Measuring the Efficiency of Construction Companies in Gaza Strip Using Data Envelopment Analysis DEA

Abstract

The science of performance measurement has rapidly developed towards the end of the last century with the emergence of Data Envelopment Analysis (DEA) technique. This powerful technique has contributed considerably to solve the problem of measuring the performance of organizations called Decision Making Units (DMUs) with multiple inputs and outputs. DEA model is a linear programming-based technique for measuring the relative efficiency of DMUs. The initial study focused on identifying the measures (variables) of efficiency and identifying vital measures (variables) of input and output. These input and output variables are determined through a questionnaire survey for construction companies of the first grade working in the fields of buildings, roads, water and sewage, and electromechanical works. The annual average of materials cost, labor cost, cash flow, subcontractors cost, equipment and machine value, and the number of management staff are used as input variables. The average annual amount of executed works is used as the single output variables. Determining input and output variables facilitated formulating the basic DEA model. In this research, the mathematical background and characteristics of DEA model is presented and a short case study on construction companies work in Gaza Strip through the period from 2002 to 2006 is given. Input oriented DEA model was used under the assumptions of constant returns to scale and variable returns to scale to evaluate the relative efficiency of 22 of the leading construction companies. The study revealed that there is no technique used to measure the efficiency of construction companies in Gaza. The overall technical efficiency, technical efficiency, and scale efficiency were investigated by Efficiency Measurement System program (EMS). The efficiency analysis not only provides an efficiency score for each DMU but also how much and in what areas an inefficient DMU needs to be improved in order to be efficient. Inefficient companies have to reduce their resources where they suffer of intensive possessed machines, cash flow, and management staff. Efficiency analysis found that construction companies have high efficiency scores. The average technical efficiency growth trend over time was 1.27%. It was found that the overall technical efficiency average scores was 94.4%, technical efficiency average score was 98.9%, and the scale efficiency was 95.2%.