Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, 2Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh Department of Production and Metallurgy Engineering, University of Technology, Baghdad, Iraq, mohammedraof415@yahoo.com 1 1,2 doi:10.4156/jdcta.vol5.issue3. 9 Abstract The main problem of this paper is to improve product planning through the distinction between engineering specifications of the product, and the nomination of the basic engineering specifications that are depend in the development process, according to the opinions of different designers and customers, that take into account more than one goal to achieve the principle of competition for the product. To achieve this goal, a new hybrid methodology has been built. This new methodology utilized Quality Function Deployment (QFD) with fuzzy logic for improving product planning. The methodology was conducted in six models that include four different of House of Quality (HOQ); two of them are in traditional state, and the other in fuzzy state. These models are current crisp QFD model, virtual crisp QFD model, initial fuzzy QFD model, final fuzzy QFD model, optimum fuzzy QFD model and optimum crisp QFD model. Application results show new values and ranking for the new engineering specifications of the desired product to be developed, and it was possible to use the values of these specifications by the designer in its crisp format rather than the fuzzy. Thus were able to find out the critical and important specifications for improving product planning which should be considered in product development process. Keywords: Quality Function Deployment, House of Quality, Fuzzy logic, Product planning, Critical specifications 1. Introduction Manufacturing companies facing competition in the global markets realize that to efficiently manufactured products preferred by customers at a competitive cost within shorter timeframe over those offered by competitors is critical to their survival. Therefore the organizations direct their efforts towards meeting their customer requirements, development cycle times shortened, market penetration increased with improved product quality, gaining better customer satisfaction and higher income [1.2, 3]. A widespread practice in industry is to cope with global competition by the adoption of QFD. In the traditional, QFD most of the input variables are assumed to be precise and are treated as crisp numerical data. However, linguistic variables expressed in fuzzy numbers seem more appropriate for describing those inputs in QFD. Research on fuzzy QFD has received considerable attentions. There is a need to develop a hybrid system to incorporate the principles of QFD to determine design targets [2]. In order to make the development process of the product successful by using the QFD and getting better outcomes, there is need to have the important inputs. And one of the inputs to HOQ in QFD is the customer's needs and the extent of the importance of these needs. Also the engineering specifications of the product and the range of their effects on meeting the customer's requirements are also important inputs. In addition competitive analysis includes some of the essential inputs in the market in order to approach high accuracy in technical importance rating of the newly developed product. There shall be the need to propose a new hybrid method that differs from the traditional method of QFD, as this method would include the principles of QFD together with fuzzy logic so as to improve the product planning that will lead to development the current product, under the following conditions: 1. Possibility of selling the current product in original market and competitiveness markets. 2. Take into account the opinions of different designers and customers. 