Project management Determining Budget

RQ: How can historical project data be utilized to improve budget estimation for future projects? Name Institution Course Name Instructor Due Date ABSTRACT This investigation explores the use of historical project data in making informed decisions on the budgetary information of the current project. It has been found that historical data enhances budget estimation accuracy and precision. Various methods were found to have been used in analyzing the historical data. All of the methods highlighted the importance of historical data in identifying key cost drivers and improving the models used for the estimation process. The practical significance of these findings has highlighted the use of historical data in making decisions, cost control, and enhancing stakeholder confidence. How can historical project data be utilized to improve budget estimation for future projects? I. Introduction Historical data represents a wide variety of information about previous projects, and different data types contribute to the accurate forecasting of projects. It is crucial to note that a project cost and benefit estimation in an exact way is a prerequisite for successful project management. To achieve this goal, the management team uses historical data to provide trends and generate hypotheses for the current project. Historical data is an important source of data, which can help in understanding the projects executed in the past. Besides, historical data gives the reader a clue into the costs, benefits, and challenges faced during the undertaking of the project (Higginbottom et al., 2021). In fact, through it, the project managers and stakeholders are able to understand what may happen and allocate the resources accordingly. The historical data leads to budget forecasting by the data-driven process that reduces the speculative part and hence improves the precision and reliability of the estimate. These forecasted costs include total cost estimation, maintenance effort estimation, and stochastic estimation, among other estimated costs (Kwon & Kang, 2018). Different studies that have employed different methodologies or techniques have transformed the historical data to point out the budgeting challenge of the current project. The method which is mostly applied is the three-point estimation approach, which is used extensively in project planning. This technique is used to approximate any ongoing task or project in three different ways to estimate it. The optimistic estimate is the most optimistic of these estimates, the pessimistic estimate is the most pessimistic of these estimates, and the most likely or the expected estimate is the third one of these estimates (Kreo, 2023). The planners normally produce the most optimistic estimate as it is based on probability (Kostiuk et al., 2020). R-value is yet another technique to calculate risk in costs. The main principle of this method is that the determination coefficient (R2) is calculated; meanwhile, its square root is also calculated (Paikun et al., 2017). The square root of the coefficient of determination gives r, a value that is known as the correlation coefficient. If the value of r is large, then this would show that the project has a high success rate. II. Method Various techniques have been applied to forecast the current budget based on historical information. The above table shows that various researchers relied on various research methods. On the other hand, other researchers have employed other relevant methods. Through the historical data analysis, Sharrard et al. (2008) applied the environmental LCA approach. LCA was applied to quantify the environmental indicators, such as greenhouse gas emissions, energy consumption, and material depletion. Furthermore, they were able to disclose the long-term maintenance costs and the estimated savings for every roofing material. From all of these methods, the regression analysis (R-value) can be employed in subsequent studies because it has a higher probability of accurately providing estimates from data. In the second place, the method is easy to use and can be applied to other simulation methods, like the Monte Carlo simulation. There are other methods that have also been found to be essential in estimating the current costs using historical data. For instance, in a study conducted by Gasia et al. (2021), the researchers used life cycle assessment (LCA) and life cycle costing (LCC) to project the current costs using historical data. The researchers developed different volume scenarios of electricity consumption from different countries with lower and higher temperatures starting from 2019 to 2022. Machine learning is another method that can be used to forecast costs for a new project. Machine learning (ML) refers to a collection of methods that automatically build predictions from complex data (Wasserbacher et al., 2022). This method can not only be used in forecasting but also in planning tasks that involve causal inference. III. Results and Discussion From the literature review, I have been able to rely on different findings. The survey shows that estimation techniques that use machine learning (ML) models, Bayesian networks (BN), and ensemble methods (EM) for software maintenance budget estimation have the highest accuracy in terms of forecast-based historical dat...