scholarly journals Model for technological innovation integration and new product development in firms under innovation discontinuities

2020 ◽  
Vol 10 (2) ◽  
pp. 72-80
Author(s):  
Jorge Lino Alves ◽  
Selma Regina Martins Oliveira
Keyword(s):  
Technology Integration ◽  
Product Development ◽  
New Product Development ◽  
Technological Innovation ◽  
Information Needs ◽  
Phase 1 ◽  
New Product ◽  
Product Development Process ◽  
Promethee Ii ◽  
A Company

This article aims to contribute to a new planning policy in the development of innovative products. To do so, it presents a new modelling proposal to integrate technological innovation and new product development in a company of the traditional pewter sector in Portugal, under innovation discontinuities, carried out according to the following stages: Phase 1: Modelling of the information needs in product development process; Phase 2: Determining of technology integration dimensions to the product; and Phase 3: Evaluating the performance of technology integration dimensions to the product. A case study was conducted in a company of traditional tin sector in Portugal. The investigation was helped by the intervention of specialists. In order to reduce the subjectivity in the obtained results, the methods of Categorical Judgment Law, Artificial Neural Networks, the multi-criteria Electre III methods, compromised programming and Promethee II, multivariate analysis and the neuro fuzzy technology were used. The results were satisfactory, validating the present proposal.   Keywords: Modelling proposal, planning, integration, technological innovation, new product development, traditional pewter sector.  

scholarly journals Modeling the New Product Development Process

2009 ◽  
pp. 208-227 ◽  
Author(s):  
Jonathan D. Owens
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Development Process ◽  
New Product ◽  
Product Development Process ◽  
Successful Outcome ◽  
Positive Effects ◽  
Innovative Capabilities ◽  
Development Route ◽  
A Company

Success in new product development (NPD) can be considered a general aim for any company wishing to survive in the 21st Century. It has been found that positive effects can result from the existence of formal “blueprints” and “roadmaps” of the NPD process. This chapter discusses numerous NPD processes which can assist a company to capture what it does, and follow a structured development route, from which it is possible to gain a better understanding of how to improve the development process, and thus reap the potential and tangible benefits. This chapter’s focus is at organisations that are considering implementing a new product development (NPD) process in order to improve repeatability and ultimately sustainability of their innovative capabilities, a necessary and vital component for survival. It aims to bring an understanding of the underlying characteristics that may contribute to a potential and successful outcome during the development process within organizations, through the adoption of a structured NPD process.

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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