THE USAGE OF FUZZY QUALITY FUNCTION DEPLOYMENT AND FUZZY FAILURE MODE AND EFFECTS ANALYSIS AT PRODUCT DESIGN PROCESS

Year 2012, Volume: 12 Issue: 23, 51 - 80, 01.06.2012

Esra Aytaç Muhsin Özdemir Selim Bekçioğlu

Abstract

The survival of the companies by differentiating themselves in a competitive market
is only possible by tending the issues such as creation, protection, development and
improvement of the quality. That requires the quality to be measured and analyzed. With
this aim many quality improvement methods have been developed. Quality Function
Deployment (QFD) and Failure Mode and Effects Analysis (FMEA) come at the
beginning of these methods.
Quality Function Deployment (QFD) is a customer-driven quality management and
product development system for achieving higher customer satisfaction. The basic
concept of QFD is to translate the customer requirements into product design or
engineering characteristics and subsequently into parts characteristics, process plans and
production requirements. Each translation uses a matrix, called the house of quality
(HOQ) which provides a conceptual map for the design process, as a construct for
understanding customer requirements and establishing priorities of engineering
characteristics to satisfy them. Decision making process of QFD is based on subjective
judgments and evaluations and linguistic terms such as ‘low importance’, ‘high
importance’, ‘strong relationship’ and ‘weak relationship’ are usually used by decision
makers. Most of these linguistic input variables are assumed to be precise and treated as
numerical data. And also the data available for product design is often limited and may be
inaccurate because of the uncertainties in the design process especially when a new
product is developed, a certain degree of vagueness is often inevitable.
Failure Mode and Effects Analysis (FMEA) is a powerful method for system safety
and the reliability analysis of products and processes in a wide range of industries. FMEA
is a method that determines current or possible failure modes in the product, the process
or the system as well as the causes, the effects and the occurrence frequency of these
failure modes. FMEA also ranks the failure modes according to their risks as a result of
these determinations. So the main objective of FMEA is to determine and prioritize the
potential failure modes that could have a detrimental effect on the system and its
performance. FMEA uses the risk priority number (RPN) while ranking the failure modes.
The RPN is obtained by multiplying of three factors, which are the probability of failure,
the severity of the failure and the probability of not detecting the failure. Some drawbacks
have emerged as a result of increasing industrial applications of FMEA. The main
drawback of RPN is various sets of probability of failure, the severity of the failure and
the probability of not detecting the failure may produce same value of RPN, however, the
risk implication may be totally different and may result in high-risk events going
unnoticed. The other important drawback of the RPN ranking method is that it neglects
the relative importance among probability of failure, the severity of the failure and the
probability of not detecting the failure. The three factors are assumed to have the same
importance but this situation is not suitable real life practical applications. And also
FMEA has inability of modeling the qualitative data applied during evaluation period as
well as the ambiguity of expert judgements used in the case of inadequate historical data
regarding the product and process under examination.
In order to eliminate these drawbacks of QFD and FMEA, in the literature these
methods have been combined with fuzzy logic and fuzzy set theory and organized as fuzzy QFD and fuzzy FMEA. Fuzzy sets and fuzzy logic are powerful mathematical tools
for modeling uncertain systems in many areas while making decisions in the absence of
complete and precise information. A fuzzy set is an extension of a crisp set and it is
characterized by a membership function which assigns to each object a grade of
membership ranging between zero and one.
In this study efforts have been made in order to indicate the feasibility of fuzzy FMEA
and fuzzy QFD in the cable production sector. These methods have been applied to a real
industrial case, which refers to one of the biggest cable company operated in Denizli,
Turkey. A product for a cable company has been developed using fuzzy QFD. In this
study the four-phase decision process has been considered. First of all the house of quality
has begun with the customer attributes about the cable and then engineering
characteristics have been defined by the company. The body and the roof of the house
have been filled and the most important engineering characteristic has been found. In the
next phase of this method, part matrix has been organized with the result of the first phase
and the most important part characteristic of the cable has been found. In the third phase
process matrix has been organized and the most important process during producing the
cable has been found. And finally the production matrix has been organized and the most
important production requirement or control action which is needed during the design and
production processes for the cable has been found. After defining the most important
engineering characteristic, part, process and control action of the cable, potential failure
modes which might occur in the engineering and part characteristic, production process
and the control action have been determined and evaluated in terms of occurrence of
failure, the internal severity of the failure, the external severity of the failure and not
detecting the failure by the decision makers. The decision makers in the cable company
have used linguistic variables during evaluation process. Finally the determined failure
modes have been ordered using fuzzy FMEA according to their riskiness. So the
difficulties like the lack of enough information during the product development process or
problems regarding the non-assignment of precise values for the risk factors concerning
failure modes have been eliminated via fuzzy logic approach. From now on, the company
will get a new ranking for every new product by adding new failure modes and be careful
against the unexpected situation during the new product development. It’s thought that
these methods will serve as early warning system for the company.

Keywords

QFD, FMEA, Fuzzy Sets, Fuzzy QFD, Fuzzy FMEA

ÜRÜN TASARIM SÜRECİNDE BULANIK KALİTE FONKSİYON GÖÇERİMİ VE BULANIK HATA TÜRÜ VE ETKİLERİ ANALİZİNİN KULLANIMI

Abstract

İşletmelerin kendilerini farklılaştırarak rekabet ortamında ayakta kalabilmeleri; kalitenin yaratılması, korunması, geliştirilmesi ve iyileştirilmesi gibi konulara eğilmeleri ile mümkün olmaktadır. Bu amaçla birçok kalite iyileştirme yöntemi geliştirilmiştir. Bu yöntemlerin başında Kalite Fonksiyon Göçerimi (KFG) ve Hata Türü ve Etkileri Analizi (HTEA) gelmektedir. Yöntemlerin bazı eksiklikleri bulunmaktadır. Literatürde bu eksiklikleri gidermek için KFG ve HTEA, bulanık mantık ve bulanık küme teorisi ile birleştirilerek bulanık HTEA ve bulanık KFG olarak düzenlenmiştir. Bu çalışmada da bulanık KFG ve HTEA’nın kablo sektöründe uygulanabilirliği gösterilmeye çalışılmış, bulanık KFG ile bir kablo işletmesi için ürün geliştirilmiş ve bu ürünün gerçekleşmesi esnasında çıkabilecek hata türleri, bulanık HTEA ile belirlenmiş ve risklerine göre sıralanmıştır. Tüm değerlendirme süreçlerinde karar vericiler, doğal dile dayanan sözel terimleri kullanmıştır.

Keywords

KFG, HTEA, Bulanık Kümeler, Bulanık KFG, Bulanık HTEA