3. The improvement is done without the need to make large investments in the manufacturing processes and without the customer's loss in original market. - 98 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 2. Literature Review QFD is part of Total Quality Management (TQM) that lead the companies to the success. QFD is one of these important systems for improving product planning that leads to develop the product. Over three decades have passed since Japanese academics and industries began to formalize the QFD technique in the late 1960s and early 1970s [4]. Dr. Mizuno, professor at Tokyo Institute of Technology is credited with initiating the QFD system. The first application of QFD was at Mitsubishi Heavy Industries Ltd. and in the Kobe Shipyard, Japan in 1972. After four years of case study development, refinement, and training, QFD was successfully implemented in the production of mini-vans by Toyota. Using 1977 as a base, a 20% reduction in startup costs was reported in the launch of the new van in 1979, a 38% reduction by 1982, and a cumulative 61% reduction by 1984. QFD was first introduced in the United States in 1984 by Dr. Clausing. QFD can be applied to practically any manufacturing or service industry. It has become a standard practice by most leading organizations, which also require it of their suppliers [5]. Many QFD applications and studies have been reported due to its effectiveness in product development and quality management. Literature review saw the publication of papers and articles and explains the history of researches on projects in QFD and its objectives. QFD and Fuzzy logic are connected together, and these subjects are used as a master key words in paper. From literature survey, the QFD plays an important role in industrial application where with this tool one can obtain new development of a product. The survey shows the following important points: 1. The Analytic Network Process (ANP) procedure, Analytic Hierarchy Process (AHP) technology, zero–one goal programming methodology and conjoint analysis are employed in QFD implementations to get the best design targets with which the detail design is completed [6, 7, 8]. 2. Mixed Integer Linear Programming (MILP), Kano model, argumentation systems and graph tool such as Computer Aided Design (CAD) that are related to computational services are used with QFD to optimize the product development and decision-making [9,10,11,12]. 3. QFD is used with Kansei engineering methodology and Kano model to satisfy customer needs [13, 14]. 4. Fuzzy logic is applied with QFD and other tools such as Linear Programming (FLP) model, competitive analysis, Least-Squares Regression (FLSR) approach, Failure Modes and Effect Analysis (FMEA) and nonlinear programming models for maximizing customer satisfaction, optimizing the values of engineering characteristics and finding the optimal target values of the engineering characteristics [15, 16, 17]. The researcher has presented many researches about improving quality, product planning, cost, consumer needs, reliability, maintenance, sales, decision making and other. But they did not take into consideration the need to develop a product to compete with other competition products in its other markets, under the conditions of development permitted in the company without the need for additional investment, in addition to the maintaining on the satisfactions of customers in its market and competition in it at the same time. 3. Proposed Methodology Models In the formalizing and constructing the proposed methodology which shown in figure (1). The condition (A) (percentage of permitted change) in engineering specifications of current product should not exceed the percentage, that determined by the technical expert of QFD team in the factory, which is required the development of the current product for the purpose of competition with similar products in other markets. The main reasons of choosing the fuzzy logic in improving the product planning with QFD: 1. To get the optimum flexibility of technical ratings for each product specification. 2. To convert the fuzzy values of new engineering specifications to a crisp values. 3. To nomination the basic engineering specifications (critical) which should be considered in product development. 