Declaration We Hagazi Abrha

Declaration
We Hagazi Abrha, Leake Gidey, Selomon Weldesenbet, students of the school of Mechanical and Industrial Engineering university of Mekelle, aware of our responsibility of the panel low, Declare and certify with our signature that our thesis entitled “Optimization of Product Design through Quality Function Deployment and Analytical Hierarchy Process” (In the case study of MAA Garment and Textile Factory) is entirely the result of our own work. We have faithfully and accurately cited all our sources, including books, journals, handouts and unpublished manuscripts, as well as other media, such as Internets and significance personal communication.

Name of Writers signature
Hagazi Abrha
Leake Gidey
Selomon Weldesenbet
Approved By: Signature
Ins Belay Mihrete
Acknowledgement
First and for most, Praises and Thanks to God, the Almighty, for his showers of blessings throughout our research to complete it successfully. We would express our deepest and sincere to Ins Belay Mihrete our Advisor who have been a great source of motivation and encouragement as well as guidance. It has been a wonderful learning experience throughout and this work would have been impossible without him, we thank him for his invaluable, insightful comment and idea for improvement of our thesis.

We also would like to thank our friends for being pillar of support throughout the completion of the thesis.

Last but not least, we would like to thank Mr……………………….head of technical department of MAA Garment and Textiles Factory for arranging to meet each departments of the company both in the Textile and Garmenting Departments. WE also would like to thank all the respondent persons for their cooperation and time.
Abstract
The success of a product or service largely depends on how they meet the customer needs and expectations. This thesis work employees both quality function deployment and Analytical Hierarchy process to translate customer needs and requirements in to the valuable (quality) for an existing product and optimizing the design of the product using quality function deployment is a management tool that provides a visual connective process to help teams focus on the needs of customer throughout the total optimization cycle of a product or process. It provides the means for translating customer requirements in to appropriate technical requirements for each stage of product optimization. The Analytical hierarchy process is also a technique used for decision making by prioritizing one over the other customer requirements can also prioritize and get a relative weight of each of the customer requirement.

The study was conducted in the case company of MAA garment and Textiles factory. The study used focus Group Discussion and questionnaires to identify customer needs of T-shirts of the case company and based on that survey ten customer requirements and technical attributes were identified and a survey was held to gather the satisfaction and importance of the product (T-shirt) then analytical hierarchy process were used as a method for prioritizing customer requirements. In order to identify the most prominent customer needs and product technical attributes, data’s were analyzed using quality function deployment and the relative weights of the technical attributes were gained. It revealed in the study that the case company failed in terms of meeting the needs of customer needs specifically men’s T-shirt and so as to alleviate those problems, the study recommended to use Quality function deployment as a means to meet customer needs and expectations and to have customer loyalty and market win.
Contents
TOC o “1-3” h z u Chapter one PAGEREF _Toc514954559 h 11Background of the study PAGEREF _Toc514954560 h 11.1Product Selection PAGEREF _Toc514954561 h 21.2PROBLEM STATEMENT PAGEREF _Toc514954562 h 31.3Objectives PAGEREF _Toc514954563 h 41.3.1General objective PAGEREF _Toc514954564 h 41.3.2Specific objectives PAGEREF _Toc514954565 h 41.4Significance of the study PAGEREF _Toc514954566 h 41.5Scope of the study PAGEREF _Toc514954567 h 4Chapter two PAGEREF _Toc514954568 h 52Literature Review PAGEREF _Toc514954569 h 52.1Quality Function Deployment PAGEREF _Toc514954570 h 52.2QFD Applications in Product Optimization PAGEREF _Toc514954571 h 72.2.1Phase of QFD: PAGEREF _Toc514954572 h 82.2.2Steps to the House of Quality PAGEREF _Toc514954573 h 92.3Methods for Assessing User Needs PAGEREF _Toc514954574 h 112.2.1 Utilization of Existing Knowledge PAGEREF _Toc514954575 h 122.2.2 Generation of New Information: PAGEREF _Toc514954576 h 122.2.3 Provision of Need Information by other Methods PAGEREF _Toc514954577 h 122.4Analytic Hierarchy Process (AHP) PAGEREF _Toc514954578 h 132.4.1The three Basic principles of AHP PAGEREF _Toc514954579 h 142.4.2Computational Details PAGEREF _Toc514954580 h 152.4.3Consistency PAGEREF _Toc514954581 h 17Chapter Three PAGEREF _Toc514954583 h 203Methodology PAGEREF _Toc514954584 h 203.1Target population PAGEREF _Toc514954585 h 213.2Data collection (customer assessment) PAGEREF _Toc514954588 h 223.3Data Analysis Technique PAGEREF _Toc514954589 h 244RESULT AND DISCUSSION PAGEREF _Toc514954590 h 254.1Collecting Customer Needs PAGEREF _Toc514954591 h 254.2Priority Needs- Analytic Hierarchy Process (AHP) PAGEREF _Toc514954592 h 274.2.1Consistency PAGEREF _Toc514954593 h 324.3House of quality matrix PAGEREF _Toc514954594 h 36Chapter Five PAGEREF _Toc514954595 h 395Conclusion and Future scope PAGEREF _Toc514954596 h 395.1Conclusion PAGEREF _Toc514954597 h 39APPENDEX PAGEREF _Toc514954598 h 426Bibliography PAGEREF _Toc514954604 h 54
List of tables and figures
TOC h z c “Table” Table 1 Pair wise comparison of requirements for AHP PAGEREF _Toc514954756 h 16Table 2 result of customer survey on rate of importance the product (T-shirt) PAGEREF _Toc514954757 h 25Table 3 Result of customer survey on products (T-shirt) satisfaction PAGEREF _Toc514954758 h 26Table 4 Interpretation of entries in pair wise comparison matrix PAGEREF _Toc514954759 h 28Table 5 Interpretation of entries in pair wise comparison matrix PAGEREF _Toc514954760 h 29Table 6 the relative average of each customer requirement PAGEREF _Toc514954761 h 31Table 7 SAAT’s random index for n values of customer requirement PAGEREF _Toc514954762 h 34Table 8 Importance weight scale PAGEREF _Toc514954763 h 35Table 9 Rate of importance of the customer requirement PAGEREF _Toc514954764 h 35Table 10 Customer needs weight PAGEREF _Toc514954765 h 40Table 11 percentage of weight of technical attributes PAGEREF _Toc514954766 h 41 TOC h z c “Figure”
Figure 1 QFD matrix form PAGEREF _Toc514957093 h 6Figure 2 the four phases of QFD PAGEREF _Toc514957094 h 8Figure 3 steps of Quality function deployment PAGEREF _Toc514957095 h 11Figure 4 the research methodologies PAGEREF _Toc514957096 h 20Figure 5 company technical attributes PAGEREF _Toc514957097 h 23Figure 6 the hierarchy of the customer requirements PAGEREF _Toc514957098 h 28Figure 7 QFD of the T-shirt PAGEREF _Toc514957099 h 39
Chapter oneBackground of the studyToday, there is an intensive and unprecedented international competition in textile and garment Industry. This poses new challenges for an emerging textile and garment industry in Ethiopia. MAA Garment Textile Company is one facing with competition to the global market and rapid improvements in information. Optimization product design is critical to overcome the market performance problems and to compute up on other relatives. Launching quality function deployment (QFD) and analytical hierarchy process (AHP) to apply design in aspects of customer perspectives is a pin point to overcome the challenges.

