Chapter two Literature Review Quality Function Deployment QFD was developed by Yoji Akao in Japan in 1966

Chapter two
Literature Review
Quality Function Deployment
QFD was developed by Yoji Akao in Japan in 1966 (Akoa .Y ., 1997). Described QFD method as an attempt to build a structured method to deploy flexibility related CRs in the features of various manufacturing systems. The unique approaches of its ability integrate CNs with PTRs. It helps the cross-functional team to make the key tradeoffs between the customers’ needs and the technical requirements so as to develop a high quality service or product.
QFD as it is commonly known is a process that provides structure to the development cycle. This structure ‘can be’ likened to the framework of a house. The foundation is customer requirements.
The frame consists of the planning matrix, which includes items such as the importance rating, customer-perceived benchmarking, sales point, and scale-up factors. The second floor of the house includes the technical features. The roof is the trade-off of technical features. The walls are the interrelationship matrix between the CRs and the technical characteristics. Other parts can be built using things such as new technologies, functions, technical characteristics, processing steps, importance ratings, competitive analysis, and sales points. The components utilized are dependent upon the scope of the project (Akao, 1990).
The QFD process starts with the identification of the needs of the customers. A customer need is a description, in the customer’s own words, of the benefit to be fulfilled by a product. Identifying and prioritizing CNs are extremely important for effective product design because, generally, consumers evaluate product(s) on more than one criterion. Therefore, the QFD process starts with the collection of qualitative and/or quantitative information from the customer about their needs and preferences (Cengiz et al., 2004)5 (Cengiz .K, 2004)
In general, QFD facilitates organization:
1) Understanding the actual requirements of customers,
2) Prioritizing CRs in order of importance from the customer’s point of view,
3) Communicating among team members in order to ensure decision making and reducing loss of data,
4) Designing the products which meet or exceed CRs,
5) Planning or selecting the product design strategically (Han et al., 2001)13 (Hunt. R, 2003).
When the firm adopts the QFD approach it directly affects the product life cycle (PLC) and product/process development cycle (Vivianne ;Hefin, 2000)23 (Vivainne. B, 2000). It is very clear that QFD use the
VOC; the CNs and requirements changes continuously and firm have to respond accordingly. The firm introduces a new product in market and customers get satisfied with the product design and features certainly the product will move towards the growth stage. As the growth stage is going to get completed the customer’s needs and requirements start changing and customer wants something new with more features. Now at this time if the organizations do not respond to the customer changing needs in a timely manner the product will eventually move to the maturity and then declining stage. So if the firm wants to remain in market it has to design product according to the customer wants. When appropriately applied, QFD has demonstrated the reduction of development time by one-half to one-third (Akao, 1990)21 (Aguilar-Lasserre, 2009) (Akao, 1990).

Figure 1 QFD matrix form (Akao, 1990)
QFD Applications in Product Optimization
QFD was originally developed and implemented in Japan at the Kobe Ship yards of Mitsubishi Heavy Industries in 1972. It was observed that Toyota was able to reduce start up pre-production costs by 60% from 1977 to 1984 and to decrease the time required for its development by one-third through the use of QFD (Hauser and Clausing1988)11 (Hauser. JR, 1988). Early users of QFD include Toyota, Ford Motor Company, Procter, 3M Corporation, Gamble, AT&T, Hewlett Packard, and Digital Equipment Corporation, etc. (Cohen 1995)8 (Cohen, 1995). Besides, the American Supplier Institute(ASI) in Dearborn, Michigan and GOAL/QPC (Growth Opportunity Alliance of Lawrence/Quality Productivity Center) in Methuen, Massachusetts have been the primary organizations offering an overview and workshop type training since QFD was introduced to the United States in the early 1980s (Prasad 1998)19 (Review of QFD and related deployment techniques , 1998).QFD was originally proposed, through collecting and analyzing the voice of customer, to develop products with higher quality to meet or surpass customer’s needs.
Thus, these primary functions of QFD are product development, quality management, and customer need analysis. Later QFD’s functions had been extended to wider field such as design, planning, decision-making, engineering, management, teamwork, timing and costing (Chan and Wu 2002)6 (Quality Function Deployment A literature Review, 2002).QFD is a useful tool for developing the requirements of new products, and its benefits are well documented (Causing and Cohen 1994, Cohen 1995, Hauser and Causing 1988, King 1989)781116 (Chohen, 1994) (Cohen, 1995) (Hauser. JR, 1988) (King.B, 1989).QFD is a customer-driven design process. Its use is essential in product design. Sullivan defines QFD as an overall concept that provides a means of translating customer requirements into the appropriate technical requirements at each stage of product development and production (i.e. marketing, planning, and product design, and engineering prototype evaluation, production process development, production sales). Many QFD methodology development and applications have been published by (Kim, Lai et al., 2007)15 (Kim. k, 1997). Various applications within the literature can be grouped under three categories as: QFD implementations before the design stage; QFD implementations during the design stage and QFD implementations after the design stage (Dikmen, et al. 2005)9 (Dikmen I, 2005).
QFD Structure
Phases of QFD:
The QFD system consists of the following four interlinked phases (Cengizetal, 2004)5 (Cengiz .K, 2004), as shown in figure 2.5 below.
Phase I-The first phase of QFD system is HOQ. This translates CNs (WHATs) into engineering characteristics (ECs) the HOWs.
Phase II-QFD second phase is parts deployment, which translates key ECs (new WHATs) determined in the previous phase into parts characteristics (HOWs).
Phase III- Process planning, which translates key parts characteristics (new WHATs) obtained in the previous stage into process operations (HOWs). During process planning, manufacturing processes are flowcharted and process parameters (or target values) are documented.
Phase IV-Finally, the company needs the right production plan to get the processes to run effectively and efficiently. This results in the last phase, production planning, which translate key process operations (new WHATs) into day-to-day production requirements (HOWs).