4. To reduce the design uncertainties. - 99 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 Symmetrical triangular fuzzy numbers is suitable for using in QFD. In this research, a special program has been done for the purpose of calculating all the mathematical operations in the practical application for six models in the proposed methodology through the use of MATLAB. 3.1. Current Crisp QFD Model In this model, traditional status by using the crisp numbers so to obtain a technical rating for each engineering term of the product, in order to find out which of the engineering specifications of the product is necessary, and should be considered and improved for competition purposes in the original market(a). The engineering specifications of little importance that could be disregarded in development process. This model consists of (13) steps as shown in figure (2). Figure 2. Steps of current crisp QFD model - 100 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 Figure 2. Steps of current crisp QFD model 3.2. Virtual Crisp QFD Model In this model, the traditional QFD principles will be implemented on the competitive products in the competitive markets, where a first competing market will be marked with the letter (b) and the second competing market with (c), to find out the values of the engineering specifications that should be provided in the product which are required to be developed to meet the customer's requirements in the markets (b) and (c) together. The benefit of this model is to obtain an engineering specifications for virtual product, which meet the requirements of the customer in the markets where competition is only required in these markets. This model consists of (12) steps as shown in figure (3). Figure 3. Steps of virtual crisp QFD model 3.3 Initial Fuzzy QFD Model Based on comparison results of the previews models (model-1, model-2), there are two possibilities: First, there is no need to utilize fuzzy logic model when the Percentage Difference between first and second model (DI) of HOWs is (≤) than Percentage of permitted change in engineering specifications (A) for specifications of current product in the original factory. - Second, when (DI) of HOWs is (>) than (A), there is a need for applications of fuzzy logic models. Model three shown in figure (4) and consists of (6) steps. - - 101 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 Figure 4. Steps of initial fuzzy QFD model 3.4. Final Fuzzy QFD Model In this model, a fuzzy model will be proposed for the markets where the competition is required to get new technical rating to the virtual product in fuzzy state, which suggested in the area of competitors markets. The intention from that is to find out the values of the new engineering specifications that must to be achieved in the new product to meet the customers' requirements in the competitors markets. This model includes four steps shown in figure (5). Figure 5. Steps of final fuzzy QFD model 3.5. Optimum Fuzzy QFD Model The fuzzy logic principles are applied to identify the optimum flexibility values in engineering specifications for the newly developed product, when obtain at shared area between the triangles of technical rating in model-3 and model-4. The fuzzy final technical rating for the newly developed product will establish in model-5, in order to obtain the engineering specifications of new developed product, that should be reached with short time and achieve the customer's desires in the markets (a), (b) and (c) together. In this model, one of the operations that is related with fuzzy logic, which an intersection operation is applied. Where a shared area will be found from resulting values from the third and fourth models to reach the new triangular fuzzy numbers for each HOW, and these numbers are considered the limits of optimum value for each HOW. This model will be applied in conditions, involving the use of three values for confidence levels (α- cut) in membership function; they are (0.8, 0.5 and 0.2) which represent good confidence, average confidence and low confidence respectively, that has been limited to these three values