Module 1: Introduction to Data Envelopment Analysis (DEA)
Data Envelopment Analysis (DEA) is a powerful mathematical modelling technique used in the field of operations research and management science to assess the relative efficiency of decision-making units (DMUs). DEA is employed to evaluate the efficiency of multiple entities (DMUs) that transform inputs into outputs. The primary goal is to identify the most efficient units and offer insights into improving the performance of less efficient ones. DEA assesses efficiency by comparing the input-output performance of DMUs. It uses a non-parametric approach, which does not require specific functional forms for relationships between inputs and outputs. DEA focuses on finding a "production frontier" that represents the maximum output attainable from a given set of inputs. DEA considers both input and output variables. Inputs are the resources or factors used in the production process, while outputs are the products or services generated. The selection of these variables is crucial, and DEA allows for flexibility in defining them based on the specific context of the analysis. DEA assigns efficiency scores to each DMU, ranging from 0 to 1. A score of 1 indicates perfect efficiency, meaning the unit is operating on the production frontier, while lower scores denote relative inefficiency. Units with scores below 1 have room for improvement.
DEA includes different models, such as input-oriented and output-oriented models, radial and non-radial models, and constant returns to scale (CRS) and variable returns to scale (VRS) models. Each model addresses specific variations in the efficiency evaluation process. DEA has been widely applied in various sectors, including banking, healthcare, education, manufacturing, and public administration. It helps organisations identify best practices, allocate resources more effectively, and enhance overall performance. In summary, Data Envelopment Analysis is a valuable method for evaluating the relative efficiency of decision-making units, offering insights that can guide improvements in performance and resource allocation across diverse industries.
Learning Outcomes:
Module 2: Advanced Data Envelopment Analysis (DEA)
The Advanced Data Envelopment Analysis (DEA) Workshop – Series 2 is designed for participants who have already acquired fundamental knowledge of DEA and wish to deepen their expertise. This advanced session focuses on extending the traditional DEA models by incorporating sensitivity analysis and exploring techniques to identify the optimal combination of input variables that can improve the efficiency scores of Decision-Making Units (DMUs).
Building on the foundational concepts from Series 1, this workshop will provide a comprehensive understanding of how changes in input and output variables influence efficiency measurements. Participants will learn how to conduct sensitivity and stability analyses to assess the robustness of efficiency scores under varying data conditions. Additionally, the workshop will cover advanced DEA modelling approaches that allow for a more refined assessment of performance and identification of strategies to optimise resource allocation.
Key topics include advanced DEA models (including Super-Efficiency and Network DEA), scenario analysis, and practical applications in various sectors. Real-world case studies will be used to demonstrate how sensitivity analysis can guide decision-makers in determining which adjustments to inputs can lead to higher efficiency scores and improved operational performance.
Learning Outcomes
By the end of this workshop, participants will be able to:
Data Envelopment Analysis (DEA) is a powerful mathematical modelling technique used in the field of operations research and management science to assess the relative efficiency of decision-making units (DMUs). DEA is employed to evaluate the efficiency of multiple entities (DMUs) that transform inputs into outputs. The primary goal is to identify the most efficient units and offer insights into improving the performance of less efficient ones. DEA assesses efficiency by comparing the input-output performance of DMUs. It uses a non-parametric approach, which does not require specific functional forms for relationships between inputs and outputs. DEA focuses on finding a "production frontier" that represents the maximum output attainable from a given set of inputs. DEA considers both input and output variables. Inputs are the resources or factors used in the production process, while outputs are the products or services generated. The selection of these variables is crucial, and DEA allows for flexibility in defining them based on the specific context of the analysis. DEA assigns efficiency scores to each DMU, ranging from 0 to 1. A score of 1 indicates perfect efficiency, meaning the unit is operating on the production frontier, while lower scores denote relative inefficiency. Units with scores below 1 have room for improvement.
DEA includes different models, such as input-oriented and output-oriented models, radial and non-radial models, and constant returns to scale (CRS) and variable returns to scale (VRS) models. Each model addresses specific variations in the efficiency evaluation process. DEA has been widely applied in various sectors, including banking, healthcare, education, manufacturing, and public administration. It helps organisations identify best practices, allocate resources more effectively, and enhance overall performance. In summary, Data Envelopment Analysis is a valuable method for evaluating the relative efficiency of decision-making units, offering insights that can guide improvements in performance and resource allocation across diverse industries.
Learning Outcomes:
- To understand the concept of efficiency measurement and how DEA evaluates the performance of decision-making units in transforming inputs into outputs.
- To deliver the ability to apply DEA in real-world scenarios, which includes selecting appropriate input and output variables, collecting and organising data, and running DEA models to assess the efficiency of decision-making units.
- To adapt DEA to different contexts, such as finance, healthcare, education, and manufacturing, understanding the specific challenges and considerations in each sector.
Module 2: Advanced Data Envelopment Analysis (DEA)
The Advanced Data Envelopment Analysis (DEA) Workshop – Series 2 is designed for participants who have already acquired fundamental knowledge of DEA and wish to deepen their expertise. This advanced session focuses on extending the traditional DEA models by incorporating sensitivity analysis and exploring techniques to identify the optimal combination of input variables that can improve the efficiency scores of Decision-Making Units (DMUs).
Building on the foundational concepts from Series 1, this workshop will provide a comprehensive understanding of how changes in input and output variables influence efficiency measurements. Participants will learn how to conduct sensitivity and stability analyses to assess the robustness of efficiency scores under varying data conditions. Additionally, the workshop will cover advanced DEA modelling approaches that allow for a more refined assessment of performance and identification of strategies to optimise resource allocation.
Key topics include advanced DEA models (including Super-Efficiency and Network DEA), scenario analysis, and practical applications in various sectors. Real-world case studies will be used to demonstrate how sensitivity analysis can guide decision-makers in determining which adjustments to inputs can lead to higher efficiency scores and improved operational performance.
Learning Outcomes
By the end of this workshop, participants will be able to:
- Apply advanced DEA models (Super-Efficiency, Network DEA, and non-radial models) to evaluate DMUs beyond the basic efficiency frontier.
- Conduct sensitivity and stability analysis to assess how variations in inputs and outputs affect DEA results and the reliability of efficiency scores.
- Identify optimal input combinations to enhance the efficiency of less efficient DMUs and support evidence-based decision-making.
- Interpret and communicate advanced DEA findings for practical application in sectors such as finance, healthcare, education, manufacturing, and public administration.
- Develop actionable recommendations based on scenario and sensitivity analysis to drive performance improvement and resource optimisation.
About The Speaker
Nurazilah Zainal is an Associate Professor from the Faculty of Business and Management, Universiti Teknologi MARA Malaysia. She is currently enrolled as a Post Doctoral Fellow at the International Centre for Education in Islamic Finance (INCEIF). She holds a PhD in Business Economics from Universiti Putra Malaysia. Her main research interest is evaluating the score of efficiency among financial institutions, econometrics, financial economics, business economics and corporate finance.
|
FEE
RM69 / USD17 for both modules only! PAYMENT DETAILS: Bank Transfer Bank Name : Alliance Bank Bank Account Number : 55012-00100-199-83 Bank Account Name : MNNF Network Swift Code : MFBBMYKL Bank Address : 1G & 1-1, Seremban City Centre, Jalan Tuanku, Munawir, 70000 Seremban, Negeri Sembilan. Details : DEA Webinar or by Credit/Debit Card or PayPal |
|
