Phase Of Quality Function Deployment Research Articles

Quality improvement of pillow product using Quality Function Deployment (QFD) methods at PT. XYZ

PT XYZ is a company engaged in the manufacturing of bedding goods. One of the products from this company that will be the object of research is the pillow. The number of disability pillow products that occurred during 2018 amounted to 21.69%, which could cause a decrease in company productivity and the shift of consumers of these companies to competing companies. The method used to improve the quality of pillow products is Quality Function Deployment. In QFD Phase I, consumer desires are translated into technical characteristics. The results of QFD Phase I, show that the highest priority technical characteristics are machine capacity with a difficulty level of 4, importance of 20% and estimated cost of 19%. Determination of product critical parts using QFD Phase II. The results of QFD Phase II show the highest priority of critical parts based on the level of difficulty, degree of importance and estimated cost is durability of 20%, measurement accuracy of 15% and fabric dimensions of 15%.

Designing Instant Corn Rice Using the Product Design Phase of Quality Function Deployment

Corn rice is one of popular foods in Madura. It refers to main meal consisting of rice grains and grinding corn. Often, it takes long time for preparation; thus, instant product of corn rice is needed. The purpose of this study aims to design Madura instant corn rice products according to consumer preference. Development of product design development involves Quality Function Deployment (QFD) method of the product design phase. The results of this study indicate eleven stages of the process carried out to make instant corn rice products. The product was made as follows: (1) sorting, carried out to select good quality corn, (2) milling, carried out to operate size reduction of the corn, (3) sieving, carried out to produce uniform corn rice faction, and (4) weighing. Washing and soaking of corn was required to remove foreign materials; then followed by steaming, drying 1, boiling, cooling, and drying 2. Ultimately, instant corn rice was packaged. In this regard, boiling process showed importance, with a value of 492,921, then sorting with a value of 454,721, and drying process with a value of 432,446.

Prioritizing critical parts of crumb rubber product by using quality function deployment (QFD) phase II : Product design matrix

Product quality is the most important thing in product design. To produce a qualified product, the designer must adjust the technical characteristics to the customer’s needs, so that the critical part of the crumb rubber product can be determined. The critical part of crumb rubber product is carried out using the second phase of quality function deployment (QFD) method. Quality Function Deployment (QFD) is a method used to improve the quality of a product by considering consumer needs associated with technical characteristics, and to obtain critical part priority, the designer must develop QFD phase II, which is a continuation of QFD phase I. In QFD phase I, the result obtained is a priority of technical characteristics, so the priority of technical characteristics becomes input for QFD phase II as attribute of consumer needs. The results obtained from QFD phase II method in this study is the critical part priority which is used as the corrected part of a crumb rubber product to improve the quality of the crumb rubber product type SIR 20. The critical part priority obtained is the resistance of the rubber product to the heat, the resistance of plasticity, and plastic resistance to heat.

The improvement of thresher design by using the integration of TRIZ and QFD approach

The research described in this paper aimed to propose an integrative method by using quality function deployment (QFD) and TRIZ approach to improve thresher design manufactured by SMEs in West Sumatra, Indonesia. The process consisted of the following stages: 1) identification of the customer needs followed by the determination of their requirements rating; 2) the preparation of QFD phase 1; 3) the creation of QFD phase 2 for technical characteristics which do not contradict with the design characteristics or the selection of alternative solutions using TRIZ for both contradictory characteristics; 4) the thresher design improvement according to the design characteristics in the final stage. The distinctions between the re-design threshers with the reference design were wheels to facilitate mobilisation; screw type threshing teeth design on the threshing cylinder to meet the user need; and the ergonomic feeding table dimension to improve the user convenience.

