Programme PhD Term IV Academic Year 2021-22

Course title Data Envelopment Analysis Area Economics Credits 1.00

Prof. Abhiman Das

Course Description & Objectives
In the present age of globalization, efficient utilization of resources is becoming more and more important for firms to survive and prosper in the face of intense competition from both domestic and foreign firms. The usual measure of efficiency often relies on a single indicator like output per worker or business per employee. While easily understood as a convenient measure of performance, it fails to account for the use of other inputs (like materials, energy, and capital) that contribute to the output and constitute the bulk of the production costs of a firm. It is imperative that a comprehensive measure of performance includes all the relevant factors that are important for production.

In evaluating the performance of a business, the owners or the managers would typically like to know:
  • Is the company making the best use of the resources?
  • Is it possible to produce more from the same input bundle? If so, which outputs and how much more?
  • Can the firm economize on the resources used? If so, which inputs and by how much?
  • Is the firm’s input-mix consistent with the relative prices of the inputs? If note, which input should be substituted for what?
  • Is the firm of the right size? If not, is it too big or too small?
  • Would a potential merger with another specific firm enhance efficiency?
The list goes on. This course should enable the participants to answer such questions in light of actual data from the firms and the industry concerned.

The objective of this course is to provide the students with conceptual foundations of productivity and efficiency from the perspective of production economic theory, Operations Research (OR) and also to show how one can use real life data to measure and compare performance of different decision making units.
This is an advanced doctoral level course in DEA. The emphasis of this course is both on understanding the production theory and OR applications using mathematical programming.