scholarly journals A Complete MCDM Model for NPD Performance Assessment in an LED-Based Lighting Plant Factory

2018 ◽  
Vol 2018 ◽  
pp. 1-24
Author(s):  
Wen-Chin Chen ◽  
Yen-Fu Lin ◽  
Kai-Ping Liu ◽  
Hui-Pin Chang ◽  
Li-Yi Wang ◽  
...  
Keyword(s):  
Decision Making ◽  
Product Development ◽  
New Product Development ◽  
Crop Yields ◽  
Rapid Development ◽  
New Product ◽  
Fuzzy Decision Making ◽  
Trade Offs ◽  
Fuzzy Delphi ◽  
Plant Factory

Globally, industries and economies have undergone rapid development and expansion over the last several decades. As a result, global warming and environmental contaminations have resulted in climate change and jeopardized food security. In many developing countries, already decreasing crop yields are threatened by extreme weather and soil damaged by genetically modified food, making environmental problems worse and increasing food and organic product prices. For these reasons, this study proposes a hybrid multicriteria decision-making (MCDM) model for new product development (NPD) in the light-emitting diode- (LED-) based lighting plant factory. First, literature reviews and expert interviews are employed in constructing a list of decision-making objectives and criteria for new product development. Then, a fuzzy Delphi method (FDM) is used to screen the elements of the objectives and criteria, while a fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is used to determine the relationships among the objectives and criteria. Finally, a fuzzy analytic network process (FANP) and a composite priority vector (CPV) are manipulated to determine the relative importance weights of the critical objectives and criteria. Results show that the proposed method can create a useful and assessable MCDM model for decision-making applications in new product development, and a case study is herein performed to validate the feasibility of the proposed model in a Taiwanese LED-based lighting plant factory, which not only provides the decision-makers with a feasible hierarchical data structure for decision-making guidance but also increases the competitive advantages of trade-offs on developing novel products.

A new fuzzy decision-making approach for selecting new product development project

2016 ◽  
Vol 24 (3) ◽  
pp. 240-250 ◽  
Author(s):  
Chiu-Chi Wei ◽  
Agus Andria ◽  
Houn-Wen Xiao ◽  
Chiou-Shuei Wei ◽  
Ting-Chang Lai
Keyword(s):  
Decision Making ◽  
Product Development ◽  
New Product Development ◽  
New Product ◽  
Development Project ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Product Development Project

An Integrated Evaluation Model for New Product Development in Light-Emitting Diode Plant Lighting Industry

2013 ◽  
Vol 690-693 ◽  
pp. 577-581
Author(s):  
Wen Chin Chen ◽  
Li Yi Wang ◽  
Hui Pin Chang
Keyword(s):  
Decision Making ◽  
Product Development ◽  
New Product Development ◽  
Light Emitting Diode ◽  
New Product ◽  
Assessment Model ◽  
Light Emitting ◽  
Fuzzy Delphi ◽  
Lighting Industry ◽  
Interpretive Structural Model

This study would create a decision making model for new product development (NPD) in light-emitting diode (LED) plant lighting industry. At first literature reviews and expert interviews will be employed to list five perspectives and 19 criteria thereof. A fuzzy Delphi method (FDM) will be then used to screen factors for the criteria. An interpretive structural model (ISM) will be managed to get the relationships among the perspectives and among the criteria. A fuzzy analytic network process (FANP) will be finally manipulated to compute the weights of all criteria. Results of the study will provide an assessment model of NPD decision making for LED plant lighting industry.

scholarly journals Product Portfolio Management: An Important Business Strategy

2015 ◽  
Vol 7 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Mishelle Doorasamy
Keyword(s):  
Decision Making ◽  
Product Development ◽  
New Product Development ◽  
Portfolio Management ◽  
Business Strategy ◽  
New Product ◽  
Product Portfolio ◽  
Historical Developments ◽  
Statistical Trends ◽  
The Relationship

Abstract The aim of this article is to provide reader with a comprehensive insight on the theories, empirical findings and models of Product Portfolio Management (PPM) during new product development. This article will allow for an in-depth theoretical approach on PPM and demonstrate to managers the importance of adopting PPM as business strategy during decision making. The objective of this paper is to present a literature review of models, theories, approaches and findings on the relationship between Product Portfolio Management and new product development. Relevant statistical trends, historical developments, published opinion of major writers in this field will be presented to provide concrete evidence of the problem being discussed.