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Figure 2 the four phases of QFD

2.2.2Steps to the House of Quality
Step 1: Customer Requirements – “Voice of the Customer” The first step in a QFD project is to determine what market segments will be analyzed during the process and to identify who the customers are. The team then gathers information from customers on the requirements they have for the product or service. In order to organize and evaluate this data, the team can use simple quality tools like Affinity Diagrams or Tree Diagrams.
Step 2: Regulatory Requirements Not all product or service requirements are known to the customer, so the team must document requirements that are dictated by management or regulatory standards that the product must adhere to.
Step 3: Customer Importance Ratings On a scale from 1 – 5, customers then rate the importance of each requirement. This number will be used later in the relationship matrix.
Step 4: Customer rating of the Competition Understanding how customers rate the competition can be a tremendous competitive advantage. In this step of the QFD process, it is also a good idea to ask customers how your product or service rates in relation to the competition. There is remodeling that can take place in this part of the House of Quality. Additional rooms that identify sales opportunities, goals for continuous improvement, customer complaints, etc., can be added.
Step 5: Technical Descriptors – “Voice of the Engineer” The technical descriptors are attributes about the product or service that can be measured and benchmarked against the competition. Technical descriptors may exist that your organization is already using to determine product specification, however new measurements can be created to ensure that your product is meeting customer needs.
Step 6: Direction of Improvement As the team defines the technical descriptors; a determination must be made as to the direction of movement for each descriptor.
Step 7: Relationship Matrix The relationship matrix is where the team determines the relationship between customer needs and the company’s ability to meet those needs. The team asks the question, “What is the strength of the relationship between the technical descriptors and the customer’s needs?” Relationships can either be weak, moderate, or strong or carry a numeric value of 1, 3 or 9.
Step 8: Organizational Difficulty Rate the design attributes in terms of organizational difficulty. It is very possible that some attributes are in direct conflict. Increasing the number of sizes may be in conflict with the company’s stock holding policies, for example.
Step 9: Technical Analysis of Competitor Products to better understand the competition, engineering then conducts a comparison of competitor technical descriptors. This process involves reverse engineering competitor products to determine specific values for competitor technical descriptors.
Step 10: Target Values for Technical Descriptors At this stage in the process, the QFD team begins to establish target values for each technical descriptor. Target values represent “how much” for the technical descriptors, and can then act as a base-line to compare against.
Step 11: Correlation Matrix This room in the matrix is where the term House of Quality comes from because it makes the matrix look like a house with a roof. The correlation matrix is probably the least used room in the House of Quality; however, this room is a big help to the design engineers in the next phase of a comprehensive QFD project. Team members must examine how each of the technical descriptors impacts each other. The team should document strong negative relationships between technical descriptors and work to eliminate physical contradictions.
Step 12: Absolute Importance Finally, the team calculates the absolute importance for each technical descriptor. This numerical calculation is the product of the cell value and the customer importance rating. Numbers are then added up in their respective columns to determine the importance for each technical descriptor. Now you know which technical aspects of your product matters the most to your customer!( houser & causig,. 1988)11 (Hauser. JR, 1988)
The following figure shows some steps of quality function deployment.

