1) Bottom shape (0.2) Function-keys arrangement (0.3) Color treatment (0.4) Product sustainability index Prod Prod Prod Prod Prod Prod Prod Prod Prod Prod Algorithm was analyzed using MATLAB. BP Neural Network model uses all the four design elements as input variables, which for the design element type and the value of the corresponding input neuron is followed by the given code on each element. The output for this network is one neuron, which refers to the product sustainability index of the PDA. The number of neurons in the single hidden layer uses a widely used rule [24], (the number of input neuron + the number of output neurons)/2. Then, the network was constructed with a BP Neural Network. The transfer function is Sigmoid for all layers [24], the learning rate is 0.05, performance goal is 0 and max training epochs were set to 10,000. After the training process was completed, the best of trained network was saved and the last four products were tested for estimating the sustainability index using the trained network. The inferred output that obtained from the trained network is defined as the product sustainability index. Table 4 shows the value of the product sustainability index of each PDA design model from the trained network being compared to the actual value. From the comparison, it shows that the value from the trained network is approximate to the actual value and the sequence of product rating is similar. Table 4: Estimated product sustainability index value and the sequence of product rating Product sustainability index Product ID Product BP Neural Product Actual value rating Network rating Product Product Product Product The total combination of PDA design is 36 (3x3x2x2) with different combination of design element types on each design element, together with its corresponding product sustainability index value (10 PDAs from the AHP method, 26 PDAs are estimated using trained network of BP Neural Network model). Table 5 illustrates the highest product sustainability index of the PDA form design. Therefore, the product designers can determine the desirable sustainability index or desirable design element type for a new PDA. 1116

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