Evaluating and data analysis of technical efficiency using DEA-Solver
Who should attend: Managers, researchers and scientists, postgraduate students (all subjects), analysts and those interested in assessing performance of organizational units, for instance different regional offices, bank branches, sales outlets, hospitals or schools.
What will you gain from the course: Theory of Data Envelopment Analysis, making decision based on technical efficiency scores based on multiple inputs and multiple outputs, sensitivity analysis, cost minimization, profit maximization, resource assignment, stock management, etc.
What the course will cover: The concept of relative efficiency and its measurement by Data Envelopment Analysis. Basic DEA models for measuring efficiency in multi-input multi-output situations. An illustrative assessment by DEA, carried out by course participants. Introducing to recent development in DEA including weights restrictions, assessment under variables returns to scale and target setting. Using DEA-solver installed on M.S. Excel
Technical Efficiency Measurement and Improvement Basics
• Introduction to linear algebra and linear programming
• Introductory Data Envelopment Analysis (DEA)
• DEA vs. Parametric Statistical Methods
• DEA Applications (Problems that DEA solves)
• Basic DEA models and their applications using DEA-solver.
• Types of Variables and Data entry for DEA-solver
• Running DEA-solver
• Results Interpretation
• Decision making based on results from basic DEA models using DEA-solver.
Advanced Technical Efficiency Measurement and Improvement using Data Envelopment Analysis
• Complex problems and advanced DEA models
• Data collection for DEA problems
• DEA models with restrictions on coefficients
• Non-discretionary models
• Super-efficiency models
• Categorical DEA method
• Hybrid DEA models and their applications using DEA-solver.
• SBM DEA models
• DEA models involving cost, revenue and profit
• DEA models involving risk
• Scale elasticity and congestion
• Fuzzy DEA
DEA (Data Envelopment Analysis) is a mathematical method based on linear programming, optimization and mathematical programming to evaluate technical efficiency measure using multiple-input/ multiple-output problems. It originally was developed by Charnes, Cooper, Rhodes(1978). DEA has the ability to evaluate and compare decision making units (units that the decision is going to be made for them) such as Universities, hospitals, hotels, manufacturing, site selection, tourism industry, marketing, prisons, agricultural production, banking, armed forces, sports, transportation , courts, bench-marking, index number construction and many other applications.