Unrealistic resource estimation. Estimation is the primary basis to secure the resources required for project execution. Failure to understand the skills, labour hours, and time required to successfully address requirements frequently leads to budget overruns and project failures (McGrath, 2008; Lehtinen et al., 2014)
There are many ways to estimate the cost of a project. Some methods attempt a scientific approach, like Function Ponts or COCOMO, while others are basically based on gut feeling. None of them is accurate (Brade and Khalkar, 2013).
An old study (Kemerer, 1987) measured the accuracy of four different scientific methodologies. Although the sample was admittedly small, the study found an average error rate ranging from 85% to 772%.
The Standish Group suggests that success depends on five factors including size and agility: a smaller size (duration and number of team members) and an agile process correspond to a higher success rate (Gaikema, 2019). That suggests that smaller projects may be full of uncertainty, but, given the smaller size, the impact of errors is also small. Agile project management is also better suited than classic processes to compensate for the initial errors, suggesting that uncertainty cannot be avoided but only managed.
Today, a common method to estimate tasks is to assign points based on the Fibonacci scale. That scale conveys the idea of how unprecise estimations can become when the task is big. It is normally a good practice to split tasks into units that could be estimated with the first elements of the scale (1, 2, 3, or 5) reserving higher numbers (8, and 13) for rare exceptional cases.
References
Borade, J. G., & Khalkar, V. R. (2013). Software project effort and cost estimation techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 3(8). Available from https://www.researchgate.net/profile/Jyoti-Borade-2/publication/313243865_Software_Project_Effort_and_Cost_Estimation_Techniques/links/5bb7020ea6fdcc9552d3e638/Software-Project-Effort-and-Cost-Estimation-Techniques.pdf
Gaikema, M., Donkersloot, M., Johnson, J., & Mulder, H. (2019) Increase the success of Governmental IT projects. Systemics, Cybernetics and Informatics, 17(1), 97-105. Available from https://www.iiisci.org/Journal/pdv/sci/pdfs/JS052RM05.pdf
Kemerer, C. F. (1987). An empirical validation of software cost estimation models. Communications of the ACM, 30(5), 416-429. Available from https://dl.acm.org/doi/pdf/10.1145/22899.22906