Project Management Efficiency Measurement with Data Envelopment Analysis: A Case in a Petrochemical Company

Author:

Coelho Marilia Botelho1,Lacerda Daniel Pacheco1ORCID,Piran Fábio Antonio Sartori1ORCID,Silva Débora Oliveira da1ORCID,Sellitto Miguel Afonso1ORCID

Affiliation:

1. Production and Systems Graduate Program, University of Vale do Rio dos Sinos, Avenida Unisinos 950, São Leopoldo 93022-180, Brazil

Abstract

The research question this study poses is how to measure the efficiency of project management activities. The purpose of this article is to quantify the efficiency of the execution of a project portfolio managed by a project management office (PMO) structure. The research subject is a PMO operating within a petrochemical manufacturing company in southern Brazil. The research method is quantitative modeling. The study employed data envelopment analysis (DEA) to calculate the relative efficiencies of projects in three classes according to complexity over a period of four years. Each project is a decision-making unit (DMU), as required by the DEA procedure. One novelty is the calculation of cost- and time-weighted efficiency values, which slightly differ from the average. The main results indicate that the average efficiency for classes of projects roughly stands between 40 and 80%. The results also indicate a learning process guided by the PMO, as the average efficiency increased over three years in two classes of projects, according to the prioritization imposed by the office. The study also pointed out that the most influential variables in determining project efficiency are accuracy in meeting deadlines and the time planned for completion. The most important implication is that, from now on, the company has a theoretical foundation to justify focusing further efforts on reducing and controlling time to completion, not only cost and scope conformity, to increase overall project efficiency. Future research should prioritize investigating management techniques that increase the likelihood of completing projects within their deadlines.

Funder

CAPES

FAPERGS

CNPq

Publisher

MDPI AG

Subject

Artificial Intelligence,Applied Mathematics,Industrial and Manufacturing Engineering,Human-Computer Interaction,Information Systems,Control and Systems Engineering

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