Key-Driver Analysis with Extended Back-Propagation Neural Network Based Importance-Performance Analysis (BPNN-IPA)

Kahraman, Cengiz; Yanık Özbay, Seda

doi:10.1007/978-3-319-24499-0_15

Key-Driver Analysis with Extended Back-Propagation Neural Network Based Importance-Performance Analysis (BPNN-IPA)

Atıf İçin Kopyala

Kahraman C., Yanık Özbay S.

INTELLIGENT DECISION MAKING IN QUALITY MANAGEMENT: THEORY AND APPLICATIONS, cilt.97, ss.447-461, 2016 (Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 97
Basım Tarihi: 2016
Doi Numarası: 10.1007/978-3-319-24499-0_15
Dergi Adı: INTELLIGENT DECISION MAKING IN QUALITY MANAGEMENT: THEORY AND APPLICATIONS
Derginin Tarandığı İndeksler: Scopus, zbMATH
Sayfa Sayıları: ss.447-461
İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Importance-performance analysis (IPA) is a popular prioritization tool used to formulate effective and efficient quality improvement strategies for products and services. Since its introduction in 1977, IPA has undergone numerous enhancements and extensions, mostly with regard to the operationalization of attribute-importance. Recently, studies have promoted neural network-based IPA approaches to determine attribute-importance more reliably compared to traditional approaches. This chapter describes the application of back-propagation neural networks (BPNN) in an extended IPA framework with the goal of discovering key areas of quality improvements. The value of the extended BPNN-based IPA is demonstrated using an empirical case example of airport service quality.