Open AccessMitigating Scope Variability for PMs in MNC in Research Projects Doing Research for Engineering NPDs Using A Decision Support System and Model
Author(s) -
Christina Chin,
Bsc Sandström,
) Qfd
Publication year - 2022
Publication title -
international journal of advanced research in engineering innovation
Language(s) - English
Resource type - Journals
ISSN - 2682-8499
DOI - 10.55057/ijarei.2022.4.1.4
Subject(s) - scope (computer science) , process management , balanced scorecard , agile software development , analytic hierarchy process , computer science , quality function deployment , decision support system , software deployment , engineering management , systems engineering , risk analysis (engineering) , engineering , operations research , business , operations management , value engineering , software engineering , artificial intelligence , programming language , operating system
This paper proposes a Decision Support Model (DSM) to be used by Project Managers (PM)s in the research of new products within multinational companies. PMs in engineering research projects shall be able to select the right option to mitigate scope variability during critical junctures like project continuity. On-the-shelf DSMs are leveraged to overcome their shortcomings with more effective and relevant solutions. The proposed DSM is a consolidation of on-the-shelf DSMs and methodologies such as Balanced Scorecard, Quality Function Deployment, Analytic Hierarchy Process, Strength, Weakness, Opportunities and Threats, and Agile methodology, leading to a Decision Support System (DSS). The application of the DSM into a DSS is done using Progressive Web Applications. The input was collected through interviews with PMs who have significant expertise in managing research projects. Performance indicators were then developed to be reviewed by industry experts to verify their applicability. Based on the feedback obtained, it is shown that the DSM and its application as DSS is effective as the surveyed PMs state that it is useful in certain situation. The developed model contributes to the engineering research industry as it mitigates common challenges that PMs face, by efficiently providing a solution-seeking guideline.