A tri-approach for modeling and analyzing project interdependencies in portfolios
وكيل مرتبط
Bashir, Hamdi,, مشرف الرسالة العلمية
تاريخ النشر
2020
اللغة
الأنجليزية
الكلمة الدالة
نوع الرسالة الجامعية
Dissertations
الملخص
Despite the academics and practitioners' increasing focus on project portfolio management (PPM) over the past three decades, only a few methods have been proposed for modeling and analyzing interdependencies among projects within a portfolio. This study proposes a novel tri-approach that integrates three techniques, namely social network analysis (SNA), fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), and cross-impact matrix multiplication applied to classification (MICMAC). Network mapping provides project managers with a holistic view of interdependencies among projects, while fuzzy TOPSIS MICMAC and SNA measures are used for classifying projects in terms of their driving power and dependence power and out- and in-degree centrality. This categorization provides project managers with an effective tool for distinguishing among projects, classifying them based on their interdependency levels thus, identifying critical projects. The proposed approach is demonstrated and validated through modeling and analyzing the interdependencies between projects within a real-life portfolio from the industry. Three types of validity are utilized, namely face validity, external validity, and internal validity. The validity results are based on semi-structured interviews with experts from different industries and the real-life portfolio. The validity results indicate that experts are satisfied with the approach findings, considering a few revisions in the findings. Therefore, the proposed approach can serve as a viable and practical means of capturing and analyzing interdependencies among projects within a portfolio.
ملاحظة
Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Ph.D. in Department of Industrial Engineering and Engineering Management, 2020.
القالب
أطروحات الدكتوراة
تصنيف مكتبة الكونجرس
HD69.P75 Z333 2020
المعرف المحلي
b13475071