The material below comprises excerpts from books by Dr. Johnathan Mun, our CEO and founder, such as Readings in Certified Quantitative Risk Management, 3rd Edition, and Quantitative Research Methods Using Risk Simulator and ROV BizStats Software Applying Econometrics, Multivariate Regression, Parametric and Nonparametric Hypothesis Testing, Monte Carlo Risk Simulation, Predictive Modeling, and Optimization, 4th Edition (https://www.amazon.com/author/johnathanmun). All screenshots and analytical models are run using the ROV Risk Simulator and ROV BizStats software applications. Statistical results shown are computed using Risk Simulator or BizStats. Online Training Videos are also available on these topics as well as the Certified in Quantitative Risk Management (CQRM) certification program. All materials are copyrighted as well as patent protected under international law, with all rights reserved.
All organizations depend heavily on project planning tools to forecast when various projects will complete. Completing projects within specified times and budgets is critical to facilitate smooth business operations. In our high-technology environment, many things can impact schedule. Technical capabilities can often fall short of expectations. Requirements are insufficient in many cases and need further definition. Tests can bring surprising results––good or bad. A whole host of other reasons can lead to schedule slips. On rare occasions, we may run into good fortune and the schedule can be accelerated.
Project schedules are inherently uncertain, and change is normal. Therefore, we should expect changes and find the best way to deal with them. So why do projects always take longer than anticipated? One reason is inaccurate schedule estimating. The following discussion presents a description of shortcomings in the traditional methods of schedule estimation and how simulation and advanced analytics can be applied to address these shortcomings.