Improving Subcontractor Selection process in Industrial Projects Using Data Mining Techniques
Keywords:
Subcontractor selection, Construction Management, Industrial projects, Data Mining, Business IntelligenceAbstract
Subcontractor selection process in industrial projects is one of the most important decisions in industrial project management, and insufficient attention to this such process may cause some major threats to a project. On the other hand, construction companies produce a huge amount of data that is distributed across many databases, which are not used to support these kinds of decisions for future projects in this company
In this research, a data warehouse was designed which is suitable for construction enterprises that are specialized in industrial projects by using the operational databases of its projects, including planning information and executive details during the life cycle of previous projects, and use it to organize, understand and use data to support subcontractor selection decisions for ongoing projects and the projects which will be constructed in the future.
The dimensional model was designed according to the requirements of construction enterprises and the available data. Online analytical processing OLAP has been applied which provides a suitable environment for direct queries and produces required reports that help to support construction management decisions and evaluate the subcontractors for each type of work. Data mining technique has been used to forecast the delay in all project systems based on the chosen subcontractor to execute these systems.
Applying the proposed methodology and using business intelligence techniques will improve the quality of subcontractor selection decisions, and hence the economic performance of construction enterprises using the proposed data warehouse and its feedback process
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