scholarly journals Application of Quality Function Development Method to Establish Application of New Product Development Information System

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Chien-Wen Hung
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Fuzzy Theory ◽  
New Product ◽  
Product Development Process ◽  
Quality Function ◽  
Function Expansion ◽  
Processing Ability ◽  
Quality Function Development ◽  
Product Specifications

In the process of new product development, the customer's feeling is usually fuzzy phenomenon, how to evaluate various factors is to test the developer's intelligence, this study takes the new product development process as the research object, and applies the Quality Function expansion (QFD) method to establish a decision support system with fuzzy processing ability. In this study, the first development of quality function expansion (QFD) applied to Customer voice collection and analysis and conversion to product specifications. Then, the integration of fuzzy theory and the provision of different commodity development solutions as the best choice for products.

Reliability and Accuracy of Bootstrap and Monte Carlo Methods for Demand Distribution Modeling

2011 ◽  
Author(s):  
Swithin S. Razu ◽  
Shun Takai
Keyword(s):  
Monte Carlo ◽  
Product Development ◽  
Conjoint Analysis ◽  
New Product Development ◽  
Demand Uncertainty ◽  
New Product ◽  
Product Development Process ◽  
Choice Data ◽  
Product Concept ◽  
Demand Distribution

Estimation of demand is one of the most important tasks in new product development. How customers come to appreciate and decide to purchase a new product impacts demand and hence profit of the product. Unfortunately, when designers select a new product concept early in the product development process, the future demand of the new product is not known. Conjoint analysis is a statistical method that has been used to estimate a demand of a new product concept from customer survey data. Although conjoint analysis has been increasingly incorporated in design engineering as a method to estimate a demand of a new product design, it has not been fully employed to model demand uncertainty. This paper demonstrates and compares two approaches that use conjoint analysis data to model demand uncertainty: bootstrap of respondent choice data and Monte Carlo simulation of utility estimation errors. Reliability of demand distribution and accuracy of demand estimation are compared for the two approaches in an illustrative example.

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