Figure 3 steps of Quality function deployment

Methods for Assessing User Needs
In contrast, in today’s dynamic environment with enormous changes in user needs and expectations, utmost technological advancements, growing international competition and decreasing product life cycles, the only way for companies to survive is a good coupling of thoroughly understanding user needs with an awareness of technological possibilities (Crush,2000; Holt et al., 1984)12 (Holt K., 1984). To understand the real needs of the users, it is needed to apply systematic, well-defined procedures and ‘methods’ through the process of collecting need related information. Considering the large number of methods, Holt et al. (1984)12 (Holt K., 1984) classify these methods into three categories:
2.2.1 Utilization of Existing Knowledge: this is relatively cheap way of obtaining information about user needs. The major problems are to locate the most important sources, to train and make those involved need- conscious, and to develop and maintain a practical procedure for systematization, registration, and utilization of relevant data.
2.2.2 Generation of New Information: this approach requires a relatively great effort and therefore a more expensive way of assessing user needs. One has to plan and implement special activities in order to provide the information. On the other hand, the information acquired in this way is usually more complete and reliable.
2.2.3 Provision of Need Information by other Methods: this group includes informal approaches, i.e. information related to user needs obtained by informal contacts with knowledgeable persons, and ‘environment-related methods’ such as product safety analysis, ecological analysis, and resource analysis (Holt et al., 1984)12 (Holt K., 1984).
The first step towards understanding CNs is to identify attributes and customer consequences.
Attributes are defined as the physical or abstract characteristics of a service or product. They are objective, measurable, and reflect the provider’s perspective. Consequences are a result of using attributes; basically, an end result in what a customer “gets” from using a service or product.
Customers judge services and products based on their consequences, not their attributes. In other words, customers judge a service or product on its outcome, or effect of use on them. A service or product has many attributes, and each may have more than one consequence (Fisher and Schutta,2003)10 (Fisher, 2003).
The VOC is a term used in business to describe the process of capturing customer s’ requirements. The VOC is a product development technique that produces a detailed set of customer wants and needs which are organized into a hierarchical structure, and then prioritized in terms of relative importance and satisfaction with current alternatives (Hauser, 1991)11 (Hauser. JR, 1988).
The VOC process has important outputs and benefits for product developers (optimizers). VOC provides:
A detailed understanding of the customer’s requirements.
A common language for the team going forward.
Key input for the setting of appropriate design specifications for the new product or service.
A highly useful springboard f or product innovation.
There are four aspects of the VOC – CNs, a hierarchical structure, priorities, and customer perceptions of performance. Customers continually want more reliable, durable products and services in a timely manner. In order to remain competitive, all organizations must become more responsive to customers, QFD has been widely used to capture the VOC and translate it into technical requirements in the development of products and services. It is a link between product or service development and technical specifications to achieve customer satisfaction. Applications of QFD range from product development, service development, and product re-projecting (Carnevalli& Miguel, 2008)4 (REview Analysis and Classification of the Literature on Quality function deployement – Types of research ,Difficulity and Benefits, 2008)
Analytic Hierarchy Process (AHP)
The analytic hierarchy process (AHP), an important mathematical method introduced by Saaty (1977, 1980, 2000),212022 (Saaty. T, 1980) (A scaling Method for priorities in Hierarchal structures,, 1977) (Saaty. T, 2000) has been accepted as a leading and flexible modeling methodology and applied to lots of research aspects for the resolution of complex problems (Zahedi 1986, Sundarraj 2004)2526 (Zahedi., 1986) (Sudarraj. R B). AHP has great advantage over other mathematical models or methods, which is reflected in the consideration of subjective and judgmental information from both practitioners and academics, and the solution of discrete multiple criteria decisive problems. Meanwhile, AHP application in pest management has not yet been reported. Traditional ratio of cost to profit on the basis of economics (Kahraman, et al. 2000, Lee 2005) 1417 (Justification of manufucturing technologies using fuzzy benefit/cost ratio analysis , 2000) (Lee.H, 2005)has been applied in many aspects to evaluate the superiority of different strategies. Traditional pest management strategy is also centered on economy. However, the complex system including economic, social and ecological systems should be taken into consideration as the object instead of economic system only, regardless of any method of pest management. As we all know, every strategy has both cost and profit to the complex eco-system. Here, we suggest that the positive effect of different strategies used in the complex system be called Comprehensive Profit (CP)—including economic, ecological and social profit. The negative effect can be called Comprehensive Cost (CC)—including economic, ecological and social cost. An index system of CP and CC can be constructed accordingly. To evaluate the superiority of different strategies, an index of Ratio of Comprehensive Cost to Comprehensive Profit(RCCCP) is presented, and a RCCCP model is constructed based on the AHP. This produced the matrix of RCCCP, where the RCCCP index matrix Wcc/Wcp is defined as the index optimization matrix of CC divided by the index optimization matrix of CP.
The RCCCP model with AHP is used to evaluate the priority of different pest-control measures in IPM. Theoretically, the lower the value of RCCCP is the more superior the corresponding strategy is. The strategy with the lowest value should be accepted and applied in management practices.
The three Basic principles of AHP
• The hierarchy construction principle: The AHP underlying assumption is that complex systems can be better understood through decomposition into essential elements. These elements can be the criteria involved in the considered decision problem, and be hierarchically structured into several levels, according to the relative importance of each element with respect to another one. The highest level represents the main decision objective, while the lowest one is constituted by the different alternatives.
• The priority setting principle: Human beings are able to intuitively perceive relationships between two elements, to express a preference of one on the other and to numerically evaluate this preference. This is still true regarding subjective considerations, since the idea is to translate a feeling. However, a fixed priority scale must be implemented in order to make the evaluation independent from the different orders of magnitude that characterize each element. From the synthesis of this pair wise judgment set is derived the priority scale between all the considered elements.
• The logical consistency principle: The comparisons evoked in the previous paragraph must respect one constraint, namely transitivity. For instance, considering three events A, B and C, if A is better than B and B better than C, then A must be better than C. Moreover, if A is twice better than B and B is three times better than C, then A must be six times better than C: this would constitute a perfectly consistent judgment. Nevertheless, perfect consistency cannot be expected because of the subjective character of the evaluated comparisons and of the changing circumstances: for instance, the same decision-maker might express different choices at two different moments. The AHP technique thus involves quantitative and qualitative aspects into a unique analysis structure in order to convert the natural thoughts of any human being into an explicit process. This latter is implemented in a decision-support tool that provides objective and reliable results, even under different scenarios occurrence. It is worth noting that, being subjective the perceptions of the priority scale provider (i.e. the manager), the AHP method does not integrate the possible existence of an “always true, correct, immutable” decision. The AHP main steps include (Wang 2007)25 (Selection of optimum Hierarchy process, 2007):
Hierarchy design step: All the elements interfering into the decision-making problem must be determined and structured into levels as a family tree. The first level consists of the primary or main objective while the following ones are devoted to the secondary aims, etc. In the lowest level are the alternatives, i.e. the possible solutions of the multi-criteria problem (and so, in the case considered in the study, the non-dominated solutions provided by the Pareto sort): this phase allows clarifying the problem components and their interaction.
Development of judgment matrices: One of the main features of the AHP technique is its pair wise comparison working mode, for all the criteria (or alternatives) belonging to the same hierarchical level. Judgment matrices can then be defined from these reciprocal comparisons. The pair wise comparisons are based on a standardized evaluation schemes (cf. next subsection).
Computing of local priorities: Several methods for deriving local priorities (i.e. the local weights of criteria or the local scores of alternatives) from judgment matrices have been developed, such as the eigenvector method (EVM), the logarithmic least squares method (LLSM), the weighted least squares method (WLSM), the goal programming method (GPM), etc. Consistency check should be implemented for each judgment matrix.
Alternative ranking: An aggregation procedure accounting for all local priorities (thanks to a simple weighted sum) then enables to obtain global priorities regarding the main objective, including global weight of each criteria or global scores of each alternative. The final ranking of the alternatives is determined on the basis of these global priorities.
Steps of AHP
The research uses the following AHP steps.
identifying customer requirements and specifying the solution desired
organize the customer requirements as hierarchy from general to detail
Constructing a pair wise comparison matrix of each customer requirements on each other. in this matrix pair of requirements (needs) is compared with the other based on the result gained from the customer survey that have the most important and judgment of the research team with some selected Experienced customers and company workers.
Having collected all pair wise comparison data and entered to reciprocals together with unit entries down the main diagram and the priorities are obtained and normalized weight is gained.
Use hierarchal comparison (synthesis) to weight the vectors of priorities by the weight of the requirements.
Evaluate consistency from the hierarchal result and finally scale the customer requirements importance using frequency distribution method in to 1-5 scale.
Computational Details
Assume that n decision factors are considered in the quantification process of the relative importance of each factor with respect to all the other ones. This problem can be set up as a hierarchy as explained in the previous section. The pair wise comparisons will then be made between each pair of factors at a given level of the hierarchy, regarding their contribution toward the factor at the immediately above level. The comparisons are made on a scale of 1–9, as shown in table 6. This scale is chosen to support comparisons within a limited range but with sufficient sensitivity (a psychological limit for the human beings to establish quantitative distinction between two elements was proved by psychometric studies). These pair wise comparisons yield a reciprocal (n,n)-matrix A, where aii=1 (diagonal elements) and aji=1/aij.
Table 1Pair wise comparison of requirements for AHP (A scaling Method for priorities in Hierarchal structures,, 1977)
Values of aij Interpretation
1 Characteristics i and j are of equal importance
3 Characteristics i is weakly more important than Characteristics j
5 Experience and judgment indicate that Characteristics i is strongly
more important than Characteristics j.
7 Characteristics i is very strongly or demonstrably more important
than Characteristics j.
9 Characteristics i is absolutely more important than Characteristics j.
2,4,6,8 Intermediate values—for example, a value of 8 means that
Characteristics i is midway between strongly and absolutely more
important than Characteristics j.