Quality function Deployment (QFD) is a planning tool used to fulfill customer expectation it is a disciplined approach to product development. An organization that implements QFD can improve engineering Knowledge, productivity quality and reduce costs, Product development time and engineering changes. It focuses on customer expectations or requirements, often referred to as the voice of customer. It is employed to translate customer expectations, in terms of specific requirements, into directions and actions. Here Conflicting characteristics or requirements are identified early in the QFD process and can be resolved before production.

Priority Needs – Analytic Hierarchy Process (AHP)
The customer needs, by using the AHP is going to be quantified, and then prioritized by actual customers so we know which needs are important.

This thesis is on optimization of product design through QF and AHP on MAA Garment Textiles Company on T-shirts. First the customer needs (specifications) are going to be studied through questionnaires and will be prioritized using an AHP and then transformed to a customer oriented product (T-shirt).

Voice of Customer:
The QFD process reduces the overall cycle time in bringing the product to market. The driving force behind QFD is that the customer dictates the attributes of a product Customer satisfaction like quality is defined as meeting or exceeding customer expectations. Words used by the customers to describe their expectations are often referred as the voice of the customer. Sources for determining customer expectations are focus groups, surveys complaints, consultants, standards and federal regulations.

Product SelectionThe study by Christoph; Martin (2011) shows that T-shirt has low level of risk in terms of customer empowerment in product development compared to any other garment and textile products. First, T-shirts are generally perceived to be associated with “low” overall risk. And MAA Garment Textile Company produces many products; from those products T-shirt is produced in mass and found mainly in the companies shop or market but not mostly perceive customer desire compared with other companies products mainly imported once. In order to remain on the market, companies need to extend the product lifecycle, redesign the product, and/or develop new products.

Because of the above reasons also MAA Garment Textile company supplies to local market below its capacity one of the reason is lack of demand and weak relationship with customers, so to overcome this the study selects polo t-shirt because of low level risk.

Here are one twos on selection of product
By the times of visit to the company T-shirts were produced in mass and hence we have selected it for easily knowing quality characteristics and design requirements
From experiences out of MAA Garments products T-shirts have more demand on market and this helps as easily gain customer requirements from our colleagues
PROBLEM STATEMENTTextile industry in Ethiopia has demonstrated, considerably have rapid progress during the past few years. But most textile companies does not give emphasis to local market due to this the market and customer perspectives are underestimated and poorly considered. In MAA Garment and Textiles Factory for example we have observed that a T-shirt it is not meeting customer requirement in case has low consideration/demand in their local market which causes a customer shift to an imported T-shirts. The diversified expectations of the customer can be addressed using Quality function deployment which integrates customer needs to technical attributes and Analytical hierarchy process which prioritizes customer needs on the case companies T-shrt.
ObjectivesGeneral objectiveThe general objective of this study is to optimize product design through the application of Quality function deployment and analytical hierarchy process In the case study of MAA Garment and Textiles Factory in the particular product T-shirt.

Specific objectivesDetermining customer needs and product requirements through data collection techniques.

Quantifying and prioritized the customer needs on the hierarchy diagram providing accurate ratio-scale priorities.

Improving product design through Quality Function Deployment.

To give direction based on the implications QFDandAHP that helping MAA- garment textile to be the exemplary competent.

Significance of the studyThe basic thing in any company is to understand the customer need and to develop product that satisfy them. Quality function deployment and Analytical hierarchy process are used to select the design characteristics of the product to satisfy the expresses needs and preferences of customers as well as to prioritize those features and select the most important feature for special attention (optimization) later in the design and processing . It helps the cross-functional team to make the key tradeoffs between the customer needs and product technical requirements so as to develop a high quality product.

Scope of the studyThis Research mainly focuses on assessing customer expectations on T-shirts of MAA Garment and Textile Factory and design customer oriented product with the implementation of quality function deployment and Analytical hierarchy process both on the Textile and Garmenting departments of the case company.