The improvement of thresher design by using the integration of TRIZ and QFD approach

The research described in this paper aimed to propose an integrative method by using quality function deployment (QFD) and TRIZ approach to improve thresher design manufactured by SMEs in West Sumatra, Indonesia. The process consisted of the following stages: 1) identification of the customer needs followed by the determination of their requirements rating; 2) the preparation of QFD phase 1; 3) the creation of QFD phase 2 for technical characteristics which do not contradict with the design characteristics or the selection of alternative solutions using TRIZ for both contradictory characteristics; 4) the thresher design improvement according to the design characteristics in the final stage. The distinctions between the re-design threshers with the reference design were wheels to facilitate mobilisation; screw type threshing teeth design on the threshing cylinder to meet the user need; and the ergonomic feeding table dimension to improve the user convenience.

Rough set-based approach for modeling relationship measures in product planning

Quality function deployment (QFD) provides a planning and problem-solving methodology that is widely renowned for translating customer requirements (CRs) into engineering characteristics (ECs) for new product development. As the first phase of QFD, product planning house of quality (PPHOQ) plays a very important role in this process. The degrees and directions of the relationship measures between CRs and ECs have serious effects on the special planning of ECs, modeling the relationship measures is an important step in constructing PPHOQ. The current paper presents a rough set (RS)-based approach for modeling relationship measures by determining the knowledge and experience of the QFD team, aided by the introduction of the type factor of a relationship used to express the effects of the relationship types. A study of general cases is used to demonstrate the performances and limitations of the proposed RS-based approach. The results show that the novel approach effectively determines the relative knowledge of the QFD team and facilitates decision-making in new product development.

PROMETHEE Group Decision Support System and the House of Quality

Quality function deployment (QFD) is a multi-step method that monitors customer needs throughout a product development process. The House of Quality (HOQ) exercise undertaken in the first phase of QFD is considered as the most important, since customer needs must be accurately translated into a set of technical requirements for the final product. This paper provides a PROMETHEE group decision support system (GDSS) approach that integrates the design preferences of the QFD team. We highlight the selection and ranking of the technical requirements in the HOQ exercise, where a group of multidisciplinary decision makers (DMs) in a globally dispersed QFD team is required to input their individual preferences. Our approach advances the HOQ group decision making context in three important areas. First, it treats each criterion and DM as unique in terms of the preference function and threshold levels. Second, it seeks a multi-criteria approach for the HOQ process, where some DMs may play a more important role than others on a certain criterion. Third, sensitivity analysis through the Geometrical Analysis for Interactive Assistance (GAIA) plane provides valuable information about the conflicts, similarities, or independencies between the criterion and the DMs, respectively. A case on an automotive part illustrates the performance of the PROMOTHEE approach with GAIA.

A rough set based data mining approach for house of quality analysis

As the first phase of quality function deployment (QFD) and the only interface between the customers and product development team, house of quality (HOQ) plays the most important role in developing quality products that are able to satisfy customer needs. No matter in what shape or form HOQ can be built, the key to this process is to find out the hidden relationship between customers’ requirements and product design specifications. This paper presents a general rough set based data mining approach for HOQ analysis. It utilises the historical information of customer needs and the design specifications of the product that was purchased, employs the basic rough set notions to reveal the interrelationships between customer needs and design specifications automatically. Due to the data reduction nature of the approach, a minimal set of customer needs that are crucial for the decision on the correlated design specifications is derived. The end result of the approach is in the form of a minimal rule set, which not only fulfils the goal of HOQ, but can be used as supporting data for marketing purposes. A case study on the product of electrically powered bicycles is included to illustrate the approach and its efficiency.

