Mapping customer needs to engineering characteristics: an aerospace perspective for conceptual design

Designing complex engineering systems, such as an aircraft or an aero-engine, is immensely challenging. Formal Systems Engineering (SE) practices are widely used in the aerospace industry throughout the overall design process to minimise the overall design effort, corrective re-work, and ultimately overall development and manufacturing costs. Incorporating the needs and requirements from customers and other stakeholders into the conceptual and early design process is vital for the success and viability of any development programme. This paper presents a formal methodology, the Value-Driven Design (VDD) methodology that has been developed for collaborative and iterative use in the Extended Enterprise (EE) within the aerospace industry, and that has been applied using the Concept Design Analysis (CODA) method to map captured Customer Needs (CNs) into Engineering Characteristics (ECs) and to model an overall ‘design merit’ metric to be used in design assessments, sensitivity analyses, and engineering design optimisation studies. Two different case studies with increasing complexity are presented to elucidate the application areas of the CODA method in the context of the VDD methodology for the EE within the aerospace secto

Modelling customer satisfaction for product development using genetic programming

Product development involves several processes in which product planning is the first one. Several tasksnormally are required to be conducted in the product-planning process and one of them is to determinesettings of design attributes for products. Facing with fierce competition in marketplaces, companies try to determine the settings such that the best customer satisfaction of products could be obtained.To achieve this, models that relate customer satisfaction to design attributes need to be developed first. Previous research has adopted various modelling techniques to develop the models, but those models are not able to address interaction terms or higher-order terms in relating customer satisfaction to design attributes, or they are the black-box type models. In this paper, a method based on genetic programming (GP) is presented to generate models for relating customer satisfaction to design attributes. The GP is first used to construct branches of a tree representing structures of a model where interaction terms and higher-order terms can be addressed. Then an orthogonal least-squares algorithm is used to determine the coefficients of the model. The models thus developed are explicit and consist of interaction terms and higher-order terms in relating customer satisfaction to design attributes. A case study of a digital camera design is used to illustrate the proposed method

Evaluation Indicators and Development Strategies of Agricultural Revitalization for Rural Rejuvenation

The goal of rural rejuvenation is to establish newly regenerated rural villages via economic development and beautification. However, it is necessary to engage agriculture in rural areas as a basis to reach the goal. In order to effectively promote agricultural development, the objective of this study is to develop the related indicators as evaluation criteria. A modified Delphi method is applied to develop the questionnaire. The indicators are divided into two categories: requirement and implementation evaluation indicators. This implies indicators in both sides should be considered simultaneously for effectively promoting agricultural development. There are four dimensions, consisting of twelve items, which are included in requirement indicators. The four dimensions are to (1) activate agricultural production (2) to promote agricultural marketing (3) to construct the distinguishing features of rural life and culture, and(4) to develop leisure agriculture and rural village experiences. The implementation indicators are comprised of five dimensions including 21 items. The five dimensions are (1) community factors (2) human resource factors (3) local resource surveys (4) environmental and facilities planning, and (5) government subsidies and guidance. To determine the relative importance sequence of the target evaluation indicators, the fuzzy analytic hierarchy process (FAHP) is applied to calculate the weight for each item. Then, the quality function development method (QFD) is adopted to explore the relative importance sequence of implementing indicators. Based upon the important items of evaluation indicators, this study proposes the development strategies recommended for the agricultural authority

A fuzzy quality cost estimation method

Quality cost control is one of the most important aspects in the development of a quality management system. This paper presents a method for the estimation of quality cost that aims to take into account the so-called hidden quality costs, which are typically unobserved or unknown. Although this is a subject that has already been approached in other studies, subjectivity and uncertainty are not included in their formal approach, which any attempt to address hidden quality costs should include. Our methodology begins by observing the position each business occupies in Crosby’s Quality Management Maturity Grid. Obtaining the stage index on the basis of the experts’ opinions permits the valuation of the company’s membership for each of the stages of Crosby’s Maturity Grid. The application of Crosby’s corrector coefficient to an adequate weighting of the stage index makes it possible to obtain the fuzzy number quality cost. The measures obtained and their short-term predictions enable us to know the situation at all times and act accordingly, establishing precise corrective plans that will correct tendencies and make continuous improvement possible

