Improving the Predictive Validity of Quality Function Deployment by Conjoint Analysis: A Monte Carlo Comparison

Author(s):  
Daniel Baier ◽  
Michael Brusch
2016 ◽  
Vol 13 (2) ◽  
pp. 618
Author(s):  
Desrina Yusi Irawati ◽  
Moses Laksono Singgih ◽  
Bambang Syarudin

The advantages of QFD is to translate customer need into a technical response. But QFD has some disadvantages related to the difficulties in distinguishing the difference of needs between consumers, difficulties to fulfill the needs of different consumer groups, and the exietence of conceptual gap between consumers and companies. The proposed method to overcome these disadvantages is conjoint analysis. The main advantage of conjoint analysis is the ability to get the optimal design combination for products or services based on consumers' preference. The result of conjoint analysis, estimation of perceived value, and integration of QFD can be used to know the preference market needs among consumers, identify the office desk, determine consumer segments and technical respons, and estimate the additional price of office desk attributes as an effort to the development of the office desk. In overall the best Office desk combination results based on consumers' preference of Office desk is the white color without additional drawers or supporting features, with table size is 120x60x75 cm and footstool. Segmentation based on preferences resulting in three clusters, namely size, color, and availability of drawer. The highest technical response to be the company's priority in meeting the needs of consumers is to make the proper hole connection. Based on the perceived value, the company is capable to predict that the additional prices of 1 drawer is Rp.1-Rp.500.000, the addition price of 2 drawers is Rp.800,000 - Rp.900.000, and the addition price of the foundation of the foot is Rp.50.000 - Rp.150.000, and the additional price of supporting features is Rp.150.000-Rp.250.000.Keywords: Quality Function Deployment (QFD), conjoint analysis, segmentation and perceived value.AbstrakKeunggulan QFD adalah menterjemahkan customer need menjadi respon teknis. Namun QFD mempunyai kekurangan terkait sulit membedakan antara beragam kebutuhan konsumen yang bertentangan, sulit memenuhi kebutuhan konsumen yang berbeda kelompok, dan kesenjangan konseptual antara konsumen dan perusahaan. Untuk melengkapi kekurangan QFD, diusulkan metode conjoint analysis. Keunggulan utama conjoint analysis mampu mendapatkan kombinasi desain yang optimal untuk produk yang melekat pada preferensi konsumen. Hasil integrasi QFD dan conjoint analysis serta estimasi perceived value dapat mengetahui preferensi konsumen meja kantor, mengidentifikasi segmen konsumen meja kantor, menentukan respon teknis, dan mengestimasi harga penambahan atribut meja kantor sebagai upaya pengembangan meja kantor. Secara keseluruhan hasil kombinasi meja kantor terbaik berdasarkan preferensi konsumen meja kantor adalah warna putih, tidak membutuhkan penambahan fitur laci, tidak membutuhkan penambahan fitur pendukung, ukuran meja 120x60x75 cm, dan terdapat tumpuan kaki. Berdasarkan segmentasi preferensi terbentuk tiga klaster, yaitu klaster warna, klaster ukuran, dan klaster ketersediaan laci. Secara keseluruhan respon teknis yang menjadi prioritas perusahaan untuk memenuhi kebutuhan konsumen adalah pembuatan lubang sambungan yang tepat. Berdasarkan hasil perceived value, perusahaan dapat memperkirakan harga penambahan 1 laci berkisar Rp.1 - Rp.500.000, penambahan 2 laci adalah Rp. 800.000 – Rp. 900.000, penambahan tumpuan kaki Rp. 50.000 – Rp. 150.000, dan penambahan fitur pendukung Rp. 150.000-Rp. 250.000.Kata kunci: Quality Function Deployment (QFD), conjoint analysis, segmentasi, dan perceived value


1981 ◽  
Vol 18 (1) ◽  
pp. 101-106 ◽  
Author(s):  
Dick R. Wittink ◽  
Philippe Cattin

Conjoint analysis has been applied in a large number of commercial projects as well as in many noncommercial studies. Often MONANOVA, a nonmetric technique, is applied to a preference rank order obtained for a set of hypothetical objects. The authors report simulation results obtained for four alternative estimation procedures, ANOVA, LINMAP, LOGIT, and MONANOVA. The results suggest, within the limitations of the simulation study, that ANOVA may be the preferred procedure for compensatory models, whereas LINMAP is most likely to provide the best predictive validity for models with a dominant attribute.


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