Suppose that only the first column of matrix A is provided to state the relative importance of factors 2,3,…,n with respect to factor 1. If the judgments were completely consistent, then the remaining columns in the matrix would be completely determined due to the transitivity of the relative importance of the factors. However, there is no consistency except for that obtained by setting aji=1/aij. Therefore, the comparison needs to be repeated for each column of the matrix, and i.e. independent judgments must be made over each pair. Suppose that after all the comparisons are made, the matrix A includes only exact relative weights.
Multiplying the matrix by the vector of weights w=(w1,w2,…,wn) yields:
?(a11&a12&…&[email protected]&a22&…&[email protected]?&?&…&[email protected]&an2&…&ann)?([email protected]@[email protected])=?(w1/w1&w1/w2&…&w1/[email protected]/w1&w2/w2&…&w2/[email protected]?&?&…&[email protected]/w1&wn/w2&…&wn/wn)?([email protected]@[email protected])=n?([email protected]@[email protected])

Therefore, to recover the overall scale from the matrix of ratios, the EVM was adopted. (Zeng, 2007) (Optimization of wastewater treatement alternatives selection by hierarchy grey Relational analysis , 2007). According to the previous equation, the problem can formulate as AW=NWorA-nI=0, which represents a system of homogenous linear equations (I is the identity matrix). This system has a nontrivial solution if and only if the determinant of (A-nI) vanishes, meaning that n is an eigenvalue of A. Obviously, A has unit rank since every row is a constant multiple of the first row and thus all eigenvalues except one are equal to zero. The sum of the eigenvalues of a matrix equals its trace and in this case, the trace of A equals n. So, n is an Eigen value of A and a nontrivial solution. Usually, the normalized vector is obtained by dividing all the entries Wi by their sum. Thus, the scale can be recovered from the comparison matrix. In this exact case, the solution is any normalized column of A. Notably, matrix A in this case is consistent, indicating that its entries satisfy the condition ajk=aji/aki(transitivity property).
Consistency
However, in actual cases, precise values of Wi/Wjare not available, but their estimates, which in general differ from the ratios of the actual weights, are provided by the decision-maker. The matrix theory illustrates that a small perturbation of the coefficients implies a small perturbation of the Eigen values. Therefore, an Eigen value close to n, which is the largest Eigen value max, should be found since the trace of the matrix (equal to n) remains equal to the sum of the Eigen values while small errors of judgment are made and other Eigen values are non-zero. The solution to the problem of the largest Eigen value, which is the weight eigenvector w that corresponds to max, when normalized, gives a unique estimate of the underlying ratio scale between the elements of the studied case. Furthermore, the matrix whose entries are Wi/Wj remains a consistent estimate of the “actual” matrix A which may not be consistent. In fact, A is consistent if and only if max=n. However, the inequality maxes>n always exists. Therefore, the average of the remaining Eigen values can be used as a “consistency index” (CI) which is the difference between max and n divided by the normalizing factor (n-1).
CI=?max-n/(n-1)
The CI of the studied problem is compared with the average RI obtained from associated random matrices of order n to measure the error due to inconsistency, (Saaty 1980)20. As a rule of thumb, a consistency ratio CR=CI/RI value of 10% or less is considered as acceptable, otherwise the pair wise comparisons should be revised,( Aguilar-Lasserre, et al. 2009)1 (Aguilar-Lasserre, 2009)
T-shirt design techniques and quality characteristic
General manufacturing process consist different steps. These are broadly divided in to two categories pre-production and production process. The preproduction consists designing the garment, pattern design, production pattern making, grading and maker making and sample making. Production process consists cutting, stitching, (preparatory and assembly) and finishing all these process are described here.
Registering the design
Raw material selection: the very beginning for quality of t-shirts is selection of material type in considerations of quality parameters. The material types such are the cotton, nylon, Lenin, polystyrene etc are the determinants of t-shirt durability , elasticity of volumes , strengthens and other quality parameter determinants. There is also system of clamps and screws allow for very minute adjustments to the position of the screen. These adjustments are depending on the design features like the color verities. Depend on these the desired t-shirts to produce are:
Cotton t-shirts
Polyester t-shirts
Play cotton mix fabric
Viscose and others
Manufacturing processes
Machine setting techniques: machine setting and adjustments are the other pin points of producing quality t-shirts. From some of the over look machine is used to attach two fabric pieces together like front and back or other t-shirt sides. It provides seam stitch the garment and same machines have additional cutters that cut extra fabric that is left whine the two panels are joined. The additional cutters provide the aesthetic appeal to the garment and helps in the aesthetic appeal of the t-shirt product.
T-shirt manufacturing process: the t-shirt process encompass the grinning, spinning, knitting, dying, pattern making, cutting, stitching, checking and packing.
Design
Garment life expectancy is affected by range of decisions; choice of material & yarn fabric construction and finishing, trimming, garment design (shape) make up. Because these decisions are made at the design stage, production design has been identified as pivotal to determining of t-shirt quality parameters. Designers must be specifies many relevant characteristics of final garment like fashionably and styling.
Fabric finishing: Textile manufacturing process is used to improve the look performance or hand (feel) of the finished textile or clothing these include the mechanical and chemical treatment which produce arrange of effects including:
Fabric stiffing and softening
Scouring and bleaching impacting on fabric quality and feel
Water proofing and strain resistance measures
Pre shrinkage actions
Ant pilling resistance actions and so on
T-shirt quality characteristics
Quality of design requires higher amount of market research to establish the ultimate customer preference at an acceptable price amongst a competitive characteristics; these can form the basis for design. The various combinations of customer demand generate the following t-shirt quality characteristics.
Price and value of many
Individuality of appearance
Fashion is appropriate to the period or groups
Image enhancement (reliability of an executive suit, smartness)
Comfortable in wear both in, cut and fabric
Durability function and appearance
Ease of care (crease and stain resistance, shape retention, washeabilty)
Size and shape
Consistency of product
Chapter two
Literature Review
Quality Function Deployment
QFD was developed by Yoji Akao in Japan in 1966 (Akoa .Y ., 1997). Described QFD method as an attempt to build a structured method to deploy flexibility related CRs in the features of various manufacturing systems. The unique approaches of its ability integrate CNs with PTRs. It helps the cross-functional team to make the key tradeoffs between the customers’ needs and the technical requirements so as to develop a high quality service or product.
QFD as it is commonly known is a process that provides structure to the development cycle. This structure ‘can be’ likened to the framework of a house. The foundation is customer requirements.
The frame consists of the planning matrix, which includes items such as the importance rating, customer-perceived benchmarking, sales point, and scale-up factors. The second floor of the house includes the technical features. The roof is the trade-off of technical features. The walls are the interrelationship matrix between the CRs and the technical characteristics. Other parts can be built using things such as new technologies, functions, technical characteristics, processing steps, importance ratings, competitive analysis, and sales points. The components utilized are dependent upon the scope of the project (Akao, 1990).
The QFD process starts with the identification of the needs of the customers. A customer need is a description, in the customer’s own words, of the benefit to be fulfilled by a product. Identifying and prioritizing CNs are extremely important for effective product design because, generally, consumers evaluate product(s) on more than one criterion. Therefore, the QFD process starts with the collection of qualitative and/or quantitative information from the customer about their needs and preferences (Cengiz et al., 2004)5 (Cengiz .K, 2004)
In general, QFD facilitates organization:
1) Understanding the actual requirements of customers,
2) Prioritizing CRs in order of importance from the customer’s point of view,
3) Communicating among team members in order to ensure decision making and reducing loss of data,
4) Designing the products which meet or exceed CRs,
5) Planning or selecting the product design strategically (Han et al., 2001)13 (Hunt. R, 2003).
When the firm adopts the QFD approach it directly affects the product life cycle (PLC) and product/process development cycle (Vivianne ;Hefin, 2000)23 (Vivainne. B, 2000). It is very clear that QFD use the
VOC; the CNs and requirements changes continuously and firm have to respond accordingly. The firm introduces a new product in market and customers get satisfied with the product design and features certainly the product will move towards the growth stage. As the growth stage is going to get completed the customer’s needs and requirements start changing and customer wants something new with more features. Now at this time if the organizations do not respond to the customer changing needs in a timely manner the product will eventually move to the maturity and then declining stage. So if the firm wants to remain in market it has to design product according to the customer wants. When appropriately applied, QFD has demonstrated the reduction of development time by one-half to one-third (Akao, 1990)21 (Aguilar-Lasserre, 2009) (Akao, 1990).

