Performance Measurement of Heavy Equipment Retailing Organizations A Data Envelopment Analysis (DEA) Approach

Abstract

Abstract

Measuring and improving the performance has always been the center of attention of organizations. Organizations usually rely on different ratios to measure key performance. However, measuring performance by merely relying on ratios has its shortcomings. A more powerful tool in measuring relative performance is Data Envelopment Analysis (DEA). DEA is a mathematical programming technique for determining relative efficiencies of peer decision making units (DMU) and the technical efficiency of individual DMUs. It is a data-oriented approach for evaluating the performance of DMUs. DEA has been successfully used in both public sector and private sector. One of the industries that have been greatly overlooked is the heavy equipment industry and its retailing organizations. The main objective of the thesis is to develop models using DEA for measuring performance of heavy equipment retailing organizations. In this research performance measurement of heavy equipment retailing organization is evaluated by treating each branch (DMU) as whole unit and by analyzing the internal structure of each DMU. The organization under study is a Canadian heavy equipment retailing organization(HERO). The four DEA models used in the study measures efficiency from different perspectives. Such a measurement provides a comprehensive framework for measuring the performance of HERO. The study helps in benchmarking and locating best practices that are not visible through other commonly used management methodologies in the heavy equipment industry. The key findings of this research are: a) identification of branches that are efficient and inefficient b) Ranking of the branches based on super-efficiency scores that enable in benchmarking. d) The effect of environmental variables on the efficiency scores. f) Found that the efficiency of individual departments of the branch is less than the efficiency of the whole branch g) there is fluctuation in efficiency scores over a four-year period. The contributions are a) facilitates in benchmarking b) enables inefficient branches to improve its efficiency levels c) identification of variables that affects efficiency scores. d) PEDMAS as a new tool to measure the performance of heavy equipment branches. e) Identification of factors that will assist in improving efficiency. Keywords: Data Envelopment Analysis, HERO, Performance.