Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

A Predictive Model for Optimizing Claims and Risks in the Tendering Stage of Construction Projects

Document Type : Original Article

Authors
1 Ph.D. Student, Construction Engineering and Management, Department of Civil Engineering and Earth Resources, Islamic Azad University, Central Tehran Branch, Tehran, Iran
2 Assistant Professor, Department of Civil Engineering and Earth Resources, Islamic Azad University, Central Tehran Branch, Tehran, Iran
Abstract
Participation in tenders and investment in construction projects involves numerous factors and significant complexities. Decision-making in this context is highly sensitive and critical due to constant fluctuations in the economic conditions of the construction industry and the target investment environment. Consequently, companies require accurate information and rigorous data analysis, often expending substantial time and financial resources on human expertise to address these challenges. It is evident that a comprehensive and precise evaluation of project conditions prior to winning a tender plays a fundamental role in achieving ultimate success for all project stakeholders. To address this need, and within the context of Iran’s construction industry, key decision-making variables were identified to develop a deterministic mathematical model based on an operational research approach. The model incorporates four primary variables: 1) maximum investment cost and bid price, 2) project duration and associated time-based costs, 3) projected net profit, and 4) likelihood of claims occurrence and related claim costs. Using this model, tender decisions can be assessed according to overall price indices, construction duration, expected profit, and potential claims with their corresponding costs. Ultimately, it is concluded that such a model cannot rely solely on analytical operational research solutions and must be calculated using numerical approaches and approximations.
Keywords

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Volume 7, Issue 1
Winter 2025
Pages 30-45

  • Receive Date 23 December 2024
  • Revise Date 02 February 2025
  • Accept Date 16 March 2025