Figure 1 QFD matrix form (Akao, 1990)
QFD Applications in Product Optimization
QFD was originally developed and implemented in Japan at the Kobe Ship yards of Mitsubishi Heavy Industries in 1972. It was observed that Toyota was able to reduce start up pre-production costs by 60% from 1977 to 1984 and to decrease the time required for its development by one-third through the use of QFD (Hauser and Clausing1988)11 (Hauser. JR, 1988). Early users of QFD include Toyota, Ford Motor Company, Procter, 3M Corporation, Gamble, AT&T, Hewlett Packard, and Digital Equipment Corporation, etc. (Cohen 1995)8 (Cohen, 1995). Besides, the American Supplier Institute(ASI) in Dearborn, Michigan and GOAL/QPC (Growth Opportunity Alliance of Lawrence/Quality Productivity Center) in Methuen, Massachusetts have been the primary organizations offering an overview and workshop type training since QFD was introduced to the United States in the early 1980s (Prasad 1998)19 (Review of QFD and related deployment techniques , 1998).QFD was originally proposed, through collecting and analyzing the voice of customer, to develop products with higher quality to meet or surpass customer’s needs.
Thus, these primary functions of QFD are product development, quality management, and customer need analysis. Later QFD’s functions had been extended to wider field such as design, planning, decision-making, engineering, management, teamwork, timing and costing (Chan and Wu 2002)6 (Quality Function Deployment A literature Review, 2002).QFD is a useful tool for developing the requirements of new products, and its benefits are well documented (Causing and Cohen 1994, Cohen 1995, Hauser and Causing 1988, King 1989)781116 (Chohen, 1994) (Cohen, 1995) (Hauser. JR, 1988) (King.B, 1989).QFD is a customer-driven design process. Its use is essential in product design. Sullivan defines QFD as an overall concept that provides a means of translating customer requirements into the appropriate technical requirements at each stage of product development and production (i.e. marketing, planning, and product design, and engineering prototype evaluation, production process development, production sales). Many QFD methodology development and applications have been published by (Kim, Lai et al., 2007)15 (Kim. k, 1997). Various applications within the literature can be grouped under three categories as: QFD implementations before the design stage; QFD implementations during the design stage and QFD implementations after the design stage (Dikmen, et al. 2005)9 (Dikmen I, 2005).
QFD Structure
Phases of QFD:
The QFD system consists of the following four interlinked phases (Cengizetal, 2004)5 (Cengiz .K, 2004), as shown in figure 2.5 below.
Phase I-The first phase of QFD system is HOQ. This translates CNs (WHATs) into engineering characteristics (ECs) the HOWs.
Phase II-QFD second phase is parts deployment, which translates key ECs (new WHATs) determined in the previous phase into parts characteristics (HOWs).
Phase III- Process planning, which translates key parts characteristics (new WHATs) obtained in the previous stage into process operations (HOWs). During process planning, manufacturing processes are flowcharted and process parameters (or target values) are documented.
Phase IV-Finally, the company needs the right production plan to get the processes to run effectively and efficiently. This results in the last phase, production planning, which translate key process operations (new WHATs) into day-to-day production requirements (HOWs).

