Dear Subrahmanya Kumar N.

Greetings,

Please find the attached file chapter of efficiency as concepts and types of efficiency may be useful for you ,

Best Regards,

Dear Faris Alshubiri  Sir

Thanks a lot for sharing the chapter.

With regards

Kumar

Please have a look bellow:

Applying quality-driven, efficiency-adjusted DEA (QE-DEA) in the pursuit of high-efficiency-high-quality service units: An input-oriented approach Panagiotis Zervopoulos · Theodosios Palaskas

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ABSTRACT: The data envelopment analysis (DEA) is used extensively for estimating the comparative efficiency of operational units that employ similar production processes. Efficiency optimization, however, is not adequate by itself to ensure the successful operation of an enterprise because it is related only to the internal production process of a business unit. Therefore, the unit, to be able to achieve its goals, should take into consideration external factors, such as customer satisfaction, which reflect the quality of the services provided. In this context, the current paper strongly supports the view that synchronous assessment of both efficiency and quality is necessary. Using the quality-adjusted DEA model of Sherman & Zhu (2006a), we develop a quality-driven, efficiency-adjusted DEA model (QE-DEA) which seeks to provide a road map for a high-quality and high-efficiency operation of every service unit of the sample.

IMA Journal of Management Mathematics 09/2011; 22(4):401-417. DOI:10.1093/imaman/dpr014 · 0.50 Impact Factor

Basically efficiency is how good the input processes are transformed into the output ones. That's clear if you think to manufacturing. However in the bank sector that's quite complex. According to Dea efficiency is defined as the ratio between the weighted Sum of outputs and that of inputs, although to keep the model linear an output or input oriented transformation is required. This means that you can maximize outputs while keeping fixed inputs or minimize inputs while fixing the outputs. Hence inputs are those parameters you want to minimize (such as price) and outputs are those to be maximized (such as the service quality). I'm working on this issue under uncertainty for the public procurement office of a bank and results are good enought. Othewise other Mcda techniques can be also applied

Efficiency of a firm/sector is its ability to produce the greatest amount of output possible from a fixed amount of inputs. In another way , an efficient firm is the one that given a state of technical know-how, can produce a given quantity of goods/service by using least quantity of inputs possible. The concept of efficiency is derived from a particular interpretation of the notion of production frontier, which in its classical sense is the relationship between output, on the one hand, and the quantity of the inputs used in the production process to obtain that output, on the other.

You can check the following research on efficiency analysis..

http://www.indianjournals.com/ijor.aspx?target=ijor:ajrbem&volume=4&issue=2&article=020

Some information....

"The most popular approaches to calculate efficiency are: 1) the non-parametric techniques (Charnes et al. 1978), the Data Envelopment Analysis (DEA) based on the linear programming tools, and 2) parametric techniques (Aigner et al. 1977; Meeusen and van den Broeck 1997), the Stochastic Frontier Analysis (SFA) - Stochastic Frontier Production  (SFP) -  based on econometric tools.

Concepts:

DEA involves de concept of efficiency, and Farell (1957) had decomposed the efficiency into: 1) technical efficiency, and 2) allocative efficiency. The technical efficiency measures the maximum equiproportion reduction in all inputs that still allows continued production of given outputs. The allocative efficiency reflects the ability of a firm to use the inputs in optimal proportions, given their respective price. These two concepts, forms the concept of economics efficiency (Coelli 1995). The allocative inefficiency measures de magnitude of consequent loss. Similar considerations are applied to economics efficiency and inefficiency.

Therefore, the overall measure of technical efficiency can be disaggregated into three components: 1) pure technical efficiency due to producing within in isoquant frontier; 2) congestion due to over-utilization of inputs, and 3) scale efficiency, due to deviations from constant returns to scale (Weersink et al. 1990).

Advantadges or not:

The DEA model allows the comparison of a firm to a benchmark (best producer) and then the measure of efficiency is relative to the best producer in that group of firms, not necessarily the maximum real output per input used.

Sharma et al. (1999) points as main advantage of DEA the avoiding of the parametric specification of technology such as the distributional assumption for the inefficiency term.