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment

MARKOV CHAINS IN QUALITY FUNCTION DEPLOYMENT: AN EXAMPLE OF AUTOMATIVE SECTOR

Kalite Fonksiyon Göçerimi (KFG), müsteriyi tatmin etmeyi ve müsterinin talep ettiklerini tasarım hedeflerine ve üretim sırasında kullanılacak baslıca kalite güvence noktalarına dönüstürmek amacıyla tasarım kalitesini gelistirmeyi amaçlayan bir yöntemdir. Bu yöntemin bir asaması, müsteri gereksinimleri ile teknik gereksinimler arasındaki iliskiyi belirlemektir. Bu çalısmada iliskinin modellenebilmesi için Markov zincirlerinden yararlanılmıs ve otomotiv sektöründe otomobil sahiplerinin isteklerine yönelik otomobil tasarımı için kalite fonksiyon göçerimi uygulanmıstır. Bu anlamda Markov zincirlerinin temelinde bulunan olasılık ve geçis matrisleri yardımıyla müsteri gereksinimleri ile teknik gereksinimler arasındaki iliski, beklenen degerler bazında degerlendirilmis ve teknik gereksinimlerin gelecekte farklı dönemlerde alacagı degerler gözlemlenerek bir analiz yapılmıstır. Quality function deployment (QFD) is a method that aims satisfying customers and improving design quality for transforming customer requirements into design targets and quality assurance points that used during production. First step of this method determines the relationship between customer requirements and the technical requirements. Because of the uncertainty of quality by its nature, Markov chains are used for modeling the relationship correctly and applied to quality function deployment for the requirements of automobile owners at automobile industry. In this basis the relationship between customer requirements and the technical requirements is evaluated on the expected value base by means of probability and transition matrices which are the basic of Markov chains. An analysis is made by observing the value of technical requirements through different time periods

MARKOV CHAINS IN QUALITY FUNCTION DEPLOYMENT: AN EXAMPLE OF AUTOMATIVE SECTOR

Kalite Fonksiyon Göçerimi (KFG), müsteriyi tatmin etmeyi ve müsterinin talep ettiklerini tasarım hedeflerine ve üretim sırasında kullanılacak baslıca kalite güvence noktalarına dönüstürmek amacıyla tasarım kalitesini gelistirmeyi amaçlayan bir yöntemdir. Bu yöntemin bir asaması, müsteri gereksinimleri ile teknik gereksinimler arasındaki iliskiyi belirlemektir. Bu çalısmada iliskinin modellenebilmesi için Markov zincirlerinden yararlanılmıs ve otomotiv sektöründe otomobil sahiplerinin isteklerine yönelik otomobil tasarımı için kalite fonksiyon göçerimi uygulanmıstır. Bu anlamda Markov zincirlerinin temelinde bulunan olasılık ve geçis matrisleri yardımıyla müsteri gereksinimleri ile teknik gereksinimler arasındaki iliski, beklenen degerler bazında degerlendirilmis ve teknik gereksinimlerin gelecekte farklı dönemlerde alacagı degerler gözlemlenerek bir analiz yapılmıstır. Quality function deployment (QFD) is a method that aims satisfying customers and improving design quality for transforming customer requirements into design targets and quality assurance points that used during production. First step of this method determines the relationship between customer requirements and the technical requirements. Because of the uncertainty of quality by its nature, Markov chains are used for modeling the relationship correctly and applied to quality function deployment for the requirements of automobile owners at automobile industry. In this basis the relationship between customer requirements and the technical requirements is evaluated on the expected value base by means of probability and transition matrices which are the basic of Markov chains. An analysis is made by observing the value of technical requirements through different time periods

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account