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Project Scheduling - Improved Approach

Last reviewed: April 8, 2013 ~5 min read
Abstract

This project consisted of a summary and critique of V. Khodakarami, N. Fenton and M. Neil's (2007) study, "Project Scheduling - Improved Approach to Incorporate Uncertainty Using Bayesian Networks." An introduction is followed by the summary and critique as well as its applicability to project integration management, project scope management and project time management. A summary of the research and important findings are presented in the conclusion.

¶ … Project Scheduling - Improved Approach to Incorporate Uncertainty Using Bayesian Networks" by V. Khodakarami, N. Fenton and M. Neil's (2007)

Although all projects are unique in some fashion, they all share the common management constraints of uncertainty. This paper provides a summary and critique of Khodakarami, Fenton and Neil's (2007) study, "Project Scheduling- Improved Approach to Incorporate Uncertainty Using Bayesian Network," followed by an analysis concerning how the article relates to the concepts of project integration management, project scope management and project time management. Finally, a summary of the research and important findings concerning Khodakarami et al.'s project scheduling study are provided in the conclusion.

Summary and Critique

A fundamental characteristic of successful risk management is the ability to effectively manage uncertainty. Indeed, uncertainty resulting from a lack of comparable experience in the past (a major source of uncertainty), the need to trade off between performance measures and a dearth of accurate information can all cause problems in project scheduling (Khodakarami et al., 2007). The Khodakarami et al. study examines how the widely used scheduling technique, critical path method, can be incorporated into Bayesian networks to provide project managers with improved uncertainty management capabilities by providing quantifiable measures of the different aspect of uncertainty. The integration of the critical path method is noteworthy because this method has been shown to provide improved decision information for project planning as well as generating information that is valuable for project management purposes as well (Lauren, 2008).

In their study, Khodakarami et al. report that risk management is a key area of project management as described by the Guide to the Project Management Body of Knowledge, 2000 edition ("PMBOK Guide") and a central issue involved in risk management is the ability to handle uncertainty. According to Khodakarami et al., "Current project risk management processes induce a restricted focus on managing project uncertainty because the term 'risk' has become associated with 'events' rather than the more general sources of significant uncertainty" (p. 39). Although the critical path method is among the most widely used project management tools, it does not take uncertainty into account, a gap that is filled by Khodakarami et al.'s integration of Bayesian networks. According to these researchers, "The most well established approach to handling uncertainty in these circumstances is the Bayesian approach. Where complex causal relationships are involved, the Bayesian approach is extended by using Bayesian networks. The challenge is to incorporate the critical path method into Bayesian networks" (Khodakarami et al., p. 40).

To their credit, the authors preface their discussion of Bayesian networks with a concise description of the critical path methodology and notation that helps readers understand the need for the integration of an uncertainty component. In this regard, Khodakarami et al. report that, "Critical path method is very simple and provides very useful and fundamental information about a project and its activities schedule. However, because of its single-point estimate assumption, it is too simplistic to be used in complex projects" (p. 41). The authors' extensive use of acronyms, though, makes it difficult to understanding some of their more esoteric descriptions of the integration of critical path methods into Bayesian networks notwithstanding their use of illustrative graphics in support of their assertions. Despite this constraint, the authors describe several aspects of their innovative approach that can contribute to project management in several ways, including the following.

Project Integration Management

Project integration management involves the processes required for overall project coordination (Kendrick, 2003). The methodology developed by Khodakarami et al. allows for the development of various "what-if" scenarios that can model the effects of different processes at different points in time (Khodakarami et al.).

Project Scope Management

The methodology developed by Khodakarami et al. may be less useful for project scope management applications. In this regard, project scope management describes the project processes that are required in order to ensure that the project "defines the deliverable(s) and that it includes all the work required, and only the work required, for successful project completion" (Kendrick, 2003, p. 10). The work breakdown structure of the critical path method is regarded as being part of project scope management as defined in the PMBOK Guide (Kendrick, 2007).

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References
3 sources cited in this paper
  • Kendrick, T. (2003). Identifying and managing project risk: Essential tools for failure-proofing your project. New York: AMACOM.
  • Khodakarami, V., Fenton, N. & Neil, M. (2007, June). Project scheduling: Improved approach to incorporate uncertainty using Bayesian networks. Project Management Journal, 39-49.
  • Lauren, B. (2008, May). The case for e-project management. ABA Banking Journal, 100(5), 47- 51.
Cite This Paper
PaperDue. (2013). Project Scheduling - Improved Approach. PaperDue. https://www.paperdue.com/essay/project-scheduling-improved-approach-89124

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