The extension of fuzzy QFD: From product planning to part deployment

By focusing on listening to the customers, quality function deployment (QFD) has been a successful analysis tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have attempted to apply the fuzzy set theory to QFD and have developed various fuzzy QFD approaches. Their models usually concentrate on product planning, the first phase of QFD. The subsequent phases (part deployment, process planning, and production planning) of QFD are seldom addressed. Moreover, their models often use algebraic operations of fuzzy numbers to calculate the fuzzy sets in QFD. Biased results are easily produced after several multiplicative or divisional operations. Aiming to solve these two issues, the objective of this study is to develop an extended fuzzy quality function deployment approach (E-QFD) which expands the research scope, from product planning to part deployment. In product planning, a more advanced method for collecting customer requirements is developed while the competitive analysis is also considered. In part deployment, the original part deployment table is enhanced by including the importance of part characteristics (PCs) and the bottleneck level of PCs. A modified fuzzy k-means clustering method is proposed to classify various bottleneck (or importance) groups of PCs. The failure mode and effects analysis (FMEA) is conducted for the high bottleneck (or high importance) group of PCs through the fuzzy inference approach. Moreover, E-QFD employs a more precise method, α-cut operations, to calculate the fuzzy sets in QFD instead of algebraic operations of fuzzy numbers. Finally, a case study is given to explain the analysis process of the proposed method.

Integration of marketing research techniques into house of quality and product family design

Quality function deployment (QFD) is a well-known technique used for designing products and services to reflect customer needs. The first phase of QFD, usually called house of quality (HOQ), is of fundamental and strategic importance in the QFD process. Although the traditional HOQ has been successfully used for several decades, it has often been faced with a number of problems in real application cases. This paper will discuss three main problems: (1) the conceptual gap between customers and designers, (2) the existence of the possible customer segments, and (3) the need for trade-off among different levels of customer needs, which tend to come up in the early stage of the HOQ process. In order to overcome these problems, a new methodology is proposed based on the integration of two marketing research techniques: conjoint analysis and two-stage cluster analysis. Conjoint analysis is used to bridge conceptual gap and to balance different levels of customer needs. A two-stage clustering method is employed to cluster customers into different segments based on the main benefits derived from the conjoint study, and to foster the development of a product family. This paper also introduces three indices, namely, the commonality percentage, the cost reduction, and the satisfaction percentage to analyze the results of developing a generic product in comparison with a product family. A case study on office chair is put forward to illustrate the performance of the proposed methodology.

A rough set enhanced fuzzy approach to quality function deployment

Quality function deployment (QFD) provides a systematic methodology to assist companies in developing quality products that are able to satisfy customer needs. The house of quality (HOQ), as the first phase of QFD, plays the most important role in product development. Frequently, fuzzy numbers are used to quantify the vagueness of linguistic terms so as to facilitate subjective assessments in the HOQ. However, the issue concerning how to determine the boundary intervals of fuzzy numbers remains unresolved. This work proposes a novel approach based on rough set theory, and introduces two concepts called rough number and rough boundary interval to address this issue. A comparative case study presented in this work shows that the proposed approach has significant advantages compared to the prevailing fuzzy number based method in processing subjective linguistic assessments in QFD.

Quality Function Deployment: A Comprehensive Review of Its Concepts and Methods

This paper attempts to present a comprehensive description of the quality function deployment (QFD) process to facilitate a clearer reference of the basis of QFD. In particular, the paper provides (i) a review of QFD's definitions and contents, (ii) an account of QFD's Four-Phase Model, (iii) a detailed description of house of quality, the first and most important phase of QFD, and (iv) a summary of QFD's benefits. The paper concludes with some common problems in implementing QFD and suggestions for possible solutions.

Application of multiattribute decision analysis to quality function deployment for target setting

In the product design and development process, quality function deployment (QFD) provides a comprehensive, systematic approach to support the design of new products intended to meet or exceed customer expectations. The authors use multiattribute value theory to support new product design and hence to augment a design team's experience and judgment. We introduce the concept of a target set, the set of all value score vectors that are at least as preferred as the value score vectors of any of the given design alternatives. Assuming mutually preferential independence, we characterize the target set and indicate how it can be used to support selecting targets: 1) for the level of customer satisfaction to be attained by the new product, for each customer requirement and 2) for design requirements, presumably based in part on information from engineering competitive assessments. We then use the target set concept to augment the product planning phase of QFD. The concepts are illustrated and evaluated through the retrospective application of the methodology to an actual surgical product (universal converter). Although the analysis was performed retrospectively, without knowledge of the market success of the product and its competitors, the results were consistent with product acceptance and provided valuable insights to the lead engineer.