Strategy development of contractor with Contractor Full Pre-Finance (CPF) scheme using risk-based approach to increase cost performance of toll road development projects

Wisnu Isvara, Mohammad Ichsan, Farras Ammar Muhammad, Jodi Noor Handibyanto, Sri Bramantoro Abdinagoro

Research output: Contribution to journalReview articlepeer-review

Abstract

The construction of the toll road in Indonesia is currently being carried out very intensively and is also highly prioritized, marked by the status of the National Strategic Project (NSP) which oversees it. One of the efforts made to accelerate and increase attractiveness is to use the Contractor’s Full Pre-Finance (CPF) scheme. However, the CPF contract scheme burdens the contractor with risks that were previously owned by the project owner. To overcome these problems, this research was conducted to develop a contractor implementation strategy to mitigate and reduce the impact of each risk on toll road projects with the CPF scheme. Through expert opinion, this research validates 22 risk indicators which are divided into 5 risk categories (Politics, Economics, Construction, Contracts, and Project Management) in toll road projects with CPF schemes that affect cost performance. Then, the dominant risk indicators were identified through case studies of remaining work items on the XYZ Toll Road Project. It was later discovered that these dominant risk indicators had an effect on the project’s Cost Overrun rate of 18.41%, 21.56%, 23.03%, and 24.08% based on PERT analysis and Monte Carlo simulation at the confidence level p = 80%, 90%, and 95%. To reduce the level of the risk, a strategy was carried out with 16 preventive and 13 corrective actions for “vital few” work items with the potential to reduce the project’s Cost Overrun rate to 11.01%, 13.28%, 14.46%, and 15.35%.

Original languageEnglish
Article number2243718
JournalCogent Engineering
Volume10
Issue number1
DOIs
Publication statusPublished - 2023

Keywords

  • contractor Full Pre-Finance
  • cost performance
  • Monte Carlo simulation
  • PERT

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