Article Preview
TopIntroduction
Organizations undergo daily problems that need to be rectified. Project management mainly targets any future problems that the organization is likely to encounter, as well as the possible dangers that the problems may bring in the future (Ali & Bhaskar, 2016; Besner & Hobbs, 2012; Zwikael, & Smyrk, 2012; Parker, Parsons, & Isharyanto, 2015). Project managers are expected to plan the projects, supervise, and control all activities that determine the success of the projects. Since projects are normally exposed to great risks that threaten their success, the risk management tools are meant to ensure that the risks are reduced, controlled, or avoided (Carley & Prietula, 2014; Ahern, Leavy, & Byrne, 2014; Cova & Salle, 2005; Galli, Kaviani, Bottani, & Murino, 2017). A project management environment is where the project takes place, and it normally has a great impact on the project. Thus, the environment needs to have favorable conditions that will impact the project positively.
In the project environment, there is the interaction of various factors that include the physical factors, social factors, organizational factors, physiological factors, financial factors, cultural factors, operational factors, ecological factors, and economic factors. The effectiveness of all of these factors will estimate the success of the project. An unsuccessful project is usually an expense to the organization that causes great loss because the investment was expected to bring about profits. If a project is unsuccessful, then the investment becomes a waste, and no profit is expected. Thus, projects need to employ the use of statistical analysis tools to analyze the data and generate conclusions to make decisions. Several tools can be used in statistical analysis, such as activity diagrams, critical chain project management, critical path method, critical path drag, program evaluation and review technique, drag cost, etc.
Research Statement
Effective and ineffective statistical analysis tools in project management are aspects that largely determine the failure or success of an organization. This paper focuses on analyzing the two types of statistical analysis tools that are effective and ineffective in the environment of project management, which depends on how the tools are applied to the data and the environment in which the analysis is to be conducted.
Literature is available that stresses these variables, their concepts, and models as important factors in project management and performance, but a research gap has developed. Research does not address the part that these variables, their concepts, and models play in allowing a smooth progression in project management and performance. This study will attempt to fill this research void by focusing on assessing the elements and applications of these variables, their concepts, and models. The objective is to see overlaps and disparities to reveal any likenesses and differences. Afterwards, this study aims to prepare a universal framework, so as to amass the best practices and elements from the current model. This framework would apply to any form of project, operation, and performance. Also, answers are based on evidence for any primary questions from experts on these variables, their concepts, and models, such as how to best utilize them for project management and performance success. With this study, future researchers can use the findings as a platform on which to build a more detailed evaluation of this subject.
Managerial Relevance
Engineering managers must make vital decisions for project management and engineering. In the future, these decisions will grow in importance, and this study addresses future topics for the engineering management practitioner. This study also addresses how such future topics apply to engineering management, as well as reasons why the engineering manager must consider this in their operations, project management lifecycle, and project management settings. The implications of this study are addressed, and the findings are evaluated within various organizational levels: the corporate level, the managerial level, and the project team level. Lastly, the conclusions are useful to an engineering management practitioner for capitalizing on the variables, concepts, models, and their relationship from various project environment and operation levels.