Proposal of a Decision Support System and Model to Mitigate Scope Variability for New Product Development

Proposal of a Decision Support System and Model to Mitigate Scope Variability for New Product Development

Dhipan Raj Sundram, Kai Ling Lew, Christina Chin May May
Copyright: © 2023 |Pages: 20
DOI: 10.4018/IJDSST.315759
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Abstract

This paper proposes a decision support model (DSM) to be used by project managers (PM) in the research of new products within multinational companies to select the right option to mitigate scope variability during critical junctures like project continuity. On-the-shelf DSM are leveraged to overcome their shortcomings with more effective and relevant solutions. The proposed DSM is a consolidation of on-the-shelf DSM and methodologies such as balanced scorecard; quality function deployment; analytic hierarchy process; strengths, weaknesses, opportunities, and threats; and agile methodology, leading to an application in a decision support system (DSS) using progressive web applications. Feedback and input were collected through interviews with experienced PMs. Performance indicators were then developed to be reviewed by the PM to verify their applicability. Based on the feedback obtained, the developed model contributes to the engineering research industry as it mitigates common challenges that PMs face by efficiently providing a solution-seeking guideline.
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Background

Engineering Research and Development

Research in NPD is one of the main factors leading to sustainable growth in highly industrialized, modern knowledge-based economies (Abbassi et al., 2014). The rise in development expenditures and high failure rates in engineering research projects make project management very crucial. The significance of expenditure has risen since 2008, as the gross expenditure on research projects in Malaysia doubled from RM6,071 million in 2008 to RM15,060 million in 2018 (“Expenditure on Research and Development,” 2019). NPD projects for capital and consumer goods have also considerably high failure rates ranging from 35% - 45% given the high developmental costs (Castellion & Markham, 2013). Therefore, there is a need to prevent this issue by reducing the failure rate figures.

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