Congratulations to Doctorate in Business Administration candidate, Chandrashekhar Yegnaraman, who defended his dissertation on Tuesday, November 27, 2018.
Title of Thesis:
Performance Measurement of Heavy Equipment Retailing Organizations: A Data Envelopment Analysis (DEA) Approach
Measuring and improving the performance of organizations has always been the center of attention of business academics and practitioners alike. To evaluate the performance of different units, organizations usually rely on different key performance measures (such as productivity) that are used to compare the relative performance of certain business units. However, measuring performance by merely relying on the ratio of outputs to inputs 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 measuring the performance of hospitals, schools, universities and public-sector organizations. The tool has also been used in the private sector in measuring supply chain performance, performance of logistics operations and supplier selection. 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 also by analyzing the internal structure of each DMU. The organization under study is a heavy equipment retailing organization in Canada that has thirty-three branches spread from East to West coast of Canada.
Four DEA models have been used in the study and each of these models is to measure efficiency from a different perspective. Such a measurement provides a comprehensive framework for measuring the performance of heavy equipment retailing organizations. The study will help 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.