Figure 2 the four phases of QFD

2.2.2Steps to the House of Quality
Step 1: Customer Requirements – “Voice of the Customer” The first step in a QFD project is to determine what market segments will be analyzed during the process and to identify who the customers are. The team then gathers information from customers on the requirements they have for the product or service. In order to organize and evaluate this data, the team can use simple quality tools like Affinity Diagrams or Tree Diagrams.
Step 2: Regulatory Requirements Not all product or service requirements are known to the customer, so the team must document requirements that are dictated by management or regulatory standards that the product must adhere to.
Step 3: Customer Importance Ratings On a scale from 1 – 5, customers then rate the importance of each requirement. This number will be used later in the relationship matrix.
Step 4: Customer rating of the Competition Understanding how customers rate the competition can be a tremendous competitive advantage. In this step of the QFD process, it is also a good idea to ask customers how your product or service rates in relation to the competition. There is remodeling that can take place in this part of the House of Quality. Additional rooms that identify sales opportunities, goals for continuous improvement, customer complaints, etc., can be added.
Step 5: Technical Descriptors – “Voice of the Engineer” The technical descriptors are attributes about the product or service that can be measured and benchmarked against the competition. Technical descriptors may exist that your organization is already using to determine product specification, however new measurements can be created to ensure that your product is meeting customer needs.
Step 6: Direction of Improvement As the team defines the technical descriptors; a determination must be made as to the direction of movement for each descriptor.
Step 7: Relationship Matrix The relationship matrix is where the team determines the relationship between customer needs and the company’s ability to meet those needs. The team asks the question, “What is the strength of the relationship between the technical descriptors and the customer’s needs?” Relationships can either be weak, moderate, or strong or carry a numeric value of 1, 3 or 9.
Step 8: Organizational Difficulty Rate the design attributes in terms of organizational difficulty. It is very possible that some attributes are in direct conflict. Increasing the number of sizes may be in conflict with the company’s stock holding policies, for example.
Step 9: Technical Analysis of Competitor Products to better understand the competition, engineering then conducts a comparison of competitor technical descriptors. This process involves reverse engineering competitor products to determine specific values for competitor technical descriptors.
Step 10: Target Values for Technical Descriptors At this stage in the process, the QFD team begins to establish target values for each technical descriptor. Target values represent “how much” for the technical descriptors, and can then act as a base-line to compare against.
Step 11: Correlation Matrix This room in the matrix is where the term House of Quality comes from because it makes the matrix look like a house with a roof. The correlation matrix is probably the least used room in the House of Quality; however, this room is a big help to the design engineers in the next phase of a comprehensive QFD project. Team members must examine how each of the technical descriptors impacts each other. The team should document strong negative relationships between technical descriptors and work to eliminate physical contradictions.
Step 12: Absolute Importance Finally, the team calculates the absolute importance for each technical descriptor. This numerical calculation is the product of the cell value and the customer importance rating. Numbers are then added up in their respective columns to determine the importance for each technical descriptor. Now you know which technical aspects of your product matters the most to your customer!( houser & causig,. 1988)11 (Hauser. JR, 1988)
The following figure shows some steps of quality function deployment.

