One of the basic challenges many organizations face relates to benchmarking their performance within the organization as well as outside itself. Internal benchmarking implies comparison between the performance of different units / activities with similar units / activities within the organization having similar goals and deploying qualitatively similar resources. External benchmarking implies comparison with units / activities managed by other organizations. This comparison may become challenging when a multitude of goals need to be achieved using a number of resources.

Data Envelopment Analysis (DEA) was introduced by operations research practitioners in late 1970s as a means of facilitating such benchmarking. It was later developed (and deployed) by professionals from varied streams and industries. DEA has opened up avenues for analysis of cases which have been resistant to other analysis techniques due to the complex nature of the relationships between inputs and outputs.

What is DEA

Data Envelopment Analysis (DEA) is a data analysis technique for evaluating the performance of a set of peer entities called Decision Making Units (DMUs). DEA allows a lot of flexibility in the definition of DMUs. These units may be branches of a bank, outlets of a food chain, production units of a manufacturing organization, departments within the same firm, etc.

The core of DEA comprises identification of efficient mix of DMUs towards realizing the desired goals with the minimal deployment of resources. The resources deployed by the efficient mix are then compared with the actual resources deployed by a DMU for realization of specified goals. This comparison highlights whether the DMU under evaluation is efficient or not.

An Illustration

The deployment of DEA for efficiency analysis and benchmarking may be better understood with the following illustration.

Consider a set of 4 production facilities of Elecomp Pvt Ltd, an electronics circuit supplier. The facilities are located at Manesar, Chakan, Jaipur, and Dharuhera. The components produced by Elecomp are CE100, HL305, TL207, and SK113. All the components are manually assembled, using contract workforce, from basic materials like cables and circuit boards using hand-held devices. Elecomp management considers these materials, along with the manpower at each plant, as the key input elements. Exhibit 1 provides the total resource consumption for the different plants in March 2009.

  Chakan Dharuhera Jaipur Manesar
Cables (‘00 m) 348 123 129 154
Circuit Boards (‘000) 104 106 64 104
FTE personnel (no.) 276 285 162 210

Exhibit 1: Resource consumption at different plants

The number of components produced by the different units is provided in Exhibit 2

  Chakan Dharuhera Jaipur Manesar
CE100 175 253 148 160
HL305 23 41 27 84
TL207 37 48 35 33
SK113 46 43 27 56

Exhibit 2: Component-wise production by different manufacturing plants

Based on the above information, DEA may be deployed to analyze which of the units are operating relatively inefficiently and what should the optimal inputs (keeping output constant) or outputs (keeping inputs constant) be for the inefficient plants.

For example, a DEA analysis of the Chakan plant in this case shows that Chakan could have produced its current output using only 90% of the resources consumed currently. Alternatively, using the same amount of resources that it consumed, Chakan plant could have produced 2 more CE100 and 37 more HL305 components.


Within 30 years of its introduction by Charnes, Cooper and Rhodes, DEA has gained wide acceptability across the operations research and analytics fraternity. Both public and private institutions, across industries, have jumped to adopt DEA for internal and external benchmarking and thus identifying areas of inefficiency as well as potential for improvement. Such a quick and widespread acceptance of the approach is due to a unique set of advantages offered by DEA as listed below

  • 1. DEA is an empirical (actual data based) approach and hence grounded in actually achieved / achievable results
  • 2. It does not need priori assumptions regarding relationship between inputs and outputs. Thus there is no need to explicitly specify a mathematical form for analysis
  • 3. It can handle multiple inputs and outputs simultaneously and comprehensively
  • 4. It can be applied to time-series data as well as single time-horizon data
  • 5. The extent and sources of inefficiency may be identified for all units under analysis
  • 6. It has been proven to be useful in uncovering relationships that remain hidden from other methodologies

It must be noted, in this context, that DEA calculates relative efficiencies of the plants. Thus, it does not lead organizations to the absolute efficient frontier but only to the most efficient frontier already achieved by the different DMUs within the organization. This very fact, in a sense, also highlights the core benefit of using DEA i.e. the efficiencies outlined by DEA are more practically achievable than the efficiencies prescribed / estimated by most of the theoretical analysis models.

Areas of Application

DEA is being deployed across a wide range of industries and functional areas. Some of the industries where DEA is being applied currently are highlighted in Exhibit 3

Exhibit 3: Deployment of DEA across various Industries

It can be seen that DEA application cuts across industries. Researchers are continuously bringing in more industries under the ambit of DEA with innovative ways of leveraging its power for providing relevant results in different industries.

Similarly, the application of DEA cuts across functional areas. Some of the illustrative applications of DEA in different functional areas are outlined in Exhibit 4.

Exhibit 4: Applications of DEA across various functional areas

Benefits of leveraging DEA

Implementing DEA delivers a host of benefits to organizations

  • 1. Identify inefficient units / functions and improvement areas
  • 2. Improve resource efficiency of manufacturing and supply chain operations
  • 3. Control risks while enhancing financial returns across projects and investment options
  • 4. Identify right sourcing options for RM procurement
  • 5. Develop efficient Sales and Marketing strategies and plans
  • 6. Continuously improve performance of various functions through Window Analysis
How to Deploy DEA

To deploy DEA in-house, organizations may train managers, and equip them with the required software / hardware to conduct the analysis. Alternatively, organizations may approach data analytics firms that specialize in such techniques and have access to the desired tools.

At Beacon, we assist organizations leverage DEA for continuous improvement and elimination of inefficiencies. Our data analytics skills, coupled with high end analytics software and processors, ensure that the power of DEA may be unleashed to improve the performance of your organization. To know more about how DEA may help your organization, please contact us.