Limitations Of the study
Since it is known that the applications of quality function deployment uses a compression with any other companies (competitors) but here it is difficult to access data’s related to competitors in this level , so this research only done on the case companies product only.
Chapter twoLiterature ReviewQuality Function DeploymentQFD was developed by Yoji Akao in Japan in 1966 CITATION YAk97 l 1033 (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 CITATION Aka90 l 1033 (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 CITATION Cen04 l 1033 (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 CITATION FHu03 l 1033 (The leading Edge in starategic QFD, 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 CITATION Viv00 l 1033 (Methods and Techniques to help quality function deployment, Benchmarking, 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 CITATION Agu09 l 1033 (Aguilar-Lasserre, 2009) CITATION Aka90 l 1033 (Akao, 1990).

RelationshipmatrixHow to satisfycustomer wantsInterrelationshipsCompetitive assessmentTechnicalevaluationTarget valuesWhat the customerwantsCustomer importance ratingsWeighted rating
Figure SEQ Figure * ARABIC 1 QFD matrix form CITATION Aka90 l 1033 (Akao, 1990)QFD Applications in Product OptimizationQFD 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 CITATION Hau88 l 1033 (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 CITATION Cph95 l 1033 (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 CITATION Par98 l 1033 (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 CITATION Cha l 1033 (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 CITATION LCl94 l 1033 (Chohen, 1994) CITATION Cph95 l 1033 (Cohen, 1995) CITATION Hau88 l 1033 (Hauser. JR, 1988) CITATION Kin89 l 1033 (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 CITATION Kim97 l 1033 (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 CITATION Dik05 l 1033 (Dikmen I, 2005).

QFD Structure
Phases of QFD:The QFD system consists of the following four interlinked phases (Cengizetal, 2004)5 CITATION Cen04 l 1033 (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 SEQ Figure * ARABIC 2 the four phases of QFD2.2.2Steps to the House of QualityStep 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 CITATION Hau88 l 1033 (Hauser. JR, 1988)The following figure shows some steps of quality function deployment.

Figure SEQ Figure * ARABIC 3 steps of Quality function deploymentMethods for Assessing User NeedsIn 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 CITATION Hol84 l 1033 (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 CITATION Hol84 l 1033 (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 CITATION Hol84 l 1033 (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 CITATION Fis03 l 1033 (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 CITATION Hau88 l 1033 (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 CITATION Car08 l 1033 (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 CITATION Saa80 l 1033 (Saaty. T, 1980) CITATION Saa77 l 1033 (A scaling Method for priorities in Hierarchal structures,, 1977) CITATION TLS00 l 1033 (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 CITATION Zah86 l 1033 (Zahedi., 1986) CITATION Sud l 1033 (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 CITATION Kah00 l 1033 (Justification of manufucturing technologies using fuzzy benefit/cost ratio analysis , 2000) CITATION Lee05 l 1033 (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 CITATION Wan07 l 1033 (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 DetailsAssume 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 SEQ Table * ARABIC 1Pair wise comparison of requirements for AHP CITATION Saa77 l 1033 (A scaling Method for priorities in Hierarchal structures,, 1977)Values of aijInterpretation
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:
a11a12…a1na21a22…a2n??…?an1an2…annw1w2?wn=w1w1w1w2…w1wnw2w1w2w2…w2wn??…?wnw1wnw2…wnwnw1w2?wn=nw1w2?wnTherefore, to recover the overall scale from the matrix of ratios, the EVM was adopted. (Zeng, 2007) CITATION Zen07 l 1033 (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).

ConsistencyHowever, 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 CITATION Agu09 l 1033 (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 are 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 ThreeMethodologyMethodologically this thesis going to include the methods data analysis to collect customer fed backs or called VOC and the methods of data analysis to how to integrate the voice of customer to the engineering requirements. The following is the outline of the methodology that we are going to fellow for the successful accomplishment of our project.

This study adopts both a combinational explorative and conclusive research design. The explorative study is a study concerned to explore the research questions both customer requirement and product technical requirements and the conclusive research design is aimed at finding final findings.

Idea generation
Explore customer need and product technical requirements of T-shirt using informal interview and focus group discussion
Questionnaire development and data collection
Prioritize customer needs using the AHP process
Build relationship between customer needs and product technical requirement using the House of Quality software
Result and Discussion
Conclusion and future scope
Figure SEQ Figure * ARABIC 4 the research methodologies
Target populationDue to the unknown number of customer the sample size to the sample size to explore the needs of customers and the rating of customer satisfaction through two questionnaires have been calculated with the following equation:n=p1-pz2/d2where: n=population sizep=the expected proportion at 20%
z= normal random variable
d is the margin of error in estimating p
The confidence level corresponds to a Z-score. This is a constant value needed for this equation. At 90% confidence interval –Z score =1.646, d=0.1, based on the above given values we can have the following population size for the customers of MAA Garment and Textiles Factory T-shirt users.

n=(1.6462)*0.2*(1-0.2)/0.12n=41.33 which is approximately 40 customers are needed to be surveyed in order to obtain the CRs information. The target population of this study also comprise of employees of MAA Garment and Textiles Factory from production and design unit, knitting unit, dyeing unit, Garment unit to identify product technical requirement.

