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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DEA is also regularly used to assess the efficiency of public and not-for-profit organizations, e. There is a mathematical approach to DEA that can be adopted however – this is illustrated later below. Usually we focus on the ranking of the records rather than on the actual values of the effectiveness scores. Different variables that could be used to establish the efficiency frontier are: On a technical issue note that the scale used for the x-axis and the y-axis in plotting positions for each branch is irrelevant.

One thing that anaalysis happen in DEA is that inspection of the weights that are obtained leads to further insight and thought. Since the technique was first proposed much theoretical and empirical work has been done. Views Read Edit View history. Another drawback of this technique is that we have to solve as many linear programming problems as the number of records, something that requires a lot of computational resources.

This constraint is a value envelopjent to better reflect the reality of the situation. We use input and outputs, weighted sums and ratios to rank our records. If you want to interpret it, you must check the weights that maximized its score and try to understand in which sense and under what condition this record receives the score.

Data envelopment analysis

The currently most often DEA-based aanalysis to obtain unique efficiency rankings is called cross-efficiency. It is important to be clear about the appropriate use of the relative efficiencies we have calculated. 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.


My name is Vasilis Vryniotis.

The description and assumptions of Data Envelopment Analysis As we discussed earlier, DEA is a method which was invented to measure productivity in business. You must understand that DEA is like a black box.

Data envelopment analysis – Wikipedia

It might seem reasonable to suggest therefore that the best possible performance that Reigate could be expected to achieve is given by the point labelled Best in the diagram above. In fact, after this analysis, the company never really applied DEA, after all they had effectively one significant input and one significant output.

Note that as you add a new output variable dimensionall the records with maximum value in this dimension will be found optimal. To do this we:. 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. Consider a number of bank branches. In the version of Excel I am using different versions of Excel have slightly different Solver formats you will get the Solver model as below:.

For example we could set a target for Reigate of continuing to process the same level of output but with one less member of staff.

This allows a best-practice relationship between multiple outputs and multiple inputs to be estimated. The above optimisation problem is a nonlinear problem and hence, at first sight, difficult to solve numerically. Consider the diagram below. Data Envelopment Analysis DEA has been recognized as a valuable analytical research instrument and a practical decision support tool.

October Learn how and when to remove this template message. Journal of the Operational Research Society. Here is the pseudocode of how DEA works: One of the core characteristics of the method is the separation of the record features into two categories: What changes as a result of this extra branch being included in the analysis?

The operators would then either initiate a control actioni. The Batch Normalization layer of Keras is broken. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations.


That is why, in this article, I will cover the basic ideas and mathematical framework behind DEA and in the next post I will show you some novel applications of the algorithm on web applications. Where u and v are the weights of each output and input of the record, s is the number of output features and m is the number of input features. Here a DMU is a distinct unit within an organisation that has flexibility with respect to some of the decisions it makes, but not necessarily complete freedom with respect to these decisions.

You can see here how we associate non-negative weights with each input and output measure. In fact it can be converted into a linear programming problem. It may, or may not, be possible to do that. Here we can see that Croydon has the highest ratio daat personal transactions per staff member, whereas Redhill has the highest ratio of business transactions per staff member.

Data envelopment analysis

The other branches do not compare well with Croydon, so are filety;e performing less well. Plainly we could easily find their efficiencies from the diagram. We should stress that the above linear programming problem will gives us the best weights for record i and calculate its efficiency under those optimal weights.

Here we can see that Croydon has the highest ratio of personal transactions per staff member, whereas Reigate has the lowest ratio of personal transactions per staff member. Rather the efficiencies here would usually be taken as indicative of the fact that other branches are adopting practices and procedures which, if Reigate were to adopt them, would enable it to improve its performance.

The technique was first proposed by Charnes, Cooper and Rhodes in and since then it became a valuable tool for estimating production frontiers.