Figure 3 steps of Quality function deployment

Methods for Assessing User Needs
In contrast, in today’s dynamic environment with enormous changes in user needs and expectations, utmost technological advancements, growing international competition and decreasing product life cycles, the only way for companies to survive is a good coupling of thoroughly understanding user needs with an awareness of technological possibilities (Crush,2000; Holt et al., 1984)12 (Holt K., 1984). To understand the real needs of the users, it is needed to apply systematic, well-defined procedures and ‘methods’ through the process of collecting need related information. Considering the large number of methods, Holt et al. (1984)12 (Holt K., 1984) classify these methods into three categories:
2.2.1 Utilization of Existing Knowledge: this is relatively cheap way of obtaining information about user needs. The major problems are to locate the most important sources, to train and make those involved need- conscious, and to develop and maintain a practical procedure for systematization, registration, and utilization of relevant data.
2.2.2 Generation of New Information: this approach requires a relatively great effort and therefore a more expensive way of assessing user needs. One has to plan and implement special activities in order to provide the information. On the other hand, the information acquired in this way is usually more complete and reliable.
2.2.3 Provision of Need Information by other Methods: this group includes informal approaches, i.e. information related to user needs obtained by informal contacts with knowledgeable persons, and ‘environment-related methods’ such as product safety analysis, ecological analysis, and resource analysis (Holt et al., 1984)12 (Holt K., 1984).
The first step towards understanding CNs is to identify attributes and customer consequences.
Attributes are defined as the physical or abstract characteristics of a service or product. They are objective, measurable, and reflect the provider’s perspective. Consequences are a result of using attributes; basically, an end result in what a customer “gets” from using a service or product.
Customers judge services and products based on their consequences, not their attributes. In other words, customers judge a service or product on its outcome, or effect of use on them. A service or product has many attributes, and each may have more than one consequence (Fisher and Schutta,2003)10 (Fisher, 2003).
The VOC is a term used in business to describe the process of capturing customer s’ requirements. The VOC is a product development technique that produces a detailed set of customer wants and needs which are organized into a hierarchical structure, and then prioritized in terms of relative importance and satisfaction with current alternatives (Hauser, 1991)11 (Hauser. JR, 1988).
The VOC process has important outputs and benefits for product developers (optimizers). VOC provides:
A detailed understanding of the customer’s requirements.
A common language for the team going forward.
Key input for the setting of appropriate design specifications for the new product or service.
A highly useful springboard f or product innovation.
There are four aspects of the VOC – CNs, a hierarchical structure, priorities, and customer perceptions of performance. Customers continually want more reliable, durable products and services in a timely manner. In order to remain competitive, all organizations must become more responsive to customers, QFD has been widely used to capture the VOC and translate it into technical requirements in the development of products and services. It is a link between product or service development and technical specifications to achieve customer satisfaction. Applications of QFD range from product development, service development, and product re-projecting (Carnevalli& Miguel, 2008)4 (REview Analysis and Classification of the Literature on Quality function deployement – Types of research ,Difficulity and Benefits, 2008)
Analytic Hierarchy Process (AHP)
The analytic hierarchy process (AHP), an important mathematical method introduced by Saaty (1977, 1980, 2000),212022 (Saaty. T, 1980) (A scaling Method for priorities in Hierarchal structures,, 1977) (Saaty. T, 2000) has been accepted as a leading and flexible modeling methodology and applied to lots of research aspects for the resolution of complex problems (Zahedi 1986, Sundarraj 2004)2526 (Zahedi., 1986) (Sudarraj. R B). AHP has great advantage over other mathematical models or methods, which is reflected in the consideration of subjective and judgmental information from both practitioners and academics, and the solution of discrete multiple criteria decisive problems. Meanwhile, AHP application in pest management has not yet been reported. Traditional ratio of cost to profit on the basis of economics (Kahraman, et al. 2000, Lee 2005) 1417 (Justification of manufucturing technologies using fuzzy benefit/cost ratio analysis , 2000) (Lee.H, 2005)has been applied in many aspects to evaluate the superiority of different strategies. Traditional pest management strategy is also centered on economy. However, the complex system including economic, social and ecological systems should be taken into consideration as the object instead of economic system only, regardless of any method of pest management. As we all know, every strategy has both cost and profit to the complex eco-system. Here, we suggest that the positive effect of different strategies used in the complex system be called Comprehensive Profit (CP)—including economic, ecological and social profit. The negative effect can be called Comprehensive Cost (CC)—including economic, ecological and social cost. An index system of CP and CC can be constructed accordingly. To evaluate the superiority of different strategies, an index of Ratio of Comprehensive Cost to Comprehensive Profit(RCCCP) is presented, and a RCCCP model is constructed based on the AHP. This produced the matrix of RCCCP, where the RCCCP index matrix Wcc/Wcp is defined as the index optimization matrix of CC divided by the index optimization matrix of CP.
The RCCCP model with AHP is used to evaluate the priority of different pest-control measures in IPM. Theoretically, the lower the value of RCCCP is the more superior the corresponding strategy is. The strategy with the lowest value should be accepted and applied in management practices.
The three Basic principles of AHP
• The hierarchy construction principle: The AHP underlying assumption is that complex systems can be better understood through decomposition into essential elements. These elements can be the criteria involved in the considered decision problem, and be hierarchically structured into several levels, according to the relative importance of each element with respect to another one. The highest level represents the main decision objective, while the lowest one is constituted by the different alternatives.
• The priority setting principle: Human beings are able to intuitively perceive relationships between two elements, to express a preference of one on the other and to numerically evaluate this preference. This is still true regarding subjective considerations, since the idea is to translate a feeling. However, a fixed priority scale must be implemented in order to make the evaluation independent from the different orders of magnitude that characterize each element. From the synthesis of this pair wise judgment set is derived the priority scale between all the considered elements.
• The logical consistency principle: The comparisons evoked in the previous paragraph must respect one constraint, namely transitivity. For instance, considering three events A, B and C, if A is better than B and B better than C, then A must be better than C. Moreover, if A is twice better than B and B is three times better than C, then A must be six times better than C: this would constitute a perfectly consistent judgment. Nevertheless, perfect consistency cannot be expected because of the subjective character of the evaluated comparisons and of the changing circumstances: for instance, the same decision-maker might express different choices at two different moments. The AHP technique thus involves quantitative and qualitative aspects into a unique analysis structure in order to convert the natural thoughts of any human being into an explicit process. This latter is implemented in a decision-support tool that provides objective and reliable results, even under different scenarios occurrence. It is worth noting that, being subjective the perceptions of the priority scale provider (i.e. the manager), the AHP method does not integrate the possible existence of an “always true, correct, immutable” decision. The AHP main steps include (Wang 2007)25 (Selection of optimum Hierarchy process, 2007):
Hierarchy design step: All the elements interfering into the decision-making problem must be determined and structured into levels as a family tree. The first level consists of the primary or main objective while the following ones are devoted to the secondary aims, etc. In the lowest level are the alternatives, i.e. the possible solutions of the multi-criteria problem (and so, in the case considered in the study, the non-dominated solutions provided by the Pareto sort): this phase allows clarifying the problem components and their interaction.
Development of judgment matrices: One of the main features of the AHP technique is its pair wise comparison working mode, for all the criteria (or alternatives) belonging to the same hierarchical level. Judgment matrices can then be defined from these reciprocal comparisons. The pair wise comparisons are based on a standardized evaluation schemes (cf. next subsection).
Computing of local priorities: Several methods for deriving local priorities (i.e. the local weights of criteria or the local scores of alternatives) from judgment matrices have been developed, such as the eigenvector method (EVM), the logarithmic least squares method (LLSM), the weighted least squares method (WLSM), the goal programming method (GPM), etc. Consistency check should be implemented for each judgment matrix.
Alternative ranking: An aggregation procedure accounting for all local priorities (thanks to a simple weighted sum) then enables to obtain global priorities regarding the main objective, including global weight of each criteria or global scores of each alternative. The final ranking of the alternatives is determined on the basis of these global priorities.
Steps of AHP
The research uses the following AHP steps.
identifying customer requirements and specifying the solution desired
organize the customer requirements as hierarchy from general to detail
Constructing a pair wise comparison matrix of each customer requirements on each other. in this matrix pair of requirements (needs) is compared with the other based on the result gained from the customer survey that have the most important and judgment of the research team with some selected Experienced customers and company workers.
Having collected all pair wise comparison data and entered to reciprocals together with unit entries down the main diagram and the priorities are obtained and normalized weight is gained.
Use hierarchal comparison (synthesis) to weight the vectors of priorities by the weight of the requirements.
Evaluate consistency from the hierarchal result and finally scale the customer requirements importance using frequency distribution method in to 1-5 scale.
Computational Details
Assume that n decision factors are considered in the quantification process of the relative importance of each factor with respect to all the other ones. This problem can be set up as a hierarchy as explained in the previous section. The pair wise comparisons will then be made between each pair of factors at a given level of the hierarchy, regarding their contribution toward the factor at the immediately above level. The comparisons are made on a scale of 1–9, as shown in table 6. This scale is chosen to support comparisons within a limited range but with sufficient sensitivity (a psychological limit for the human beings to establish quantitative distinction between two elements was proved by psychometric studies). These pair wise comparisons yield a reciprocal (n,n)-matrix A, where aii=1 (diagonal elements) and aji=1/aij.
Table 1Pair wise comparison of requirements for AHP (A scaling Method for priorities in Hierarchal structures,, 1977)
Values of aij Interpretation
1 Characteristics i and j are of equal importance
3 Characteristics i is weakly more important than Characteristics j
5 Experience and judgment indicate that Characteristics i is strongly
more important than Characteristics j.
7 Characteristics i is very strongly or demonstrably more important
than Characteristics j.
9 Characteristics i is absolutely more important than Characteristics j.
2,4,6,8 Intermediate values—for example, a value of 8 means that
Characteristics i is midway between strongly and absolutely more
important than Characteristics j.

