A Dynamic Planning Approach for New Product Development

Abstract

This paper presents a quantifying and reordering method for new product development based on the concurrent engineering concept. We propose to establish a fuzzy design structure matrix (FDSM) by quantifying the information flow of activity factors of new product development and employing the fuzzy clustering method. Utilizing the proposed partition and tearing algorithm to reorder matrixes, we can realize deeply the inner hierarchy workings of concurrent engineering in the new product development process. The purpose of this paper is to develop a dynamic planning method that is innovative, efficient and flexible for new product development by using the concurrent design concept and FDSM analysis. It ultimately helpful to designers and managers to grasp the interactive information flow between each of the activity factors for planning an optimum design process in new product development. One case study is employed to illustrate this method and the result validates that it can reduce the iterations of design process and shorten the product development time.Key Words: new product development (NPD), concurrent engineering (CE), fuzzy design structure matrix (FDSM), dynamic planning method.

A fuzzy approach for sequencing interrelated activities in a DSM

“…In the results to be presented, examples 1 and 2 draw data from two real DSMs published in Luh et al (2009) and Rowles (1999), demonstrating that our method can effectively handle practical…”

“…Moreover, in cases of developing systems with an entirely new technology and/or with an entirely new set of performance features, historical data are usually unavailable (Lin et al 2009, Luh et al 2009). In such situations, activity dependencies are difficult to predict accurately and all that is available is an educated guess of the possible values.…”

“…However, owing to the presence of inexact information, uncontrollable environment and so on, information dependencies among activities are often uncertain and cannot be precisely predicted. This is true especially for large complex systems where hundreds of interrelated activities are involved or in situations where historical data are unavailable (Chen and Lin 2003, Chen et al 2004, Lin et al 2009, Luh et al 2009). To address this issue, one natural approach is to treat uncertain activity dependencies as random variables with specified probability distributions.…”

“…Since each feedback corresponds to one or more guesses of information input, minimising the total feedback value will also minimise the amount of information required to be supplied as initial guesses. Examples of studies that have addressed this important issue include Steward (1981), Kusiak and Wang (1993), Tang et al (2000), Ahmadi et al (2001), Karniel et al (2005), Luh et al (2009), andQian et al (2011). Several other objectives of scheduling interrelated activities are proposed and examined in Smith and Eppinger (1997), Abdelsalam and Bao (2006), Meier et al (2007), and Lancaster and Cheng (2008).…”

“…The idea of using fuzzy sets to represent uncertain activity dependencies is not new and has already been explored by Luh et al (2009). In their work, the fuzzy activity dependencies are first defuzzified by the interviewees into crisp numbers, and then a simple heuristic is applied to order the interrelated activities with defuzzified (i.e.…”