only. Thus, with these three values, all the other confidences have been covered in membership function. This model consists of (4) steps, that shown in figure (6): - 102 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 Identify the Optimum (α- cut) that take two conditions (first: intersection between model 3&4) and (second: highest confidence) Calculate Lower Limit Point (LL) of Optimum Fuzzy Technical Rating (OFTR) Calculate Crisp Limit Point (CL) of Optimum Fuzzy Technical Rating (OFTR) Calculate Upper Limit Point (UL) of Optimum Fuzzy Technical Rating (OFTR) Figure 6. Steps of optimum fuzzy QFD model Step-1: Identify the optimum of (α- cut) that equals at the highest value of intersection levels (α- cut) between triangles (FITR) a and (FFTR) b+c as shown in figure (7), by using the following equation [18]. μT (FITR) a ∩ μT (FFTR) b + c ≠ 0 (1) where: μT = value of (α- cut) intersection level at (0.2, 0.5 or 0.8) Figure 7. OFTR Triangle Step-2: Calculate the lower limit point for the Optimum Fuzzy Technical Ratings Triangle (OFTR) by using the following slope equation. (OFTR)LL= (α-cut). [(CTR) a-(FITR) a LL]+ (FITR) a LL (2) Where: LL= Lower limit point Step-3: Calculate the crisp limit point for OFTR from the following slope equations. α- cut CL= (CL - (FITR) a LL) / [(CTR) a - (FITR) a LL] - 103 - (3) Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 α- cut CL= (CL - (FFTR) (b + c) UL)/ [(CTR) (b + c) - (FFTR) (b + c) UL)] (4) where: CL= Crisp limit point for OFTR UL= Upper limit point Step-4: Calculate the Upper limit point for OFTR from the following slope equation. (OFTR)UL= (α-cut). [(CTR) (b+c)-(FFTR) (b+c) UL]+ (FFTR) (b+c) UL (5) 3.6. Optimum Crisp QFD Model In this model the optimum fuzzy model will be transferred to optimum crisp model, to obtain crisp numbers which enable designers and engineers to use it during the operation of improving the product planning phase in the process of realization the new product. The operation of transferring the optimum fuzzy technical ratings to optimum crisp technical ratings is done by using one of the defuzzified methods, namely the centroid method. This model consists of (5) steps, shown in figure (8): Calculate Optimum Crisp Technical Rating (OCTR) by using Centoid Method Calculate Normalized Optimum Crisp Technical Rating (NOCTR) Calculate Scaled Optimum Crisp Technical Rating (SOCTR) Calculate Percentage Difference (DF) between (NCTR) for model-1 and (NOCTR) for model-6 Calculate New Product Specification (NPS) Figure 8. Steps of optimum crisp QFD model Step-1: Defuzzify (OFTR) to obtain the Optimum Crisp Technical Ratings (OCTR) by using Centroid method equation (6) [19]: R R (OCTR) j = ∑ d j. µ j / ∑ µ j r =1 (6) r =1 where: d = crisp value for triangle OFTR µ = degree of membership function for OFTR r = 1,2,…..,R R = No. of fuzzy values in OFTR j = 1, 2,………., p p = No. of HOWs Application of this formula (6) on a triangle OFTR as shown in the following: (OCTR) j = [(OFTRLL.µLL) + (CL.α-cutCL) + (OFTRUL.µUL)]/ [µLL+α-cutCL+µUL] Step-2: Calculate the Normalized Optimum Crisp Technical Ratings (NOCTR) for each HOW by using the following equation [20]. - 104 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 P (NOCTR) j = (OCTR) j / ∑ (OCTR) j (7) j =1 Step-3: Calculate the Scaled Optimum Crisp Technical Ratings (SOCTR) to find optimum ranking for each HOW in markets (a, b and c) together by using the following equation [21]. (SOCTR) j = (OCTR) j / max. (OCTR) j (8) Step-4: Make a comparison between the results from actual crisp model for current product in market (a) and optimum crisp model for future products in markets (a, b and c) together in order to obtain the percentage difference (DF) between (NCTR) a and (NOCTR) by using equation (9). (DF) j = [(NCTR) j a - (NOCTR) j]. 