Suppose that only the first column of matrix A is provided to state the relative importance of factors 2,3,…,n with respect to factor 1. If the judgments were completely consistent, then the remaining columns in the matrix would be completely determined due to the transitivity of the relative importance of the factors. However, there is no consistency except for that obtained by setting aji=1/aij. Therefore, the comparison needs to be repeated for each column of the matrix, and i.e. independent judgments must be made over each pair. Suppose that after all the comparisons are made, the matrix A includes only exact relative weights.
Multiplying the matrix by the vector of weights w=(w1,w2,…,wn) yields:
?(a11&a12&…&[email protected]&a22&…&[email protected]?&?&…&[email protected]&an2&…&ann)?([email protected]@[email protected])=?(w1/w1&w1/w2&…&w1/[email protected]/w1&w2/w2&…&w2/[email protected]?&?&…&[email protected]/w1&wn/w2&…&wn/wn)?([email protected]@[email protected])=n?([email protected]@[email protected])

Therefore, to recover the overall scale from the matrix of ratios, the EVM was adopted. (Zeng, 2007) (Optimization of wastewater treatement alternatives selection by hierarchy grey Relational analysis , 2007). According to the previous equation, the problem can formulate as AW=NWorA-nI=0, which represents a system of homogenous linear equations (I is the identity matrix). This system has a nontrivial solution if and only if the determinant of (A-nI) vanishes, meaning that n is an eigenvalue of A. Obviously, A has unit rank since every row is a constant multiple of the first row and thus all eigenvalues except one are equal to zero. The sum of the eigenvalues of a matrix equals its trace and in this case, the trace of A equals n. So, n is an Eigen value of A and a nontrivial solution. Usually, the normalized vector is obtained by dividing all the entries Wi by their sum. Thus, the scale can be recovered from the comparison matrix. In this exact case, the solution is any normalized column of A. Notably, matrix A in this case is consistent, indicating that its entries satisfy the condition ajk=aji/aki(transitivity property).
Consistency
However, in actual cases, precise values of Wi/Wjare not available, but their estimates, which in general differ from the ratios of the actual weights, are provided by the decision-maker. The matrix theory illustrates that a small perturbation of the coefficients implies a small perturbation of the Eigen values. Therefore, an Eigen value close to n, which is the largest Eigen value max, should be found since the trace of the matrix (equal to n) remains equal to the sum of the Eigen values while small errors of judgment are made and other Eigen values are non-zero. The solution to the problem of the largest Eigen value, which is the weight eigenvector w that corresponds to max, when normalized, gives a unique estimate of the underlying ratio scale between the elements of the studied case. Furthermore, the matrix whose entries are Wi/Wj remains a consistent estimate of the “actual” matrix A which may not be consistent. In fact, A is consistent if and only if max=n. However, the inequality maxes>n always exists. Therefore, the average of the remaining Eigen values can be used as a “consistency index” (CI) which is the difference between max and n divided by the normalizing factor (n-1).
CI=?max-n/(n-1)
The CI of the studied problem is compared with the average RI obtained from associated random matrices of order n to measure the error due to inconsistency, (Saaty 1980)20. As a rule of thumb, a consistency ratio CR=CI/RI value of 10% or less is considered as acceptable, otherwise the pair wise comparisons should be revised,( Aguilar-Lasserre, et al. 2009)1 (Aguilar-Lasserre, 2009)
T-shirt design techniques and quality characteristic
General manufacturing process consist different steps. These are broadly divided in to two categories pre-production and production process. The preproduction consists designing the garment, pattern design, production pattern making, grading and maker making and sample making. Production process consists cutting, stitching, (preparatory and assembly) and finishing all these process are described here.
Registering the design
Raw material selection: the very beginning for quality of t-shirts is selection of material type in considerations of quality parameters. The material types such are the cotton, nylon, Lenin, polystyrene etc are the determinants of t-shirt durability , elasticity of volumes , strengthens and other quality parameter determinants. There is also system of clamps and screws allow for very minute adjustments to the position of the screen. These adjustments are depending on the design features like the color verities. Depend on these the desired t-shirts to produce are:
Cotton t-shirts
Polyester t-shirts
Play cotton mix fabric
Viscose and others
Manufacturing processes
Machine setting techniques: machine setting and adjustments are the other pin points of producing quality t-shirts. From some of the over look machine is used to attach two fabric pieces together like front and back or other t-shirt sides. It provides seam stitch the garment and same machines have additional cutters that cut extra fabric that is left whine the two panels are joined. The additional cutters provide the aesthetic appeal to the garment and helps in the aesthetic appeal of the t-shirt product.
T-shirt manufacturing process: the t-shirt process encompass the grinning, spinning, knitting, dying, pattern making, cutting, stitching, checking and packing.
Design
Garment life expectancy is affected by range of decisions; choice of material & yarn fabric construction and finishing, trimming, garment design (shape) make up. Because these decisions are made at the design stage, production design has been identified as pivotal to determining of t-shirt quality parameters. Designers must be specifies many relevant characteristics of final garment like fashionably and styling.
Fabric finishing: Textile manufacturing process is used to improve the look performance or hand (feel) of the finished textile or clothing these include the mechanical and chemical treatment which produce arrange of effects including:
Fabric stiffing and softening
Scouring and bleaching impacting on fabric quality and feel
Water proofing and strain resistance measures
Pre shrinkage actions
Ant pilling resistance actions and so on
T-shirt quality characteristics
Quality of design requires higher amount of market research to establish the ultimate customer preference at an acceptable price amongst a competitive characteristics; these can form the basis for design. The various combinations of customer demand generate the following t-shirt quality characteristics.
Price and value of many
Individuality of appearance
Fashion is appropriate to the period or groups
Image enhancement (reliability of an executive suit, smartness)
Comfortable in wear both in, cut and fabric
Durability function and appearance
Ease of care (crease and stain resistance, shape retention, washeabilty)
Size and shape
Consistency of product