The DEA approach has the advantage of considering many input and many output simultaneously. This has as a consequence the increasing of efficiency with the number of variables, more variables, higher efficiency (Reig- Martínez et al. 2004; Silva et al. 2004). DEA does not require a parametric specification of a functional form to construct the frontier (Silva et al. 2004) and was considered by Coelli and Perelman (1999) as the main advantage of DEA, it is very easy to perform because does not require a priori knowledge on the functional form of the frontier and benchmarking (best-practice reference) to real firms (Lauwers and van Huylenbroecks 2003). DEA does not impose any assumptions about functional form, hence it is less prone to miss-specification and does not take in account the random error, and consequently is not subject to the problems of assuming an underlying distribution about error term (Pascoe and Mardle 2000). DEA does not take account of statistical noise, and the efficiency estimates may be biased if the production process is largely characterized by stochastic elements (Pascoe and Mardle 2000).

The mathematical programming models are non-stochastic, and so we cannot have the values, of inefficiency and noise, separately. It is a non-parametric technique and not as effective as SFA, as a specification error (Reig- Martínez et al. 2004). The majors limitations of DEA is that it is difficult, conceptually, to separate the effects of uncontrollable environmental variables and measurements error, from the effect of differences in farm management and the presence of outliers (Silva et al. 2004)

The DEA model is deterministic and attributes all the deviations from the frontier to inefficiencies, a frontier estimated by DEA is likely to be sensitive to measurements errors or other noise in data (Sharma et al. 1999) and may attribute stochastic noise to the inefficiency scores and thus may be more sensitive to outliers (Lauwers and van Huylenbroecks 2003).

DEA has more flexibility in that they avoid as parametric specification of technology and assumptions about the distribution of efficiency, whilst allowing curvature conditions to be imposed easily (Lansink and Reinhard 2004).

The main DEA disadvantage to Coelli and Perelman (1999) is when the calculation of shadow-prices desired only by a range of prices, can be derived for the efficient firms.

A disadvantage of DEA approach is that there are no objective criteria (it varies if is a CRS or VRS) against assess of the model and the models only provides a reasonable representation of the actual frontier (or set of frontiers) (Pascoe and Mardle 2000).

The SFA models only have in this functional form one output and various inputs. SFA allows a correction for stochastic events, but assumes a parametric specification for the production technology, which can confuse efficiency results. SFA calculated by a translog specification, the curvature conditions (concavity inputs) are not globally satisfied, SFA makes an explicit assumption about the distribution of the inefficiency term (Lasink and Reinhard 2004).

Stochastic estimations incorporate a measure of random error but impose an explicit functional form and distribution assumption of data (Pascoe and Mardle 2000). SFP approach produces a set of statistics against which the models can be judged in terms of goodness of fit and alternative methods can be discriminated against.

One advantage of parametric methods is that they permit the testing of hypotheses such as those relating to the significance of included inputs and/or outputs, returns to scale and so on (Coelli and Perelman 1999).

The advantage of DEA over SFA is that the technological frontier is constructed without imposing a parametric functional form or (inefficiency) on a deviation from it (Reig- Martínez et al. 2004).

Sharma et al. (1999) found that DEA is more sensitive to outliers and other noise in the data, but DEA is more robust than those obtained from the parametric approach.

The most important point of frontier approach is that it deals with stochastic noise and allows statistical test of hypotheses pertaining to production structure and the degree of inefficiency. The imposed structural form and the explicit distributional assumption for the inefficiency term is the worst point of parametric techniques (Sharma et al. 1999)."

in: http://www.springer.com/gp/book/9789400757387

Efficiencyall how to produe a given output with maximum possible inputs or producing maximum possible output with a given leve of input resources.

Overall efficiency (CCR efficiency) takes into account the internal and external factors while calculating the efficiency. When the internal factors are considered, the efficiency is called pure technical efficiency (BCC efficiency). In contrast, when only the external factors are considered, the efficiency is called scale efficiency. As a result, the overall efficiency in DEA is expressed as the product of the two types of mentioned efficiencies.

Follow this book, you can free download it from libgen website

https://www.springer.com/gp/book/9788132215448

Please see the file attached.