Despite the developments in the process and tool infrastructure in the software world, project success has not significantly improved. In software projects, the definition of project success means to produce products that the customer desires in the planned effort, time and budget. To achieve this goal, planning, analysis, design, coding, integration, testing and delivery processes are operated interactively from the beginning to the end of a software project. Metrics of these processes are used to measure the performance of software projects. Since the literature review shows that project management process metrics such as budget, effort, schedule, customer satisfaction, product quality are used in measuring project performance, more comprehensive and effective criteria are needed to be defined and applied in measuring project performances. Due to the importance of the project performance evaluation, a general evaluation model was created in this study. The proposed model is designed for use in the software industry. In terms of project performance, a model has been developed that focuses on management of project, requirement, risk, quality and configuration, development, verification and validation processes. The purpose of this article is to present a model that evaluates the performance of software projects and expresses project success with a numerical value. Analytical hierarchy process (AHP) was used to calculate the relative importance of each process metric criterion and sub-criteria that provide input to the performance evaluation. Statistical process control method was used in the evaluation of project performance and calculation of the project success score. It was operated in an R&D organization to verify the proposed model and the performance of a project in delivery phase to the customer was measured. It is thought that the model presented in this study will help the managers, who monitor the project status, to evaluate project performance, as well as provide the numerical comparison of performance between projects.
Project Performance Evaluation Project Success Score Analytical Hierarchy Process Statistical Process Control
Primary Language | English |
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Subjects | Industrial Engineering |
Journal Section | Research Articles |
Authors | |
Publication Date | June 1, 2020 |
Submission Date | November 21, 2019 |
Acceptance Date | April 3, 2020 |
Published in Issue | Year 2020 Volume: 24 Issue: 3 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.