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Implementation of IT Project Management Control and Achieving the Control Objectives using Hybrid Extended Methodology
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 155 - Number 2 |
| Year of Publication: 2016 |
| Authors: Anirudh Arunan, Krishan Kumar, Manjula R. |
10.5120/ijca2016912266
|
Anirudh Arunan, Krishan Kumar, Manjula R. . Implementation of IT Project Management Control and Achieving the Control Objectives using Hybrid Extended Methodology. International Journal of Computer Applications. 155, 2 ( Dec 2016), 27-34. DOI=10.5120/ijca2016912266
Abstract
This paper looks into the various aspects and components of project management control with a view to identify a new methodology using a hybrid model to achieve control objectives. Classical approaches to project management control are explored. The best known methods of Project management are Water fall 3 and Scrum methods 3. However in a real world scenario, neither of these methodologies may be the best fit. Hence with the help of empirical project data an attempt is made to analyze options and see how effective is a hybrid model in achieving optimal control. To achieve this, additional control criteria like quality control and development of reusable components etc are used. The analysis is carried out using specific IT project implementation data of a medium size project. The project was implemented using standard Software Development Life Cycle (SDLC) techniques.
References
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- Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria by Roger Atkinson Department of Information Systems, The Business School, Bournemouth University, Talbot Campus, Poole, Dorset BH12 5BB, UK
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- Portfolios of Control Modes and IS Project Management by Laurie S. Kirsch, Katz Graduate School of Business, The University of Pittsburgh, Pittsburgh, Pennsylvania
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- Project Management Methodologies: A Review of the Literature by Pamela McClinton
- Project Controls to Minimize Cost and Schedule Overruns: A Model, Research Agenda, and Initial Results David N. Ford , James M. Lyneis , and Timothy R.B. Taylor
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