Measuring the relative efficiency of project management using data envelopment analysis

This paper presents a comprehensive review on recent applications of DEA techniques on measuring the relative efficiency of project management units. The study covers the implementation of DEA method for measuring the relative efficiency of enterprise resources planning, research and development and construction projects.
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Measuring the relative efficiency of project management using data envelopment analysis Journal of Project Management 1 (2016) 1–6 Contents lists available at GrowingScience Journal of Project Management homepage: www.GrowingScience.com Measuring the relative efficiency of project management using data envelopment analysis Sara Oroujia* a Growing Science, Toronto, Canada CHRONICLE ABSTRACT Article history: Measuring the relative efficiency of similar units has been an open research for years. Data Received: October 1, 2016 envelopment analysis (DEA) has been widely used among researchers for efficiency measure- Received in revised format: No- ment. DEA uses different inputs/outputs to measure the relative efficiency of one unit compared vember 16, 2016 with other similar units. This paper presents a comprehensive review on recent applications of Accepted: January 20, 2017 Available online: DEA techniques on measuring the relative efficiency of project management units. The study January 20, 2017 covers the implementation of DEA method for measuring the relative efficiency of enterprise Keywords: resources planning, research and development and construction projects. DEA Project management Efficiency 2017 Growing Science Ltd. 1. Introduction For years, there have been significant efforts to implement data envelopment analysis (DEA) for meas- uring the relative efficiency of similar units (Charnes et al., 1978; Banker et al., 1984). DEA has been commonly applied when input/output are not measurable by applying financial figures. For example, in educational systems, one may be interested in promoting schools with students who reach academic excellence. In electricity industry, there are many units in the world, which are reporting lost in their statements due to various reasons and any additional budgets must be based on non-financial numbers such as the number customers covered, the volume of electricity transmitted, etc. Fig. 1 shows a sample of different factors influencing on efficiency measurement. In a DEA method, there are some in- puts/outputs associated with all decision-making units. Let xij be the inputs for one of a particular unit with i=1,…,m and yrj be the outputs of other units with r=1,…,s and j=1,…,n and let u i and v j be the dual variables associated with xi and y j , respectively. The constant return to scale DEA model is formulated as follows, * Corresponding author. E-mail address: s_orougi@yahoo.com (S. Orouji) 2017 Growing Science Ltd. doi: 10.5267/j.jpm.2017.1.005 2 s max  u r yr . r 1 z m  vi xi. i 1 s (1)  ur yrj subject to r 1 m  1.  vi xij i 1 xij , yij  0 Model (1) is the original DEA, which can be executed j times to measure the relative efficiencies of different units. Since model (1) is nonlinear in structure, Charles et al. (1978) proposed a method to convert model (1) into a simple linear programming problem as follows, s z   u r yr . max r 1 s  ur yrj subject to r 1  1. m (2)  vi xij i 1 m  vi xi .  1 i 1 ur , vi  0, j  1,..., n Fig. 1. A scheme representing different input/output influencing efficiency of project management There are several applications of Malmquist productivity index (Aparicio et al., 2013; Kerstens & Van de Woestyne, 2014). Woo et al. (2015) studied the environmental efficiency of renewable energy from the static as well as the dynamic perspective in 31 OECD countries to get some insight about the effect S. Orouji / Journal of Project Management 1 (2016) 3 of renewable energy across different countries. In their study, DEA method was implemented to meas- ure environmental efficiency through multiple inputs/ outputs. Besides, to measure the dynamic envi- ronmental efficiency of renewable energy, the Malmquist productivity index was used to forecast the average efficiency change. Wijesiri and Meoli (2015) used a DEA based Malmquist technique to investigate the changes in produc- tivity of some Kenyan microfinance institutions (MFIs) from 2009 to 2012. A bootstrap procedure was also implemented to detect whether the changes in Malmquist index and its components were statisti- cally significant. They reported that MFIs had experienced about 7% annual productivity progress on average. Another bootstrapped regression analysis was also applied to study the impact of various en- vironmental variables on productivity change measures and the results stated that matured MFIs main- tained a lower productivity compared with their other counterparts. L ...