Data Envelopment Analysis                 

Gain an understanding of the ideas and concepts of efficiency assessment of organisations and how Data Envelopment Analysis (DEA) models can be formulated and applied using Excel Solver.                 

Description

  • Build your knowledge of Data Envelopment Analysis (DEA). An established non-parametric methodology used to assess efficiency, performance, and productivity of organisations. 
  • Gain an understanding of the ideas and concepts of efficiency assessment for organisations and how DEA models can be formulated and applied using Excel Solver.
  • Increase your analytical skills by being able to identify inefficiencies and benchmark organisations against the best performers.

Learning objectives

  • Learn about the main ideas and concepts of efficiency assessment of organisations
  • Discuss the main DEA models and contexts in which these could be applied
  • Formulate a DEA model and interpret the solution using Excel Solver
  • Identify the advantages and limitations of the DEA methodology
  • Learn how the results of DEA models could inform better decision and policy making.

Topics

  • Basic concepts of efficiency analysis
  • Performance measures, inputs and outputs
  • The main DEA models and their specification
  • The use of Excel Solver to solve DEA models
  • Interpretation of results in a way useful for decision makers
  • Addressing typical problems arising in applications of DEA

Audience

The course is designed for delegates with an interest in the measurement and management of organisational performance. No knowledge of this field is required. A prior knowledge of linear programming methodology would be helpful but is not essential, as this will be briefly covered in the course.

Course format

  • PowerPoint presentation to introduce the topics including illustrative examples from different sectors
  • Case studies based on ‘real world’ problems
  • Practical sessions using Microsoft Excel with the Solver tool pack
  • Group discussion/work to explore the topics in more detail
  • Bring questions from your own work to embed your learning
  • Supporting resource pack available to use following the course

Related courses

  • Optimisation and (Meta-) Heuristics
  • Statistical Methods in OR: Multivariate Statistics
  • Follow on to Forecasting: ARIMA modelling for forecasting

Want to run this course in-house? Enquire about running this course in-house

Meet the tutor

Victor Podinovski

Victor is Professor of Operational Research at School of Business and Economics of Loughborough University. Victor’s background is in applied mathematics and operational research, and he has previously worked as Professor of Operational Research at Warwick Business School.

Victor’s academic interests are in the areas of optimisation, decision making and efficiency analysis of firms. He has published extensively on these topics in many international journals, including top European and US journals.

Recently Victor was a principal co-investigator of a large EU-funded research project aimed at the development of a range of quantitative tools to help EU decision makers to simulate policy scenarios in the field of common agricultural policy.

He is a co-editor of Journal of Productivity Analysis and co-director of the Centre for Performance and Productivity Analysis at Loughborough University.