Data collection (customer assessment)The method of data collection encompasses direct interviews, literatures, direct surveys and observations of product distributions centers. After the overall collection of organized data it goes to methods of data analysis tools using QFD and AHP to have an implication the collected information.

methods Sub methods Activities Findings
primary Observation and Focus Group discussion Assessing and recording t-shirt design techniques Engineering attributes of t-shirt
And Customer requirements
Direct discussion Discussing with marketing department Product sales point
Secondary Questionnaires and surveys Distributing questionnaires’ regard to customer needs, satisfaction ; technical attribute assessment Frequency of customer selection and comment on their needs and degree of satisfaction, quantified target of engineering techniques
Literatures and survey Reading about QFD and AHP on optimization of product design Documented histories and applications of QFD ;AHP on product design optimizations
Data Analysis TechniqueAnalytical hierarchy process, AHP
AHP provides the underlining decision process that powers decision lens. It helps decision makers find one that best suits their goal and their understanding of the problem. It provides comprehensive and rational frame work for structuring a decision problem, for representing and quantifying its elements, for relating these elements to overall goals, and evaluating alternative solution. Here in this research the AHP is used for prioritizing customer requirements and providing importance rate based on their weight to 1-5 scale.
Quality function deployment QFD
The main idea of QFD approach is building a design strategy over the VOC. The CRs should be carefully studied and defined to take the first steps in the study before going further in the next phases. The next phases of the QFD are about converting the CRs into corresponding PTRs in order to combine both design and production issues in the same study. This methodology integrates the CRs and product specifications into the product design process so that the product will be likely to capture a reasonable customer attraction on the market.

CHAPTER FOUR
RESULT AND DISCUSSIONFinding Customer requirements
The customer requirements were found by group discussion of the research team with some company workers, students and some customers of the company.

Numerically: discussion was held with 2 company workers that are users of the product, 2 customers founded in the market of the product and 2 students most of them our colleagues and customers of that product and have expertise on textile products as they take their internship program in textile industries.
Having discussing the following requirements was found which are summed up.

Easily wearable
Reasonable price
Resistance to wear during washing
To be attractive
Match with many clothes
Look fashionable and Trendy
Have appropriate size
Dimensional stability
Durability (to be Durable)
Not to wrinkle easily
Finding the product Technical requirements
The technical requirements or technical attributes of the product that can be measured and benchmarked against the competition were gained through group discussion and research team’s expertise. Technical attributes may exist that your organization is already using to determine product specification, however new measurement can be created to your product is meeting customer needs.
Those technical requirements of the company were found using group discussion of the research team with some selected company workers both from Textile and Garmenting Departments, one from quality Assurance, two from supervisors (sewing + spinning) sections and two from design sewing sections and the gained quality characteristics are summarized as follows.

T-shirt printing process is comprises the garmenting and the textile process sections. In each of the process material type and fabric content usage techniques & the operational process dependent characteristics and production process techniques including the dying , anti wrinkle action, range of coloring and pattern making techniques are same of engineering techniques generate impacts on t-shirt products.

Technical attributes
Garmenting
Textile
Raw material
Processing performance
Designing
Appropriate sewing machine setting
Suitable design (design ability)
Pattern making ability
Range of colors and patterns
Ability not to form pilling
Resistance to dye
Anti wrinkle action
Glossy
Material type
Fabric content
Figure SEQ Figure * ARABIC 5 company/product technical attributes
Suitable design
Anti-wrinkle action
Appropriate sewing machine setting
Glossy
Range of colors and patterns
Fiber content of fabric
Resistance to dye
Ability not to form pilling
Type of material
Pattern making ability
Collecting Customer NeedsThe first step is to elicit and capture the basic needs and requirements of the customer – or WHATs is it that the customer wants ,as in the first phase the data obtained on CRs and needs about the respective product, information about the most important quality characteristics of the product or about the technical capacity of the organization to get those quality characteristics necessary to meet CRs in terms of efficiency, product planning is considered critical for the success of the entire QFD and AHP process.

As mentioned in methodology section the research used explorative research design to identify CNs towards men t-shirt. Focus group discussion use as a means to get CNs; those needs are mentioned below:
Easily wearable
Reasonable price
Resistance to wear during washing
To be attractive
Match with many clothes
Look fashionable and Trendy
Have appropriate size
Dimensional stability
Durability (to be Durable)
Not to wrinkle easily
Organizing the surveyed customer requirement
Questionnaire was held to collect information on both the customer requirements importance and satisfaction on the companies T-shirt.
The research team has gathered the customer requirements and tried to find the rate of importance and found the following results with regard to rate of importance: 1for not important, 2 not very important, 3 somewhat important, 4 very important and 5 most important once.

Table SEQ Table * ARABIC 2 result of customer survey on rate of importance the product (T-shirt)s/n Customer requirement “WHAT” Sample size ‘n’ mean Standard deviation
1 Easily wearable 40 3.85 2 Reasonable price 40 4.375 3 Resistance to wear during washing 40 3.5 4 To be attractive 40 2.385 5 Match with many clothes 40 3.50 6 Look fashionable and trendy 40 2.575 7 Have appropriate size 40 4.225 8 Dimensional stability 40 3.225 9 Durability (To be Durable) 40 4.27 10 Not to wrinkle easily 40 4.0 And also from the customer survey the following average of satisfaction for each of the customer requirements were gathered:
The following table shows the level of customer importance for the first level of customer requirements cost, performance, material type and Aesthetics.
s/n Customer requirement Sample size mean Standard deviation
1 Aesthetics 40 2.82 2 Performance 40 4.108 3 Material type 40 3.575 4 Cost 40 4.375 The following table summarizes the rate of satisfaction of the customers towards the seated customer satisfaction about T-shirt Of MAA Garment and Textiles Factory.

