Quality Function Deployment-based optimization and exploration for ambiguity

Yang, YoungSoon; Jang, Beom-Seon; Yeun, Yun Seog; Lee, K. H.; Lee, K. Y.

doi:10.1080/0954482031000078072

Cited by 20 publications

(10 citation statements)

References 16 publications

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“…The target value of the product KC can be obtained using Model 1. We assume that the utility functions of the designer and customer have exponential shapes since it is the widely used utility functions as noted by Yang et al 18 We also assume that the range of the shaft weight of the competitor products is 3.979-14.935 kg. This range is used to derive the customer and designer utility functions and the feasible solution of the model as generally expressed by Eq.…”

Section: Numerical Example and Analysismentioning

confidence: 99%

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Optimization Models for Deriving Optimum Target of Key Characteristics

Rosyidi

Irianto

Cakravastia

et al. 2014

J. Adv. Manuf. Syst.

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The aim of this research is to develop optimization models in deriving optimum target of key characteristic (KC). There are two kinds of product KCs introduced in this paper, namely performance and dimension product KC. The performance product KC target values must be determined by balancing customer and designer utilities subject to design cost and time provided by a company. The KCs of a product can be visualized using a KC flow-down which shows the hierarchical structure of the product. The flow-down may consist of many levels from product KC to process KC. Using axiomatic design as a methodology to map the flow-down, we conclude that product KC, assembly-components KC, and process KC are in functional domain, physical domain, and process domain respectively. In this paper, the objective function of the model for deriving optimum product KC target is to minimize utility gap between customer and designer subject to design cost and time. The assembly-component KCs have to be derived considering the product KC targets. In the absence of product KC target, the objective function of the model is to maximize the desired effect or minimizing the undesired effect. In the existence of product KC target, the objective function of the model is to attain the target considering technical constraints of the product. We use a shaft design problem as a numerical example to show the implementation of the models.

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Section: Numerical Example and Analysismentioning

confidence: 99%

“…The torque that works on the shaft can be expressed in terms of power (Watt) and the turning velocity (rpm). Equation(18) can be used to express the torque on segments AB and BC. In the equation, P denotes the power and f denotes the turning frequency of the shaft.…”

mentioning

confidence: 99%

Optimization Models for Deriving Optimum Target of Key Characteristics

Rosyidi

Irianto

Cakravastia

et al. 2014

J. Adv. Manuf. Syst.

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show abstract

“…The equations are used as constraints in the optimization problem to find optimal target values for ECs. When a designed experiment is not practically possible, these approaches may be useful (Yang et al, 2003). Dawson and Askin (1999) have proposed a nonlinear mathematical program to establish optimal ECs by considering the cost and the time of product development.…”

Section: The Concept Of the Qfdmentioning

confidence: 99%

Integrating customer's preferences in the QFD planning process using a combined benchmarking and imprecise goal programming model

Cherif¹,

Chabchoub²,

Aouni³

2009

Int Trans Operational Res

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Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to achieve higher customer satisfaction by integrating various functions of an organization. The engineering characteristics (ECs) affecting the product performances are designed to match the customer attributes (CAs). However, from the viewpoint of the QFD team, product design processes are performed in imprecise environments, and more than one factor must be taken into account in determining the target levels of ECs, especially the limited resources and increased market competition. This paper presents an imprecise goal programming (GP) approach to determine the optimum target levels of ECs in QFD for maximizing customer satisfaction under resource limitation and considerations of market competition. Based on benchmarking data of CAs, the concept of satisfaction functions is utilized to formulate explicitly the customer's preferences and to integrate the competitive analysis of target market into the modelling and solution process. In addition, the relationships linking CAs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through a car door design example.

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“…An application of the methods to a real example was also provided. Yang et al [72] proposed a customer-oriented optimization method, called the QFD-based optimization method using the Normal-Boundary Intersection (NBI) method as an effort to reflect customer's preferences in making a trade-off between multiple objectives. Jacobs and Ip [73] presented a method of combining gamer segmentation and QFD methodology with the aim of matching games to gamer requirements and desires more reliably than at present.…”

Section: Qfd Analytical Approaches and Modelsmentioning

confidence: 99%

Manufacturing Quality Function Deployment: Literature Review and Future Trends

Oke

2013

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Abstract.A comprehensive review of the Quality Function Deployment (QFD) literature is made using extensive survey as a methodology. The most important results of the study are: (i) QFD modelling and applications are one-sided; prioritisation of technical attributes only maximise customer satisfaction without considering cost incurred (ii) we are still missing considerable knowledge about neural networks for predicting improvement measures in customer satisfaction (iii) further exploration of the subsequent phases (process planning and production planning) of QFD is needed (iv) more decision support systems are needed to automate QFD (v) feedbacks from customers are not accounted for in current studies.Keywords: QFD, literature review, house of quality, voice of the customer, customer satisfaction, improvement.

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Quality Function Deployment-based optimization and exploration for ambiguity

Cited by 20 publications

References 16 publications

Optimization Models for Deriving Optimum Target of Key Characteristics

Optimization Models for Deriving Optimum Target of Key Characteristics

Integrating customer's preferences in the QFD planning process using a combined benchmarking and imprecise goal programming model

Manufacturing Quality Function Deployment: Literature Review and Future Trends

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