100 % / (NCTR) j a (9) Step-5: Translate the optimum crisp technical ratings (NOCTR) for HOWs in markets (a, b and c) to new values of the target design that represent the New Product Specifications (NPS) to obtain new development product by using equation (10): - (NCTR) a represents the current product specifications. - NOCTR represents the new product specifications. Equation (10) is obtained from using equation (9) NPS j =PS j – [PS j. DF j] (10) where: - NPS = new product specifications - PS = current product specifications 4. Case Study To enhance the product planning by using QFD with fuzzy logic, the proposed methodology was used. The selected product was (Woolen Blanket) as one of the products of the state company for Wool Industries at Al-Fattah Factory in Baghdad. The condition (A) percentage of permitted change in the engineering specifications of current blanket should not exceed (±5%) that is determined by the technical expert in the AL-Fattah factory. The following models explain the implementation of model5. There is a team of multi occupations including several persons, each of them represents one of the existing sections in the factory, starting with the design department and ending with the marketing department. This team is called the QFD team. WHATs and HOWs of this methodology are presented in tables (1 and 2) that depend on experience of QFD team and the especial questionnaire for customer of this product. (FITR)a represents output of model-3 and (FFTR)b+c represent output of model-4 as shown in table (3). Model-5 is intersection between two models (3 and 4). A stepwise-described algorithm of model-5 is simply presented as follow: Step 1: Determine the optimum of (α- cut) between three values (0.2, 0.5 or 0.7) by using equation (1), as shown in the following. For (H1) by using figure (9): 1- At α- cut= 0.8, There is no intersection between (FITR) a and (FFTR)b+c as shown in the following. μ0.8 (FITR) a ∩ μ0.8 (FFTR) b + c = 0 μ0.8 (0.2280, 1.7500) a ∩ μ0.8 (0.1934, 1.1180) b + c = 0 2- At α- cut= 0.5, There is intersection between (FITR) a and (FFTR) b +c as shown in the following. μ0.5 (FITR) a ∩ μ0.5 (FFTR) b + c ≠ 0 μ0.5 (0.2280, 1.7500) a ∩ μ0.5 (0.1934, 1.1180) b + c ≠ 0 3- At α- cut= 0.2, There is intersection between (FITR) a and (FFTR) b +c as shown in the following. μ0.5 (FITR) a ∩ μ0.5 (FFTR) b + c ≠ 0 - 105 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 μ0.2 (0.2280, 1.7500) a ∩ μ0.2 (0.1934, 1.1180) b + c ≠ 0 But the result in the previous point-3, this intersection that is shown in figure (9) are not used because these result do not represent the (OFTR), and the confidence level at (α- cut= 0.2) is lower than confidence level at (α- cut= 0.5) in degree of membership function. Therefore (α- cut= 0.5) is used to obtain (OFTR) triangle that consists from three point which named (LL, CL and UL). There is need to calculate the three intersection points between the two triangular numbers for (FITR) a and (FFTR) b + c for H1 at (α- cut= 0.5) that represents the OFTR. Step 2: (OFTR) LL is calculated at (α- cut= 0.5) by using equation (2) as shown in table (4). For H1: (OFTR) LL1 = o.5 [(0.8820- 0.2280] + 0.2280 = 0.5550 Step 3: CL is calculated by using equations (3), (4) as shown in table (4). For H1: α- cut CL= (CL- 0.2280)/ (0.8820-0.2280) (3)'' α- cut CL= (CL- 1.1180)/ (0.6228- 1.1180) (4)'' (α- cut) CL = 0.7745 CL = 0.7345 Step 4: Calculate (OFTR) UL at (α- cut= 0.5) by using equation (5) as shown in table (4). For H1: (OFTR) UL = o.5 [0.6228- 1.1180] + 1.1180= 0.8704 Therefore, three points of OFTR triangle are (0.5550, 0.7345, and 0.8704) that shown in table (5). Then calculate (OFTR) for other HOWs according to above steps. The last model in the proposed methodology is the processes for translating the optimum fuzzy model to optimum crisp model. In order to get real numbers which can understand by the designers that concerned with developing the product. Table 1. WHATs of QFD No. 1 2 3 Customer WHATs No dimensions needs (W) . Keep warm W1 Performance Not dying skin or clothes W3 light weight W4 Easy to clean W5 Smoothness W6 Price W7 Keeping the color W8 O W 1 Dimensions ً◌Length 2250 mm H1 Width 1750 mm H2 852 gm/m2 H3 Thickness 5 mm H4 Width of Selvedge 40 mm H5 Percentage of Wool 55 % H6 Percentage of Polyester 15 % H7 Percentage of Cotton 