Table SEQ Table * ARABIC 3 Result of customer survey on products (T-shirt) satisfactions/n Customer requirement “WHAT” Sample size ‘n’ Mean/Average Standard deviation
1 Easily wearable 40 3.1 2 Reasonable price 40 3.225 3 Resistance to wear during washing 40 2.575 4 To be attractive 40 3.175 5 Match with many clothes 40 2.95 6 Look fashionable and trendy 40 2.625 7 Have appropriate size 40 2.625 8 Dimensional stability 40 3.35 9 Durability (To be Durable) 40 3.225 10 Not to wrinkle easily 40 3.075 Priority Needs- Analytic Hierarchy Process (AHP)The customer requirements are first0 put on a hierarchy based on their category as follows: The following diagram shows the requirement hierarchy.

Customer Needs
Aesthetics
Cost
Functionality
Performance
Reasonable price
To have appropriate size
Easily wearable
Durability
Dimensional stability
Not to wrinkle easily
Resistance to wear during washing
To be attractive
To match with many clothes
Look trendy and fashionable
Level 1
Level 2
Durability

Figure SEQ Figure * ARABIC 6 the hierarchy of the customer requirements
From the customer requirements gained there are two levels the first one with four requirements and the second level with 10 requirements:
From the customer requirements gained
The customer needs on the hierarchy diagram must be quantified, and then prioritized by actual
Customers so we know which needs are important. For this purpose, we followed five steps.

Having taking the customer feedback and assessments AHP is a tool to prioritize the VOC in accordance to importance rates or weights of the personal perspective design requirements.

Let as represent the customer requirements
Easily wearable =CR1
Reasonable price =CR2
Resistance to wear during washing =CR3
To be attractive =CR4
Match with many clothes =CR5
Look trendy and Fashionable =CR6
Having Appropriate size =CR7
Dimensional stability =CR8
Durability (to be durable) =CR9
Not to wrinkle easily =CR10
Obtaining weights for each characteristics
Suppose there are n objectives. We begin by writing down an n*nmatrix (known as the
Pair wise comparison matrix). The entry in row iand column j of A (call itaij) indicates
How much more important objective iis than objective j. “Importance” is to be measured on an integer-valued 1–9 scale, with each number having the interpretation shown in the following table.

Table SEQ Table * ARABIC 4 Interpretation of entries in pair wise comparison matrixValues of aijInterpretation
1 Objective i and j are of equal importance.

3 Objective i is weakly more important than objective j.

5 Experience and judgment indicate that objective i is strongly
more important than objective j.

7 Objective i is very strongly or demonstrably more important
than objective j.

9 Objective i is absolutely more important than objective j.

2,4,6,8 Intermediate values—for example, a value of 8 means that
objective i is midway between strongly and absolutely more
important than objective j.

The following table shows the Pair wise relationship of the customer requirements (first level) of MAA Garment and Textiles Factory gained from the customer survey and the research team’s expertise.

s/n Customer requirement Aesthetics Performance Functionality Cost
1 Aesthetics 1 1/5 3 1/7
2 Performance 5 1 3 1/3
3 Functionality 3 1/3 3 1/3
4 Cost 7 3 3 1
5 sum 16 4.53 9.33 1.81
In this table an integer means the row entry is more important than column entry. When the column is more important we use inverse. For instance the value 3 for row 3 means that functionality is more important than Aesthetics.

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 settingaij=1/aij. Therefore, the comparison needs to be repeated for each column of the matrix, 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.

w1w1 w1 w2w1wn w2w1…wnw1w2w2…wnw2w2 wn…wnwnStep by step of AHP
Form the pair wais comparison in matrix form
A=11/51/31/751/331/331/331/37331From the above matrix A find a normalized matrix Cij using the following formula;
Cij=aijinaijC11=1/(1+5+3+7)=0.0625By using the above formula to all entries of aij we can have the following normalized matrix
customer requirement Aesthetics performance Functionality cost SUM Weight
Aesthetics 0.0625 0.044118 0.035714 0.078947 0.221279 0.05532
Performance 0.3125 0.220588 0.321429 0.184211 1.038727 0.259682
Functionality 0.1875 0.073529 0.321429 0.184211 0.766669 0.191667
cost 0.4375 0.661765 0.321429 0.552632 1.973325 0.493331
The weight in the above table right hand side is gained by using the following formula:
wi=incijnwhere n is the number of criteria’s or requirements
W1=(0.0625+0.044118+0.0357+0.0709)/4=0.05532the rest weights will be calculated by using this method and will result the following:
Wi=0.0550.260.190.49
Table SEQ Table * ARABIC 5 Interpretation of entries in pair wise comparison matrixS/N CR1
CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10
1 CR1 1 1/3 1 3 5 1 1/3 1 1/9 1/3
2 CR2 3 1 3 7 3 3 1 3 1/3 1
3 CR3 1 1/3 1 5 3 7 1/7 1 1 1
4 CR4 1/3 1/7 1/5 1 1 1 1/3 1/5 1/3 1
5 CR5 1/5 1/3 1/3 1 1 1 1/3 1/7 1/3 1
6 CR6 1 1/3 1/7 1 1 1 1 1/3 1 1/3
7 CR7 3 1 7 3 3 1 1 1 1 3
8 CR8 1 1/3 1 5 7 3 1 1 5 3
9 CR9 9 3 1 3 3 1 1 1/5 1 1
10 CR10 3 1 1 1 1 3 1/3 1/3 1 1
Sum 22.53 7.81 15.67 30 28 22.00 6.48 6.21 11.11 12.67
In this table an integer means the row entry is more important than column entry. When the column is more important we use inverse. For instance the value 3 for a12 mean that easy to wear is weakly more important than reasonable price.

