Data Envelopment Analysis. MSc in Regulation and DEA. What it is; Farrell measures of Efficiency. technical; allocative; scale. Running DEA; Dangers of DEA. 1, Data Envelopment Analysis for Students in a Hypothetical Class. 2. 3, Please note that cells with a red marker at the upper right-hand-side corner contain. In this paper, we demonstrate that Data Envelopment Analysis (DEA) can augment the Sorry, there is no online preview for this file type.

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Note the terminology here, we view branches as taking inputs and converting them with varying degrees of efficiency, as we shall see below into outputs.

For example it would be silly to compare a bank branch to a supermarket as they do radically different things. Many mangers without any technical expertise are happy with ratios.

In the DEA methodology, formally developed by Charnes, Cooper and Rhodes filetyppe, efficiency is defined as a ratio of weighted sum of outputs to a weighted sum of inputs, where the weights structure is calculated by means of mathematical programming and constant returns to scale CRS are assumed. How would you combine these figures into a single judgement?

Data envelopment analysis

Privacy Policy Terms of Use. Data envelopment analysis DEA is a linear programming methodology to measure the efficiency of multiple decision-making units DMUs when the production process presents a structure of multiple inputs and outputs. Since the technique was first proposed much theoretical and empirical work has been done. Have in mind that DEA filwtype linear combinations of the features to estimate the ratios.

Since then, there have been a large number of books and journal articles written on DEA or applying DEA on various sets of problems. Had we used a different scale above we would have had a different picture, but the efficiencies of each branch would be exactly the same. Basically the importance of each feature.

You must understand that DEA is like a black box. Journal of the Operational Research Society. Many studies have been published dealing with applying DEA in real-world situations.


By this we mean what conceptually are they, in words. For example, for the Dorking branch in one year, there were 44, transactions relating to personal accounts, 20, transactions relating to business accounts and 16 staff were employed. In microeconomic production theory a firm’s input and output combinations are depicted using a production function. The input components would include man-hours, losses, capital lines and transformers onlyand goods and services. The issue now becomes one of how to classify these performance measures into inputs and outputs, for use in DEA.

The currently most often DEA-based method to obtain unique efficiency rankings is called cross-efficiency. The output variables would include number analyxis customers, energy delivered, length of lines, and degree of coastal exposure. July Learn how and when to remove this template message. In envelopmment version of Excel I am using different versions of Excel have slightly different Solver formats you will get the Solver model as below:. The other branches do not compare well with Croydon, so are presumably performing less envelooment.

This is the point where the line from the origin through Reigate meets the efficient frontier. Mathematically the efficient filefype is the convex hull of the data. Once such an analysis has been carried out then we can begin to tackle, with a clearer degree of insight than we had before, issues such as:. xata

Data Envelopment Analysis Tutorial | Datumbox

For our bank branch example we have a single input measure, the number of staff, and a single output measure, the number of personal transactions. We use input and outputs, weighted sums and ratios to rank our records. More about DEA can be found here. In each case, those weights are selected to maximize the efficiency of the record and thus it can be considered as the upper limit of the efficiency of the record. As before, a commonly used method is ratiosjust as in the case considered before of a single output and a single input.

It is important to be clear about the appropriate use of the relative efficiencies we have calculated. What changes as a result of this extra branch being included in the analysis? This involves two key conceptual questions, the answers to which may not be at all obvious.


Hence if Reigate were to retain the same business mix i. My name is Vasilis Vryniotis. These branches, Croydon and Redhill, are the reference set for Reigate. For example consider Dorking and Reigate – Dorking is 2. Obviously there are many more unpublished studies, e. The logic here is to compare the current performance of Reigate the length of the line from the origin to Reigate to the best possible performance that Reigate could reasonably be expected to achieve the length of the line from the origin through Reigate to the efficient frontier.

A number of studies using this technique have been published for water utilities. In other words although they had embarked on a study aiming to use DEA the insights they gained were really from data collection and analysis, not directly from DEA.

Typically we have more than one input and one output. Consider now Dorking and Reigate in the figure above. A desire to Improve upon DEA, by reducing its disadvantages or strengthening its advantages has been a major cause for many discoveries in the recent literature. The purpose of these control rooms was to ensure both safety as the material moved through the pipelines had a potential for explosion and supply the material moved had to be available when consumers wanted it.

Envslopment of having to set the weights of the features and deciding on their importance before we run the analysis, the Data Envelopment Analysis calculates them from the data. Since the score is the maximum value of the Linear Programming Problem, it is not always simple to explain why the record received the score.

Examples of such units to which DEA has been applied are: