Evaluating Factors Affecting Project Success: An Agile Approach
الموضوعات :Mohammad Sheikhalishahi 1 , Mohammad Amin Amani 2 , Ayria Behdinian 3
1 - School of Industrial Engineering, University of Tehran, Tehran, Iran
2 - University of Tehran
3 - University of Tehran
الکلمات المفتاحية: Genetic Algorithm, Simulation, Machine Learning, Agile project management, Scrum,
ملخص المقالة :
Selection of the most influential factors to improve the performance of organizations has consistently been a significant task for project managers. These underlying factors aim to prevent the failure of the project and to improve the performance of employees. The success of the organization's projects is directly correlated to customer satisfaction, time, cost, and product quality at the time of project completion. In this paper, after reviewing the literature on the elements influencing the project's success, the extent to which each factor affects the project's success is accessed. A practical data evaluation method to predict the most underlying item is a machine learning algorithm, a perfect contributory method for project managers to examine the influential factors. After identifying the component with the highest effect on the project success, validating the selected items in a real-world practice paves the way for assessing that factor's effectiveness. In this study, after selecting the Agile approach as the most notable, the simulation models were utilized to measure the proportion of organizational performance improvement. Agile Management, which is considered in the actual case, signifies implementing the Scrum method and all the definitions and phases related to this method in the organization. The analyzed Agile practice (Scrum) for the case study decremented the project cost and time substantially and enhanced the service and product quality.
[1] Abdel-Hamid, T.K., K. Sengupta, and C. Swett, The impact of goals
on software project management: An experimental investigation.
MIS quarterly, 1999: p. 531-555.
[2] Ramírez-Mora, S.L., H. Oktaba, and H. Gómez-Adorno,
Descriptions of issues and comments for predicting issue success in
software projects. Journal of Systems and Software, 2020. 168: p.
110663.
[3] Ling, F.Y.Y., et al., Human resource management practices to
improve project managers’ job satisfaction. Engineering,
Construction and Architectural Management, 2018.
[4] Nasir, M.H.N. and S. Sahibuddin, Critical success factors for
software projects: A comparative study. Scientific research and
essays, 2011. 6(10): p. 2174-2186.
[5] Sudhakar, G.P., A model of critical success factors for software
projects. Journal of Enterprise Information Management, 2012.
[6] Cerdeiral, C.T. and G. Santos, Software project management in high
maturity: A systematic literature mapping. Journal of Systems and
Software, 2019. 148: p. 56-87.
[7] Tohidi,H. Human resources management main role in information
technology project management. Procedia Computer Science,
2011. 3 p. 925-929.
[8] Saeed, S., et al., Analysis of Software Development Methodologies.
International Journal of Computing and Digital Systems, 2019.
8(5): p. 446-460.
[9] Subramaniam, C., R. Sen, and M.L. Nelson, Determinants of open
source software project success: A longitudinal study. Decision
Support Systems, 2009. 46(2): p. 576-585.
[10] Arbabi, H., M.-J. Salehi-Taleshi, and K. Ghods, The role of project
management office in developing knowledge management
infrastructure. Engineering, Construction and Architectural
Management, 2020.
[11] Taherdoost, H. and A. Keshavarzsaleh, A theoretical review on IT
project success/failure factors and evaluating the associated risks.
Mathematical and Computational Methods in Electrical
Engineering, 2015.
[12] Lyytinen, K. and R. Hirschheim, Information systems failures—a
survey and classification of the empirical literature. Oxford
surveys in information technology, 1988: p. 257-309.
[13] Murray, M. and G. Coffin, A case study analysis of factors for
success in ERP system implementations. AMCIS 2001
Proceedings, 2001: p. 196.
[14] Wallace, L., M. Keil, and A. Rai, Understanding software
project risk: a cluster analysis. Information & management,
2004. 42(1): p. 115-125.
[15] Niazi, M., et al., Toward successful project management in
global software development. International Journal of Project
Management, 2016. 34(8): p. 1553-1567.
[16] Zdanytė, K. and B. Neverauskas, The theoretical substation of
project management challenges. Economics & Management,
2011. 16.
[17] Scacchi, W., Process models in software engineering.
Encyclopedia of software engineering, 2002.
[18] Thomas, G. and W. Fernández, Success in IT projects: A
matter of definition? International journal of project
management, 2008. 26(7): p. 733-742.
[19] Stare, A., Agile project management in product development
projects. Procedia-Social and Behavioral Sciences, 2014.
119: p. 295-304.
[20] Saynisch, M., Beyond frontiers of traditional project
management: An approach to evolutionary, selforganizational principles and the complexity theory—results
of the research program. Project Management Journal, 2010.
41(2): p. 21-37.
[21] Špundak, M., Mixed agile/traditional project management
methodology–reality or illusion? Procedia-Social and
Behavioral Sciences, 2014. 119: p. 939-948.
[22] Williams, T., Assessing and moving on from the dominant
project management discourse in the light of project
overruns. IEEE Transactions on engineering management,
2005. 52(4): p. 497-508.
[23] Ciric, D., et al., Agile vs. Traditional Approach in Project
Management: Strategies, challenges and reasons to
introduce agile. Procedia Manufacturing, 2019. 39: p. 1407-
1414.
[24] Augustine, S., Managing agile projects. 2005: Prentice Hall
PTR.
[25] Takeuchi, H. and I. Nonaka, The new new product
development game. Harvard business review, 1986. 64(1): p.
137-146.
[26] Huang, C.-C. and A. Kusiak, Overview of kanban systems.
1996.
[27] Kropp, M., et al., Satisfaction and its correlates in agile
software development. Journal of Systems and Software,
2020. 164: p. 110544.
[28] Behutiye, W., et al., Management of quality requirements in
agile and rapid software development: A systematic mapping
study. Information and software technology, 2020. 123: p.
106225.
[29] Allisy-Roberts, P., et al., The 11th Annual State of Agile
Report. Journal of the ICRU, 2017. 6(2): p. 7-8.
[30] Bröchner, J. and U. Badenfelt, Changes and change
management in construction and IT projects. Automation in
Construction, 2011. 20(7): p. 767-775.
[31] Schwaber, K., Scrum development process, in Business object
design and implementation. 1997, Springer. p. 117-134.
[32] Schwaber, K., Agile project management with Scrum. 2004:
Microsoft press.
[33] Amani, M.A. and S.A. Sarkodie, Mitigating spread of
contamination in meat supply chain management using deep
learning. Scientific Reports, 2022. 12(1): p. 1-10.
[34] Amani, M.A. and F. Marinello, A deep learning-based model
to reduce costs and increase productivity in the case of small
datasets: A case study in cotton cultivation. Agriculture,
2022. 12(2): p. 267.
[35] Amani, M.A., M. Ghafari, and M.M. Nasiri, Targeted
vaccination for Covid-19 based on machine learning model:
A case study of Jobs' prioritization. Advances in Industrial
Engineering, 2021. 55(4): p. 433-446.
[36] Behdinian, A., et al., An integrating Machine learning
algorithm and simulation method for improving Software
Project Management: A real case study. Journal of Quality
Engineering and Production Optimization, 2022.
[37] Braga, P.L., A.L. Oliveira, and S.R. Meira. Software effort
estimation using machine learning techniques with robust
confidence intervals. in 7th international conference on
hybrid intelligent systems (HIS 2007). 2007. IEEE.
[38] Linares-Vásquez, M., et al., On using machine learning to
automatically classify software applications into domain
categories. Empirical Software Engineering, 2014. 19(3): p.
582-618.
[39] López-Martín, C. and A. Abran, Neural networks for
predicting the duration of new software projects. Journal of
Systems and Software, 2015. 101: p. 127-135.
[40] Tadayon, N. Neural network approach for software cost
estimation. in International Conference on Information
Technology: Coding and Computing (ITCC'05)-Volume II.
2005. IEEE.
[41] López-Martín, C., Machine learning techniques for software
testing effort prediction. Software Quality Journal, 2021: p.
1-36.
[42] Rathore, S.S. and S. Kumar, Software fault prediction based
on the dynamic selection of learning technique: findings from
the eclipse project study. Applied Intelligence, 2021: p. 1-16.
[43] Mehta, S. and K.S. Patnaik, Improved prediction of software
defects using ensemble machine learning techniques. Neural
Computing and Applications, 2021: p. 1-12.
[44] Klemen, P., et al. From high-level real-time software design
to low level hardware simulation: a methodology to evaluate
performances of control embedded systems. in PDes 2009,
Workshop on Programmable Devices and Embedded
Systems. 2009.
[45] Pfahl, D., et al., Evaluating the learning effectiveness of using
simulations in software project management education:
results from a twice replicated experiment. Information and
software technology, 2004. 46(2): p. 127-147.
[46] Uzzafer, M., A simulation model for strategic management
process of software projects. Journal of Systems and
Software, 2013. 86(1): p. 21-37.
[47] Li, D., et al., Dynamic simulation modelling of software
requirements change management system. Microprocessors
and Microsystems, 2021. 83: p. 104009.
[48] Ghane, K. A model and system for applying Lean Six sigma to
agile software development using hybrid simulation. in 2014
IEEE International Technology Management Conference.
2014. IEEE.
[49] Ramingwong, S. and L. Ramingwong, Plasticine scrum: an
alternative solution for simulating scrum software
development, in Information Science and Applications. 2015,
Springer. p. 851-858.
[50] Fisher, J., D. Koning, and A. Ludwigsen, Utilizing Atlassian
JIRA for large-scale software development management.
2013, Citeseer.
[51] Zuo, J., et al., Soft skills of construction project management
professionals and project success factors: A structural
equation model. Engineering, Construction and Architectural
Management, 2018.
[52] Lalsing, V., S. Kishnah, and S. Pudaruth, People factors in
agile software development and project management.
International Journal of Software Engineering &
Applications, 2012. 3(1): p. 117.
[53] Malik, M., S. Sarwar, and S. Orr, Agile practices and
performance: Examining the role of psychological
empowerment. International Journal of Project Management,
2021. 39(1): p. 10-20.
[54] Hassan, A., S. Younas, and A. Bhaumik, Exploring an Agile
Plus Approach for Project Scope, Time, and Cost
Management. International Journal of Information
Technology Project Management (IJITPM), 2020. 11(2): p.
72-89.
[55] Tam, C., et al., The factors influencing the success of ongoing agile software development projects. International
Journal of Project Management, 2020. 38(3): p. 165-176.
[56] Dikert, K., M. Paasivaara, and C. Lassenius, Challenges and
success factors for large-scale agile transformations: A
systematic literature review. Journal of Systems and
Software, 2016. 119: p. 87-108.
[57] Dhir, S., D. Kumar, and V. Singh, Success and failure factors
that impact on project implementation using agile software
development methodology, in Software engineering. 2019,
Springer. p. 647-654.
[58] Pinto, J.K. and D.P. Slevin, Critical success factors in R&D
projects. Research-technology management, 1989. 32(1): p.
31-35.
[59] Costantino, F., G. Di Gravio, and F. Nonino, Project selection
in project portfolio management: An artificial neural network
model based on critical success factors. International Journal
of Project Management, 2015. 33(8): p. 1744-1754.
[60] Amani, M.A., et al., A machine learning-based model for the
estimation of the temperature-dependent moduli of graphene
oxide reinforced nanocomposites and its application in a
thermally affected buckling analysis. Engineering with
Computers, 2021. 37(3): p. 2245-2255.
[61] Bassil, Y., A simulation model for the waterfall software
development life cycle. arXiv preprint arXiv:1205.6904,
2012.
