Elkholosy, H; Ead, R; Hammad, A and AbouRizk, S (2024) Data mining for forecasting labor resource requirements: A case study of project management staffing requirements. International Journal of Construction Management, 24(5), pp. 561-572. ISSN 1562-3599
Abstract
Accurate labor resource allocation ensures that tasks are assigned to the most suitable individual(s) and the optimal number of staff are available to complete certain tasks, making it essential for the success of construction projects. This study proposes a methodology for forecasting labor resource requirements for upcoming projects using data mining techniques. The framework consists of two components, a data acquisition model and a forecasting model. The data acquisition model provides a structured approach for tracking and storing project data, while the forecasting model uses the stored project data and applies machine learning algorithms to predict labor requirements. A case study is used to illustrate the application of the framework in actual projects. The results indicate the usefulness of machine learning in providing low error estimates compared to methods currently adopted in the industry. The results also revealed that forecasting accuracy considerably increases as the number of historical projects increases which signifies the importance of the data acquisition model. The models presented in this study are expected to help guide and promote the application of more accurate workforce forecasting techniques.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | data mining; forecasting; machine learning; resource allocation |
| Index terms: | accuracy, project data, machine learning, construction project, methodology, forecasting, case study, data mining, resource allocation, data acquisition, project management, labour resource, labour requirement, estimate |
| Subjects: | data collection methods, data science, artificial intelligence, resource management, prediction and forecasting, financial and cost management, management, research methods, production management, project management theory and practice, professional development, business economics |
| Topics: | Digital Applications, Site Management, Information Management, Project Management, Research Practice, Business Strategy, Cost Management |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here