Influencing project success outcomes by utilising advanced statistical techniques and AI during the project initiating process

Research output: Book chapter/Published conference paperChapter (peer-reviewed)peer-review

Abstract

Employing advanced statistical techniques to intelligent data analysis scenarios within the Retail IT project management industry against an underlying phenomenological Cynefin framework, this study proposes the use of the complementary techniques of factor, cluster, and regression analysis on a stratified random sampling of a large and historical dataset to measure and predict the optimal combination of variables leading to successful project outcomes. Statistical analysis techniques will enable inference from the data that is not otherwise directly measurable and where subjectivity is inherent in human analysis. Delivering statistical validation of the ability to identify Cynefin complexity category at Initiation and predict the optimal assignment of Project Manager and delivery methodology is expected to correlate to project success outcomes. Automating this data analysis in subsequent studies using a cognitive machine learning approach and Robotic Process Automation (RPA) tools will assist in providing recommendations for optimal alignment of complexity ranking, delivery methodology, and project manager skills prior to project commencement.
Original languageEnglish
Title of host publication Computational statistical methodologies and modelling for Artificial Intelligence
EditorsPriyanka Harjule, Azizur Rahman, Basant Agarwal, Vinita Tiwari
Place of PublicationBoca Raton
PublisherCRC Press
Chapter10
Pages254-272
Number of pages19
Edition1
ISBN (Electronic)9781003253051
ISBN (Print)9781032170800
DOIs
Publication statusPublished - 01 Jan 2023

Fingerprint

Dive into the research topics of 'Influencing project success outcomes by utilising advanced statistical techniques and AI during the project initiating process'. Together they form a unique fingerprint.

Cite this