scholarly journals Fuzzy Decision Support in the Early Phases of the Fuzzy Front End of Innovation in Product Development

2010 ◽  
Author(s):  
Sofiane Achiche ◽  
Francesco Paolo Appio
Keyword(s):  
Decision Support ◽  
Product Development ◽  
New Product Development ◽  
Data Sets ◽  
Fuzzy Decision ◽  
Opportunity Identification ◽  
Fuzzy Front End ◽  
Front End ◽  
The Cost ◽  
Learning Data

The innovation process may be divided into three areas: the fuzzy front end (FFE), the new product development (NPD) process, and commercialization. Every NPD process has a FFE in which products and projects are defined. Companies tend to begin the stages of FFE without a clear definition and analysis of the process to go from opportunity identification to concepts, and often they even abort the process or start over. Koen’s Model for the FFE is composed of 5 different phases, the first two being Opportunity Identification and Opportunity Analysis, which are the focus of this paper. Furthermore, several tools can be used by designers/managers in order to improve, structure and organize their work during the FFE. However, these tools tend to be selected and used in a heuristic manner. Additionally, some tools are preferred and more effective during specific phases of the FFE; hence an economic evaluation of the cost of their usage is very critical and there is also a need to characterize them in terms of their influence on the FFE. This paper focuses on decision support for managers/designers in their process of assessing the cost of choosing/using tools in the core front end activities, namely Opportunity Identification and Opportunity Analysis. This is achieved by analyzing the Influencing Factors (Firm context, Industry context, Macro environment) along with data collection from managers followed by the automatic construction of fuzzy decision support models (FDSM) of the discovered relationships. The decision support focuses upon the estimate investment needed for the use of tools during the 2 phases cited above. The generation of FDSMs is carried out automatically using a specialized genetic algorithm applied to learning data obtained from 5 experienced managers from 5 different companies. The automatically constructed FDSMs accurately reproduced the managers’ estimations using the learning data sets and were very robust when validated with hidden data sets.

Fuzzy decision support for tools selection in the core front end activities of new product development

2012 ◽  
Vol 24 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Sofiane Achiche ◽  
Francesco Paolo Appio ◽  
Tim C. McAloone ◽  
Alberto Di Minin
Keyword(s):  
Decision Support ◽  
Product Development ◽  
New Product Development ◽  
New Product ◽  
Fuzzy Decision ◽  
The Core ◽  
Front End

Application of risk-based fuzzy decision support systems in new product development: An R-VIKOR approach

2021 ◽  
pp. 107456
Author(s):  
Seyedeh Anahita Mousavi ◽  
Hamidreza Seiti ◽  
Ashkan Hafezalkotob ◽  
Sobhan Asian ◽  
Rouhollah Mobarra
Keyword(s):  
Decision Support ◽  
Product Development ◽  
Decision Support Systems ◽  
New Product Development ◽  
Support Systems ◽  
New Product ◽  
Fuzzy Decision

Choice of consumer research methods in the front end of new product development

2013 ◽  
Vol 55 (1) ◽  
pp. 81-104 ◽  
Author(s):  
Mariëlle Creusen ◽  
Erik Jan Hultink ◽  
Katrin Eling
Keyword(s):  
Product Development ◽  
Research Methods ◽  
New Product Development ◽  
Consumer Research ◽  
New Product ◽  
Consumer Information ◽  
Online Questionnaire ◽  
Fuzzy Front End ◽  
Front End ◽  
Size Type

This study investigates the choice of consumer research methods in the fuzzy front end (FFE) of the new product development (NPD) process. First, it delivers an up-to-date overview of currently available consumer research methods for use in the FFE of NPD. Second, using an online questionnaire, we obtain insights into the use of these consumer research methods by B-to-C companies based in the Netherlands (N = 88, including many major multinational companies). Third, these companies provided the major reasons for choosing these methods, and specified the types of consumer information that they aim to gather using these methods. Finally, we investigate the influence of company size, type of products developed (durable/non-durable) and product newness on the use of these methods. Based on these findings, we build a contingency framework that helps companies to improve their choice of consumer research methods in the FFE, where consumer insights are most important for new product success.

The Fuzzy Front End: Concept Development in the Automotive Industry

2014 ◽  
Author(s):  
Christer W. Elverum ◽  
Torgeir Welo ◽  
Martin Steinert
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Automotive Industry ◽  
Early Stage ◽  
Innovation Process ◽  
New Product ◽  
Product Development Process ◽  
Structured Interviews ◽  
Fuzzy Front End ◽  
Front End

The fuzzy front end (FFE) of new product development (NPD) is a term that refers to the early stages of the innovation process. This paper investigates the FFE in the automotive industry and addresses the challenges of working in this phase of the innovation process, as well as the academic definition of the FFE relative to the real world. Two parts of the innovation process have been identified and characterized as FFE: the concept-work within satellite front-end departments and the work within the pre-development phase of the vehicle new product development process. It has been identified that one of the greatest challenges related to working in the FFE is developing viable concepts that will “sell” internally. Estimating and conveying the overall value of the final product in terms of costs and customer benefits are two of the key elements that make it difficult to achieve internal “buy in”. Furthermore, it is argued that the most common academic perception of the FFE seem to be inadequate since it only concerns work that ends with a go/no-go decision whether to continue into development or not. Consequently, it fails to capture early-stage development work of transformational innovations, where the decision of development has already been made and the uncertainty is related to the execution of the work — and — not the outcome. Semi-structured interviews with a total of eleven employees at seven different automotive OEMs form the basis for the conclusions made herein.

AMBIGUITY REDUCTION IN NEW PRODUCT DEVELOPMENT PROJECTS

2008 ◽  
Vol 12 (04) ◽  
pp. 573-596 ◽  
Author(s):  
ERIC BRUN ◽  
ALF STEINAR SAETRE
Keyword(s):  
Product Development ◽  
New Product Development ◽  
New Product ◽  
Development Projects ◽  
Essential Component ◽  
Fuzzy Front End ◽  
Front End ◽  
Deductive Method ◽  
Ambiguity Reduction

In this paper, we argue that ambiguity is an essential component of "fuzziness" in the Fuzzy Front End of New Product Development (NPD), and that a better understanding of how ambiguity emerges and is reduced, is called for. We explore the process by which ambiguity was reduced in four NPD projects, and propose a model that enhances our understanding of this process. Ambiguity arises as multiple interpretations, and interpretations can be understood as hypotheses, hence these can be tested by using the hypothetical-deductive method (HDM). We present a model showing that ambiguity in NPD projects is efficiently reduced by applying the HDM to test the multiple interpretations that give rise to ambiguity and the assumptions underlying these interpretations. We discuss theoretical implications and the usefulness of the model for practitioners of NPD.

Sign in / Sign up

Export Citation Format

Share Document