25% H8 2400 ID H9 Square Metric Weight Features Conformance Reliability Long life W9 no defects of W10 fabric-sewing No changes in measurements Nice finish Uniformity in the color of blanket Surface uniformly 5 H Product Specifications (PS) W2 Healthy for use after washing 4 Table 2. HOWs of QFD Quality Aesthetics Texture uniformly Different color options Packaging W11 2 Components 3 Economic Profit per unit W12 Percentage of White Yarn in W13 Blanket Percentage of dark Color Yarn in 4 W14 Surface Design 40 % 80 % Between Yarns W15 W16 Blanket Percentage of Colors Matching 30 % 5 W17 Chemical Properties Fixed Degree of Color 85 % Temperature of washing 90 c○ H1 0 H1 1 H1 2 H1 3 H1 4 - 106 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 Table 3. (FITR) a and (FFTR) b + c H Fuzzy Initial Technical Rating (FITR) a Fuzzy Final Technical Ratings (FFTR) b + c 1 (0.2280 , 1.7500) (0.1934 , 1.1180) 2 (0.3560, 1.9420) (0.2908 , 1.2478) 3 (3.8920, 9.1040) (2.5465 , 5.3438) 4 (2.8920, 7.0680) (1.5306 , 3.8816) 5 (0.1880, 2.1080) (0.0667 , 1.1065) 6 (4.8220, 10.7980) (2.8087 , 6.1151) 7 (1.6600, 5.3360) (0.9755 , 3.0179) 9 (4.1060, 9.6680) (2.3532 , 5.4074) 10 (1.2820, 3.9100) (0.7834 , 2.2154) 11 (1.9360, 5.1660) (1.1078 , 2.8892) 12 (1.6340, 4.8920) (0.8663 , 2.6843) 13 (1.7260, 4.3380) (1.1114 , 2.6042) 14 (2.9160, 6.6940) (1.8349 ,4.0563) Figure 9. OFTR for H1 Table 4. (OFTR) LL , CL, α- cut CL and (OFTR) UL for each HOW H (CTR) a (FITR) a LL (OFTR) LL (FFTR)(b+c)UL (CTR)(b+c) α- cut CL CL (OFTR) UL 1 0.8820 0.2280 0.5550 1.1180 0.6228 0.7745 0.7345 0.8704 1.0480 0.3560 0.7020 1.2478 0.7462 0.7471 0.8730 0.9970 6.4295 3.8920 4.3995 5.3438 4.0348 0.3774 4.8497 5.0820 4.9410 2.8920 3.3018 3.8816 2.7131 0.3076 3.5222 3.6479 1.0375 0.1880 0.6128 1.1065 0.4993 0.6305 0.7236 0.8029 7.7275 4.8220 5.4031 6.1151 4.5295 0.2879 5.6585 5.7979 3.3400 1.6600 1.9960 3.0179 1.9468 0.4936 2.4892 2.8037 5.6770 3.3720 3.8330 4.5874 3.2812 0.3366 4.1478 4.3261 6.7810 4.1060 4.6410 5.4074 3.9082 0.3118 4.9400 5.1076 10 2.4685 1.2820 1.5193 2.2154 1.4680 0.4826 1.8547 2.0659 11 3.4575 1.9360 2.2403 2.8892 1.9772 0.3917 2.5320 2.7068 12 3.1925 1.6340 1.9457 2.6843 1.7177 0.4159 2.2822 2.4910 13 2.9085 1.7260 1.9625 2.6042 1.8899 0.4630 2.2735 2.4613 14 4.6670 2.9160 3.2662 4.0563 3.0026 0.4066 3.6279 3.8456 2 3 4 5 6 7 8 9 - 107 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 Table 5. Results for optimum fuzzy model in markets (a, b and c) 2 Degree of membership function for OFTR (0.5000,0.7745,0.500 (0.5000,0.7471,0.500 Optim um α - cut 0.5 0.5 Optimum Fuzzy Technical Ratings OFTR (0.5550,0.7345,0.8 (0.7020,0.8730,0.9 3 (0.2000,0.3774,0.200 0.2 (4.3995,4.8497,5.0 4 (0.2000,0.3076,0.200 0.2 (3.3018,3.5222,3.6 5 (0.5000,0.6305,0.500 0.5 (0.6128,0.7236,0.8 6 (0.2000,0.2879,0.200 0.2 (5.4031,5.6585,5.7 7 (0.2000,0.4936,0.200 0.2 (1.9960,2.4892,2.8 8 (0.2000,0.3366,0.200 0.2 (3.8330,4.1478,4.3 9 (0.2000,0.3118,0.200 0.2 (4.6410,4.9400,5.1 10 (0.2000,0.4826,0.200 0.2 (1.5193,1.8547,2.0 11 (0.2000,0.3917,0.200 0.2 (2.2403,2.5320,2.7 12 (0.2000,0.4159,0.200 0.2 (1.9457,2.2822,2.4 13 (0.2000,0.4630,0.200 0.2 (1.9625,2.2735,2.4 14 (0.2000,0.4066,0.200 0.2 (3.2662,3.6279,3.8 H 1 5. Conclusions QFD is an improvement tool that should enable companies to achieve high competitiveness. However, tools alone cannot provide results by themselves. The conclusions of this paper regarding the utilizing of fuzzy logic into QFD for improving the product during the planning phase , enabling more effective product development and obtaining real crisp values for product specifications that can use by the product designers.. QFD is widely used in the different manufacturing companies, and can be employed to develop a framework for the new product development process. Furthermore, customer needs should be linked to product development to increase the product’s competitiveness. The benefit of the proposed methodology, were able to find out the critical and important specifications for improving product planning which should be considered in product development stage. The future work of this paper may be applied this proposed methodology to other sectors such as service to find out the extent of its advantage. 