Elevate Project Success Rate with Right Judgement of Complexity and Skill Competency Gap
Thejasvi N1, Shubhamangala B R2
1Thejasvi N, Computer Science and Engineering, Jain University, Bengaluru, India.
2Dr. Shubhamangala B R, Professor, Research Head, Bengaluru, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12898-12906 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7759118419/2019©BEIESP | DOI: 10.35940/ijrte.D7759.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Software project management constantly keep fighting triple constraint of Quality, Schedule and Time. Failures are due to poor visibility of Project complexity and incorrectly estimating right skilled team. The project success rates are constantly falling in spite of implementing various project management principles involving waterfall, Scrum, Scaled Agile framework (SAFe), etc. The approach taken to estimate the team composition are based on non-contextual, unscientific methodology based on previous project experiences by the team. As our study found that, every organization had its unique approach to derive root cause of their failures and had custom-built estimation templates. A deep-rooted empirical research is under taken here addressed around three Capability Maturity Model (CMM) level 5 organizations. Often complexity is under-estimated and this is the enemy of software estimation. If this hypothesis is right, complexity metric can forecast the depth of estimation challenges and can help in prioritizing task and various intricate efforts needed and thereby deduce the appropriate team to navigate toward project success. Our Research was under taken in below two phases: Phase I – To investigate and address poor project complexity definition through quantification of project complexity Phase II – To derive metric around skill-capability Index in conjunction with project complexity. In Phase I, interviews and deliberation techniques were leveraged involving senior software practitioners. In Phase II, evaluation of the unsuccessful projects from the initiation until operational stage was carried out. Combining the phase I and II results, overall reasons contributing to incorrect mapping of skilled staffing was found. This paper presents two research contributions, firstly an approach to uncover poor estimation of skilled staff for a project with correct complexity mapping, secondly skill-competence index in line with project complexity quantification as a multi-dimensional solution to overcome project failures. Proposed model was found to be productive in real projects; in addition, the results showed that complexity metric estimated with this unique approach had low false positive rate and minimal deviations in project outcomes.
Keywords: Project complexity; Project Failure; Project Evaluation; Skill-Capability Index.
Scope of the Article: Analysis of Algorithms and Computational Complexity.