Application of Statistical Analysis Tools and Concepts to Big Data and Predictive Analytics to New Product Development

2020 ◽  
Vol 3 (1) ◽  
pp. 17-35
Author(s):  
Brian J. Galli
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Product Development ◽  
Competitive Advantage ◽  
New Product Development ◽  
New Product ◽  
Big Data Analysis ◽  
Life Cycles ◽  
The Impact

In today's fiercely competitive environment, most companies face the pressure of shorter product life cycles. Therefore, if companies want to maintain a competitive advantage in the market, they need to keep innovating and developing new products. If not, then they will face difficulties in developing and expanding markets and may go out of business. New product development is the key content of enterprise research and development, and it is also one of the strategic cores for enterprise survival and development. The success of new product development plays a decisive role both in the development of the company and in maintaining a competitive advantage in the industry. Since the beginning of the 21st century, with the continuous innovation and development of Internet technology, the era of big data has arrived. In the era of big data, enterprises' decision-making for new product development no longer solely relies on the experience of decision-makers; it is based on the results of big data analysis for more accurate and effective decisions. In this thesis, the case analysis is mainly carried out with Company A as an example. Also, it mainly introduces the decision made by Company A in the actual operation of new product development, which is based on the results of big data analysis from decision-making to decision-making innovation. The choice of decision-making is described in detail. Through the introduction of the case, the impact of big data on the decision-making process for new product development was explored. In the era of big data, it provides a new theoretical approach to new product development decision-making.

New Product Development

2010 ◽  
pp. 192-215
Author(s):  
Robert S. Friedman ◽  
Desiree M. Roberts ◽  
Jonathan D. Linton
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Development Process ◽  
New Product ◽  
Product Development Process ◽  
Second Phase ◽  
Trade Offs ◽  
The Many ◽  
And Control ◽  
Functional Areas

The articles addressed in this chapter on new product development can be classified in two general categories—papers that address the internal processes that assist or hinder development, and those that focus on factors that contribute to a new product’s success or failure in terms of performance and diffusion. We begin with Cooper and Kleinschmidt (1986), who report on the second phase of the New Prod project. Its goal was to examine the nature of the steps that affect the development process and determine how the step-wise structure was modified by the developer companies in order to improve process performance. Clark (1989) looks at project scope, or the extent to which in-house part development affects new product development and overall project performance. The new product development process, as a comprehensive scope of work, is the subject of Millison, Raj, and Wilemon’s (1992) discussion, specifically what the tensions and trade-offs are that occur among different functional areas and how they affect innovative product development. Wheelwright and Clark (1992) provide insight into strategies to plan, focus, and control a firm’s project development, offering an aggregate project plan that promotes management clearly delineating the roles and steps of each participant’s activities. Griffin and Page (1993) offer a practitioner’s framework that identifies and coordinates the many measures of product development success and failure, and holds them up against existing measures used by academic researchers. We then move to Souder’s (1988) article examining the relationship between R&D groups and marketing groups, the nature of the problems between them, and the structure of potentially effective partnerships.

The Differential Impact of New Product Development “Make/Buy” Choices on Immediate and Future Product Quality: Insights from the Automobile Industry

2017 ◽  
Vol 81 (6) ◽  
pp. 1-23 ◽  
Author(s):  
Kartik Kalaignanam ◽  
Tarun Kushwaha ◽  
Tracey A. Swartz
Keyword(s):  
Product Development ◽  
Product Quality ◽  
New Product Development ◽  
Automobile Industry ◽  
Positive Impact ◽  
New Product ◽  
The United States ◽  
Trade Offs ◽  
Using Data ◽  
The Impact

This article examines the impact of new product development (NPD) “make/buy” choices on product quality using data from the automobile industry. Although the business press has lamented that NPD outsourcing compromises product quality, there is no systematic evidence to support or refute this assertion. Against this backdrop, this study tests a contingency model of the impact of NPD make/buy decisions on immediate and future product quality. The hypotheses are tested using data on NPD make/buy choices of 173 models of 12 automobile firms in the United States between 2007 and 2014. The authors find that whereas NPD buy has a more positive impact on immediate product quality, NPD make has a more positive impact on future product quality. Furthermore, the immediate product quality impact of NPD buy is stronger when (1) technologies are more complex and (2) firm NPD capability is higher. In contrast, the future product quality impact of NPD make is stronger when (1) there is postlaunch adverse feedback and (2) firm NPD capability is higher. The study highlights the complex trade-offs associated with NPD make/buy decisions and offers valuable insights on how firms could manage these decisions.

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