Title: Human resource allocation in engineering projects: a stepwise approach using learning curve for predicting the required man-hour

Authors: Ahmad Ebrahimi; Mohsen Nozohouri; Rouhollah Khakpour

Addresses: Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran ' Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran ' Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran

Abstract: This paper recommends a stepwise method employing learning curves (LCs) to predict the man-hour for performing activities in engineering projects. It goes beyond existing applications of LCs and debates what specific neglected issues should be included and how they can be predicted through LCs. Focusing on man-hour prediction in engineering projects through LCs is not limited to improving the human resource allocation for performing activities, where, it has significant impacts on the improvement of different issues such as labour costs, quality of engineering services, time management in engineering projects, productivity, and competition capability in tenders. This paper identifies the best-known and widely used LCs in the literature and provides analysis in a real-life engineering, procurement, and construction (EPC) contracting company. Hence, the actual man-hour data for a specified engineering activity is gathered in a number of consecutive projects and analysed to select the best fit LC for prediction.

Keywords: learning curves; engineering projects; log-linear model; human resources; stepwise method.

DOI: 10.1504/IJPOM.2023.132713

International Journal of Project Organisation and Management, 2023 Vol.15 No.3, pp.351 - 374

Received: 19 May 2021
Accepted: 31 Dec 2021
Published online: 09 Aug 2023 *

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