Program Evaluation and Review Technique (PERT) Analysis to Predict Completion Time and Project Risk Using Discrete Event System Simulation Method

Authors

  • I Gusti Agung Anom Yudistira Bina Nusantara University
  • Rinda Nariswari Bina Nusantara University
  • Samsul Arifin Institut Teknologi Sains Bandung
  • Abdul Azis Abdillah Politeknik Negeri Jakarta
  • Puguh Wahyu Prasetyo Universitas Ahmad Dahlan
  • Nanang Susyanto Universitas Gadjah Mada

DOI:

https://doi.org/10.21512/commit.v18i1.8495

Keywords:

Program Evaluation and Review Technique (PERT), Completion Time, Project Risk, Discrete Event System Simulation

Abstract

The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility.

Dimensions

Plum Analytics

Author Biographies

I Gusti Agung Anom Yudistira, Bina Nusantara University

Statistics Department, School of Computer Science

Rinda Nariswari, Bina Nusantara University

Statistics Department, School of Computer Science

Samsul Arifin, Institut Teknologi Sains Bandung

Data Science, Faculty of Engineering and Design

Abdul Azis Abdillah, Politeknik Negeri Jakarta

Mechanical Engineering Department

Puguh Wahyu Prasetyo, Universitas Ahmad Dahlan

Mathematics Education Department, Faculty of Teacher Training and Education

Nanang Susyanto, Universitas Gadjah Mada

Mathematics Department, Faculty of Mathematics and Natural Sciences

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Published

2024-04-29
Abstract 259  .
PDF downloaded 85  .