Affiliation:
1. Universidad Peruana de Ciencias Aplicadas, Universidad Nacional Mayor de San Marcos, Peru
2. Universidad Nacional Mayor de San Marcos, Peru
Abstract
Productivity in software factories is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities. One of the pillars of competitiveness is productivity, and it is related to the effort required to accomplish the assigned tasks. However, there is no standard way to measure it, making it difficult to establish policies and strategies to improve the factory. In this work, a model based on data envelopment analysis is presented to evaluate the relative efficiency of the software factories and their projects, to measure the productivity in the software production component of the software factory through the activities that are carried out in their different work units. The proposed model consists of two phases in which the productivity of the software factory is evaluated and the productivity of the projects it conducts is assessed. Numerical tests on 6 software factories with 160 projects implemented show that the proposed model allows one to assess the software factories and the most efficient projects.
Cited by
1 articles.
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1. Research on machine learning to reduce cost and increase efficiency in factories;Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management;2023-12-08