6. Acknowledgement The authors are grateful to Al-Fattah Factory for woolen blanket. 7. References [1] Bergman B. and Klefsjo B., "Quality from Customer Needs to Customer Satisfaction", McGrawHill Book Company, New York, USA, 1994. [2] Chen Y., Fung R. Y.K. and Tang J., ''Rating Technical Attributes in Fuzzy QFD by Integrating Fuzzy Weighted Average Method and Fuzzy Expected Value Operator'', European Journal of Operational Research 174, p. 1553-1566, 2006. [3] Nukala S. and Gupta S. M., "Effective Marketing of a Closed-Loop Supply Chain Network: A Fuzzy QFD Approach", Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing VI, Boston, Massachusetts, pp. 165-171, October 1-3, 2006. [4] Chan L-K. and Wu M-L., ''Quality Function Deployment: A Literature Review'', European Journal of Operational Research 143, p.463-497, 2002. [5] Besterfield D. H., Besterfield-Mivhna C., Besterfield G. H. and Besterfield-Sacre M., ''Total Quality Management'', Book, Third Edition, Pearson Prentice Hall, New Jersey, USA, 2003. [6] Hepler C. W. and Mazur G. H., "Predicting Future Health Insurance Scenarios Using Quality Function Deployment (QFD) and Analytic Hierarchy Process (AHP)", 20th Symposium on Quality Function Deployment,2008. - 108 - Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011 [7] Zhou X. and Schoenung J. M., ''An Integrated Impact Assessment and Weighting Methodology: Evaluation of the Environmental Consequences of Computer Display Technology Substitution'', Journal of Environmental Management, 2006. [8] Kahraman C., "Prioritizing Design Requirements Based on Fuzzy Outranking Methods", Journal of Applied Computational Intelligence, p 489-494, 2009. [9] Zither R. E. and Mazur G. H., ''The Kano Model: Recent Developments'', the Eighteen Symposium on Quality Function Deployment, Austin, Texas, December 2, 2006. [10] Delice E. K. and Gungor Z.," A New Mixed Integer Linear Programming Model for Product Development Using Quality Function Deployment", Computers& Industrial Engineering, March, 2009. [11] Reich Y.," Computational Quality Function Deployment is Knowledge Intensive Engineering, Cite Seer Computer and Information Science Publications Collection,2009. [12] Chen L-S. and Ko W-H., "Fuzzy Approaches to Quality Function Deployment for New Product Design", Elsevier Ltd, December, 2008. [13] Brotchner J. and Mazur G. H.,'' Brand Engineering Using Kansei Engineering and QFD'', QFD Institute, 1999. [14] Mazur G. H.,'' Life QFD: Incorporating Emotional Appeal in Product Development'', The 17th Symposium on Quality Function Deployment, Portland, 2005. [15] Zhaoling L., Qisheng G. and Dongling Z.," Product Design on the Basis of Fuzzy Quality Function Deployment", Journal of Systems Engineering and Electronics Volume 19, Issue 6, December, Pages 1165-1170, 2008. [16] Kwong C. K., Chen Y., Chan K. Y. and Luo X.," A Generalised Fuzzy Least-Squares Regression Approach to Modelling Relationships in QFD", Journal of Engineering Design, 08 December, 2008. [17] Chen L-H. and Ko W-C.," Fuzzy Linear Programming Models for New Product Design Using QFD with FMEA", Applied Mathematical Modelling, Volume 33, Issue 2, February, Pages 633647, 2009. [18] Nisaif A. H., "Fuzzy Logic Use in Evaluation of Security for Public-Key Crypto-System", M.Sc thesis, the Science College, The University Mustansiriya, Baghdad, Iraq, 2006. [19] Jawad S. F., "Complexity Evaluation of Binary Pseudo Random Sequences Using Fuzzy Logic", M.Sc thesis, The Science College, AL Mustanserya University, Baghdad, Iraq, 2004. [20] Plura J., ''Advanced Application of QFD for Customer Requirements Transformation to the New Product Quality Characteristics'', Seminar, 2001. [21] Chan L-K. and Wu M-L., "A systematic Approach to Quality Function Deployment with a Full Illustrative Example", The International Journal of Management Science, Omega 33, p. 119-139, 2005. - 109 -

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