Strategy for resource allocation process: a data-driven model for optimal candidate matching

Abstract

Efficient Resource Allocation to projects is a significant challenge for companies, as it directly affects key metrics such as opportunity costs, project efficiency, and client satisfaction. This study aims to develop an automated process for Resource Allocation, inspired by the consulting industry, specifically designed to improve efficiency by minimizing the time spent on allocating resources and increasing the accuracy of matching personnel to project requirements. The research addresses two main problems: the time-consuming nature of traditional Resource Allocation methods and the lack of precision in identifying the ideal fit for project roles, often leading to inefficiencies and suboptimal results. The approach follows the Design Science Research (DSR) methodology, which ensures the development of innovative and practical solutions through iterative design and evaluation. The process analyzes key project characteristics and automatically selects the most suitable individual for the task, thus optimizing both time and Resource Allocation accuracy. Several factors related to project typology and organizational needs are parametrized throughout the process to enhance decision-making precision. The results demonstrate that the proposed model significantly reduces the time required for Resource Allocation and increases the precision of assigning individuals to projects. This leads to higher project success rates and improved satisfaction among stakeholders. The main contributions of this study are threefold: first, the development of an automated Resource Allocation model designed to address specific organizational challenges; second, the enhancement of the decision-making process by making it faster, more precise, and less prone to human error; and third, the creation of a scalable and flexible model that can be adapted to various industries.