11/313511/311/91/3313733131/3111/315371/71111/31/71/51111/31/51/311/51/31/31111/31/71/3111/31/711111/311/3317331111311/31573115393133111/5113111131/31/311For the above matrixB1 of b11is:
b11=aijinaijb11=1(1+3+1+13+15+1+3+1+9+3)b11=0.044
After we repeat with other components this give a C matrix which consists n items B vectors
bij=Table SEQ Table * ARABIC 6 the relative average of each customer requirement
This calculation is also called as normalization. To normalize, we sum the elements and divide each element by this sum and multiply by 100. This is called the row Average of the Normalized Columns (RANC) method. This gives me percentage values of columns. We called it as W column.
wi=incijnYou can see W vector in following,
W=w1w2…wnFor instance w1 =0.044+0.043+0.064+0.10+0.179+0.045+0.051+ .122+0.010+0.02610W1=0.068Using the same calculation for the rest ten requirements the following result will gain,
W=0.0680.1560.1060.0350.0370.520.1560.1330.1450.097ConsistencyEven if AHP has a consistent system, results will be depended on decision maker. For this purpose, a consistency ratio (CR) must be calculated. To calculate Consistency ratio, we must determine the basic value () and number of factors. For determining, we multiply matrix A and W. End of this step, we draw up matrix-D. You can see this calculation in following.
A matrix A is the following
A=11/313511/311/91/3313733131/3111/315371/71111/31/71/51111/31/51/311/51/31/31111/31/71/3111/31/711111/311/3317331111311/31573115393133111/5113111131/31/311*W =0.0680.1560.1060.0350.0370.0520.1560.1670.1450.097By using matrix calculation the following normalized weight factor will result.

Weight factor D = A*W; D = 1.002.010.580.50.910.660.962.911.272.281.200.581.31Then, we divided each D and W values to eachother and it gave me (E). When we calculate the arithmetic average, it gives as 12.06. You can see this in following formulas.
Ei=diwi (i= 1, 2, 3 …n)E1=d1w1=1.000.062=12.06Ei=12.0610.4612.1911.8511.7110.9210.4310.688.5012.96?Max =i=113Ein=111.8910=11.189?Max=11.189Then the consistency index will be CI=?max-nn-1CI=11.189-11010-1 =0.132
In the final step, we divided consistency index(CI) to random index (RI) which consists of standard revision values. You can see RI table 13 in following.

Table SEQ Table * ARABIC 7 SAAT’s random index for n values of customer requirementSample size Random index value
1 0.00
2 0.00
3 0.58
4 0.90
5 1.12
6 1.24
7 1.32
8 1.41
9 1.45
10 1.49
CR =CIRI= 0.1321.49 =0.089As a result of these calculations, if CR value is under 0.10, AHP analysis is consistent. If this number is more than 0.10, it shows that there might be a calculation mistake or decision maker’s inconsistency. In this situation, whole process must be repeated. But in our case it is consistence.

Importance weights of Customer requirement
The importance weight of customer requirement is calculated from the weighting values of the AHP by making an interval scale 1-5. This scale ranks from 1-5 of the importance weights of customer requirement in the following table.

Table SEQ Table * ARABIC 8 Importance weight scaleImportance Upper bound Lower bound
5 0.167 0.134
4 0.134 0.101
3 0.101 0.068
2 0.068 0.034
1 0.034 0
Those values have the following implication
1-the requirement has strongly weak importance
2-the requirement has somewhat weak importance
3-the requirement has medium importance
4-the requirement has strong importance
5-the requirement has more strong importance
Based on the above scaling mechanism the requirements are categorized as follows;
Table SEQ Table * ARABIC 9 Rate of importance of the customer requirements/n Customer Needs (specification) Importance
1 Easily wearable 3
2 Reasonable Price 5
3 Resistant to wear during washing 4
4 To be attractive 2
5 Match with many clothes 2
6 Look fashionable and trendy 2
7 Have appropriate Sizes 5
8 Dimensional stability 5
9 Durability (to be durable) 5
10 Not to wrinkle easily 3
House of quality matrixIn this step, let’s start to draw ”house of quality” matrix. For this purpose we determined customer requirements and their importance weight with their technical attributes. And this is turn of fixing them: customer requirements take place in the left side of the house and next to them their importance weight takes place. The technical attributes’ is place tope as seen the following form.

Having filled the symbols in the accordance to the customer requirement to technical requirements relationship weights, we then goes to compute the required values. Then we start to draw right part of the house which includes our values, planning values, sales point, improvement ratios, absolute weight and customer need weight relate with the current product.

Current product: this is the weight where the product has currently. This is determined from the customer satisfaction survey data collected result of the customer requirements of 1-5 scale weights for example the requirement To be durable has a customer satisfaction of 4 out of the scale 1-5.

Plan: this is the target weight which the company intended to address. It determined from the company stated subjective value with respect to the current product level for example the requirement to be Durable has a plan of 5 out of 1-5 scale.

Improvement ratio: is a ratio calculated as plan divided by current product.

IP=PCPFor example The requirement To be Durable has an Improvement ratio of
IP=P/CP=5/4=1.25Sales point: traditional QFD uses the original scale sales point 1.5 for strong selling point &1.2 for week sales point. We discus sales point with the company’s sale department on the customer requirements strength to affect sales ability for example the requirement reasonable price have a great sales point for the people of Ethiopia.

Absolute weight: multiplying the importance weight, improvement ratio and sales point, to give customer requirements absolute weights.
AW = IW*IR*SPFor example the requirement To be durable has Absolute weight of
AW=IW*IR*SP=5*1.5*1.25=9.4Customer needs weight: determined by summing the absolute weights and each divided by the sum to give normalized customer need weights.

CNW=AWAW*100=9.471.5*100=13%
Figure SEQ Figure * ARABIC 7 QFD of the T-shirtFindings
The biggest cost in t-shirt industry is constituted by markets and customer compliance costs. For this reason, the inclusion of the QFD in production process will have significant on competitive advantage through getting more advantageous position in sales by satisfying the customer needs. For this reason, we applied the QFD to t-shirt characteristic ranks. We have categorized this process analysis in the following ranks to explain the situation more clearly.

