{"title":"BIM framework for efficient material procurement planning","authors":"Mohammadreza Kalantari , Hosein Taghaddos , Mohammadhossein Heydari","doi":"10.1016/j.autcon.2024.105803","DOIUrl":null,"url":null,"abstract":"<div><div>Inefficient procurement processes can lead to increased costs and project delays. Addressing information management inefficiencies is a significant but largely unexplored area within construction procurement strategies, despite potential for automation through Database Management Systems (DBMS) and Industry Foundation Classes (IFC). Subjective approaches constrain procurement planning, hindering optimal solutions. This paper addresses the gap by developing a comprehensive semi-automated procurement planning framework. The framework offers flexibility through a two-phased optimization employing Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), integrated with a Building Information Modeling (BIM)-driven database platform compatible with various modeling software. It enhances decision-making by considering indirect costs and allowing installment payments while generating a 4D schedule for improved supply chain stakeholder visualization and decision-making (e.g., project managers), demonstrating improvements over traditional procurement plans in a real-world case study. The developed framework enables future research on integrating real-time data, predictive analytics, and smart contracts to further enhance procurement management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105803"},"PeriodicalIF":9.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524005399","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Inefficient procurement processes can lead to increased costs and project delays. Addressing information management inefficiencies is a significant but largely unexplored area within construction procurement strategies, despite potential for automation through Database Management Systems (DBMS) and Industry Foundation Classes (IFC). Subjective approaches constrain procurement planning, hindering optimal solutions. This paper addresses the gap by developing a comprehensive semi-automated procurement planning framework. The framework offers flexibility through a two-phased optimization employing Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), integrated with a Building Information Modeling (BIM)-driven database platform compatible with various modeling software. It enhances decision-making by considering indirect costs and allowing installment payments while generating a 4D schedule for improved supply chain stakeholder visualization and decision-making (e.g., project managers), demonstrating improvements over traditional procurement plans in a real-world case study. The developed framework enables future research on integrating real-time data, predictive analytics, and smart contracts to further enhance procurement management.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.