A simulation research framework for concurrent engineering project management
Date
2005
Authors
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Journal ISSN
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Publisher
Montana State University - Bozeman, College of Engineering
Abstract
In concurrent engineering, project tasks usually are interdependent among each other. Iterations, which are required for the interdependent tasks, make traditional PERT/CPM not applicable for the estimation of the project completion time. In addition, carrying out a large scaled project in a dynamic environment has to deal with various factors throughout the entire project life cycle. When estimating the project completion time, previous research often focused on one subject of interests and assumed the other factors causing little effects on the overall project duration. The objective of this thesis is to develop a simulation research framework to help estimate the project completion time and analyze the major factors that affect the estimation for complex concurrent engineering projects.
The framework consists of three major components: 1) Data Collection, where the needed data for simulation are prepared including project task structure, task relations, and quantified team member characteristics; 2) Simulation, where tasks are dynamically assigned to the appropriate members/engineers according to each member's knowledge level to the task, teamwork capability, work schedule availability, and learning curve improvement; and 3) Data Analysis, where significant factors to the project completion time are studied by the ANOVA analysis based on the simulation results. According to the findings from the ANOVA, heuristic rules are developed to improve the performance of task-member assignments. The effectiveness of the research framework, the simulation model and the heuristics is demonstrated by an illustrative example.
The framework consists of three major components: 1) Data Collection, where the needed data for simulation are prepared including project task structure, task relations, and quantified team member characteristics; 2) Simulation, where tasks are dynamically assigned to the appropriate members/engineers according to each member's knowledge level to the task, teamwork capability, work schedule availability, and learning curve improvement; and 3) Data Analysis, where significant factors to the project completion time are studied by the ANOVA analysis based on the simulation results. According to the findings from the ANOVA, heuristic rules are developed to improve the performance of task-member assignments. The effectiveness of the research framework, the simulation model and the heuristics is demonstrated by an illustrative example.