The first task we determined the customer requirement .in this way we determined key customers, we got provide a meeting customer and product. Then we have calculated customer responses in to 1-5 scale importance and having observing a frequency a given scale which need to analyze importance competition one customer requirement upon the other.

The second item was application of analytical hierarchy process (AHP) to understand hierarch order between customer requirements. During this process, we determined same criteria are which have important roles for selling advantage. These criteria’s were design, cost and quality. After this, we tried to increase inferior criteria’s to apply consistency of the analysis.

The third step we have learned the customer needs’ weights we have calculated. According to this information we determined technical attributes which are compared with customer needs and put them in the house of quality. Also we determined the relationship between each technical attributes in the roof of HOQ.
The last step includes analyses of results which we get from house of quality. As a result of whole process we find out same results. The results demonstrate that same properties of the product can be optimized.
In conclusion we considered dates of house of quality and found these following results.
Customer needs weight: was calculated by the sum of customer absolute weights and divide to sum of all requirements as resulted in the following table.

Table SEQ Table * ARABIC 10 Customer needs weightNo. Customer requirements Percentage
1 Easy to wear 6.7%
2 Reasonable price 13%
3 Resistance to wear during washing 12%
4 To be attractive 5.6%
5 Too match with many clothes 4.5%
6 Look trendy and fashionable 4.5%
7 Having appropriate size 17%
8 Dimensional stability 13%
9 Durable to be durable 13%
10 Not to wrinkle easily 9.8%

Based on the above shown pareto analysis above 80% of the customer requirements comprises Having appropriate size, Reasonable price, Dimensional stability, Durability, Resistance To wear during washing and not to wrinkle easily, in case by addressing those customer needs can solve the customer dissatisfaction on the company’s product (T-shirt)
When we analyze the percentage of customer requirements, we observed that customers may get suspicious whether they want to buy or not the product. Because they thought “Having appropriate size” has a great importance on the customer’s interest followed by Reasonable price and Durability, according to the highest percentage of (17%) of customer requirement which is calculated with the planning of the company. Highest optimization should be given to those requirements.

Those highest valued customer requirements should be fulfilled by optimizing the technical requirements (Attributes) corresponded to those values. The following is the percentage of combination the technical attributes with customer requirements from the House of Quality Matrix as shown in the following table.

Table SEQ Table * ARABIC 11 percentage of weight of technical attributess/n Technical attributes Percentage of weight (%)
1 Fiber content of the fabric 15.74
2 Appropriate sewing machine setting 3.52
3 Range of colors and printing 5.01
4 Glossy 1.94
5 Suitable Design 15.99
6 Anti-wrinkle action 9.26
7 Resistance to dye 7.02
8 Ability not to form pilling 5.02
9 Type of material 23.06
10 Pattern making ability 13.40

The above pareto analysis shows as the most prominent technical characteristics the data analyzed using pareto diagram to arrange information in such a way that priorities for optimization of technical attributes.

Based on the above result Type of Material, Suitable design, Fiber content of fabric, Pattern making ability, Anti wrinkle action and resistance to dye comprises 80% of the technical attributes, so by optimizing those characteristics can be addressed customer dissatisfactions.
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APPENDEXHaving conducted the customer survey and organizing the resulted questioner the following tabulated table is prepared.

Having conducted the customer survey and organizing the resulted questioner the following tabulated table is prepared.

Importance description Frequency Percentage Cumulative percentage
Easy to wear
Not important 0 Not very important 1 2.5 Same what important 5 12.5 Very important 25 62.5 Most important 9 22.5 Reasonable price
Not important 0 0 Not very important 1 2.5 Same what important 5 12.5 Very important 12 30 Most important 22 55 Resistance to wear during washing
Not important Not very important 2 5 Same what important 23 57.5 Very important 8 20 Most important 7 17.5 To be attractive
Not important 1 2.5 Not very important 19 47.5 Same what important 8 20 Very important 7 17.5 Most important 5 12.5 Ensure movement of body
Not important Not very important 2 5 Same what important 21 52.5 Very important 8 20 Most important 9 22.5 Mach with many clothes
Not important Not very important 3 7.5 Same what important 18 32.5 Very important 10 25 Most important 8 32.5 Fashionable
Not important Not very important 2 5 Same what important 20 40 Very important 8 30 Most important 3 7.5 Appropriate size
Not important Not very important 2 5 Same what important 3 7.5 Very important 13 32.5 Most important 21 52.5 Dimensional stability
Not important 1 2.5 Not very important 3 7.5 Same what important 18 45 Very important 10 25 Most important 8 20 Durability
Not important Not very important 2 5 Same what important 4 10 Very important 15 37.5 Most important 19 47.5 Not wrinkle easily
Not important Not very important 1 2.5 Same what important 7 17.5 Very important 15 37.5 Most important 17 42.5 Molding easily
Not important Not very important 2 7.5 Same what important 5 12.5 Very important 20 50 Most important 12 30 Comfortable during wearing
Not important 1 2.5 Not very important 12 30 Same what important 13 32.5 Very important 7 17.5 Most important 7 17.5 Customer satisfactionHaving conducted the customer survey and organizing the resulted questioner the following tabulated table is prepared.

