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An estimation model of construction project segmentation for optimum project pricing

Author(s):




Medium: journal article
Language(s): English
Published in: Engineering, Construction and Architectural Management, , n. 9, v. 28
Page(s): 2361-2380
DOI: 10.1108/ecam-08-2020-0596
Abstract:

Purpose

For large projects, project segmentation and planning the size of contract packages in construction bids is a complex and critical issue. Due to the nature of construction projects, which frequently have large budgets, long durations and many activities with complex procedures, project segmentation involves complicated decision-making. To fill this gap, this study aims to develop an integrated model for planning project segmentation.

Design/methodology/approach

The proposed model integrates a simulation and multiple attribute decision-making method. The simulation is used to evaluate the bidding outcome of various project segmentations. The owner can then determine the bid-price behavior of contractors in response to varying work package sizes. The multiple attribute decision-making method is used to select the optimal segmentation solution from the simulated scenarios.

Findings

The proposed model is applied to a large road preservation project in Indonesia and incorporates bid participants and market conditions. The model provides seven scenarios for segmentation. The range of scenarios captures increasing competitiveness in the construction with the average bid price becoming gradually more beneficial for the owner. The model also utilizes a multiple attribute decision-making method to select the optimum scenario for the owner.

Originality/value

This study presents an applicable model for project segmentation that is useful for both project owners and contractors. By utilizing the proposed model, a project owner can segment a large project into smaller contract packages to create improved project pricing.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1108/ecam-08-2020-0596.
  • About this
    data sheet
  • Reference-ID
    10577085
  • Published on:
    26/02/2021
  • Last updated on:
    29/11/2021
 
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