Importance description Frequency Percentage Cumulative percentage
Easy to wear
Not important 3 7.5 Not very important 8 20 Same what important 15 37.5 Very important 10 25 Most important 4 10 Reasonable price
Not important 2 5 Not very important 9 22.5 Same what important 10 25 Very important 16 40 Most important 3 7.5 Resistance to wear during washing
Not important 8 20 Not very important 15 37.5 Same what important 7 17.5 Very important 6 15 Most important 4 10 To be attractive
Not important 2 5 Not very important 10 25 Same what important 16 40 Very important 8 20 Most important 5 12.5 Mach with many clothes
Not important 2 5 Not very important 14 35 Same what important 12 30 Very important 8 20 Most important 4 10 Fashionable
Not important 8 20 Not very important 11 27.5 Same what important 13 32.5 Very important 4 10 Most important 4 10 Appropriate size
Not important 5 12.5 Not very important 6 15 Same what important 12 30 Very important 13 32.5 Most important 4 10 Dimensional stability
Not important 2 5 Not very important 4 10 Same what important 16 40 Very important 14 35 Most important 4 10 Durability
Not important 2 5 Not very important 9 22.5 Same what important 11 27.5 Very important 14 35 Most important 4 10 Not wrinkle easily
Not important 3 7.5 Not very important 10 25 Same what important 12 30 Very important 11 27.5 Most important 4 10

EIT-MSCHOOL OF MECHANICAL AND INDUSTRIAL
ENGINEERING
Undergraduate program in industrial engineeringDear RespondentWe doing research on the title Optimization of product design through Quality Function Deployment and Analytical Hierarchy process to determine consumers need when buying T-Shirt on the city of Mekelle and around. One of the outcomes of this research is to integrate a consumer’s need with product technical requirement of T-shirt.

We would therefore value your opinion and would appreciate it if you could find time in your busy program to help us by completing the enclosed questionnaire. There is no right or wronganswers; we are interested in your opinion and experience.

The questioner has two parts the first one is to rank the most important requirement in buying a T-shirtand to give that best indicates how well you feel MAA Garment and Textile Factory satisfies each of therequirement which a questioner of customer of MAA Garment and Textile Factory of men’s t-shirt, whereas the second part is to production and technical department of MAA Garment and Textile Factory.

We would appreciate it very much if you could return the questionnaire, to the person who gave themto you within three days. Thank you for the courtesy of your assistance.Sincerely.

Customer survey
Using focus group discussion this are the identified consequence in men’s t-shirt need, so assign a number from 1 to 5 points, on a scale of 1 to 5 (1 =not important,2=not very important,3=somewhat important,4=very important and 5 = most important).

s/n Customer Needs (specification) Rating Scale
1 Easily wearable 1 2 3 4 5
2 Reasonable Price 1 2 3 4 5
3 Resistant to wear during washing 1 2 3 4 5
4 To be attractive 1 2 3 4 5
5 Match with many clothes 1 2 3 4 5
6 Look fashionable and trendy 1 2 3 4 5
7 Have appropriate Sizes 1 2 3 4 5
8 Dimensional stability 1 2 3 4 5
9 Durability (to be durable) 1 2 3 4 5
10 Not to wrinkle easily 1 2 3 4 5
Please rate how well MAA Garment and Textile Factory product of t-shirt delivers each of thoseneeds when you use it. Circle the number below that best indicates how well you feel the t-shirt satisfies each of the needs. For comparison purposes, use a scale of:Extremely Satisfied 5, Satisfied 4, Indifferent 3, Unsatisfied 2, Extremely Unsatisfied
s/n Customer Needs (specification) Rating Scale
1 Easily wearable 1 2 3 4 5
2 Reasonable Price 1 2 3 4 5
3 Resistant to wear during washing 1 2 3 4 5
4 To be attractive 1 2 3 4 5
5 Match with many clothes 1 2 3 4 5
6 Look fashionable and trendy 1 2 3 4 5
7 Have appropriate Sizes 1 2 3 4 5
8 Dimensional stability 1 2 3 4 5
9 Durability (to be durable) 1 2 3 4 5
10 Not to wrinkle easily 1 2 3 4 5
Company survey
Questioner for workers of MAA Garment and Textile Factory that work more on production of t-shirtand technical department.Using focus group discussion this are the identified product technical requirements thataimed at fulfilling need of men’s t-shirt, so assign a number from 1 to 5 points to identify nits difficult, on a scale of 1 to 5 (1 =not difficult,2=not very difficult ,3= difficult,4=very difficult and 5 = extremely difficult). You may assign the same number of points to morethan one product technical requirement.

s/n Technical requirement (HOW’s) Rating Scale based on company capability
1 Fiber content of Fabric 1 2 3 4 5
2 Appropriate Sewing machine setting 1 2 3 4 5
3 Range of colors and printing 1 2 3 4 5
4 Glossy 1 2 3 4 5
5 Suitable design 1 2 3 4 5
6 Anti wrinkle action 1 2 3 4 5
7 Resistance to dye 1 2 3 4 5
8 Ability not to form pilling 1 2 3 4 5
9 Type of material 1 2 3 4 5
10 Pattern making ability 1 2 3 4 5
Circle the number below that best indicates how well you feel that MAA Garment and Textiles Factory satisfy each of the product technical requirements. For comparison purposes, use a scale of: Extremely Satisfied 5, Satisfied 4, Indifferent 3, Unsatisfied 2, Extremely Unsatisfied 1.

s/n Technical requirement (HOW’s) Rating Scale based on company capability
1 Fiber content of Fabric 1 2 3 4 5
2 Appropriate Sewing machine setting 1 2 3 4 5
3 Range of colors and printing 1 2 3 4 5
4 Glossy 1 2 3 4 5
5 Suitable design 1 2 3 4 5
6 Anti wrinkle action 1 2 3 4 5
7 Resistance to dye 1 2 3 4 5
8 Ability not to form pilling 1 2 3 4 5
9 Type of material 1 2 3 4 5
10 Pattern making ability 1 2 3 4 5