Data envelopment analysis in service quality evaluation: an empirical study

Service quality is often conceptualized as the comparison between service expectations and the actual performance perceptions. It enhances customer satisfaction, decreases customer defection, and promotes customer loyalty. Substantial literature has examined the concept of service quality, its dimensions, and measurement methods. We introduce the perceived service quality index (PSQI) as a single measure for evaluating the multiple-item service quality construct based on the SERVQUAL model. A slack-based measure (SBM) of efficiency with constant inputs is used to calculate the PSQI. In addition, a non-linear programming model based on the SBM is proposed to delineate an improvement guideline and improve service quality. An empirical study is conducted to assess the applicability of the method proposed in this study. A large number of studies have used DEA as a benchmarking tool to measure service quality. These models do not propose a coherent performance evaluation construct and consequently fail to deliver improvement guidelines for improving service quality. The DEA models proposed in this study are designed to evaluate and improve service quality within a comprehensive framework and without any dependency on external data

Data envelopment analysis with fuzzy complex numbers with an empirical case on power plants of iran

Using Data Envelopment Analysis (DEA) in complex environment is an idea that has recently presented for measuring the relative efficiencies of a set of Decision Making Units (DMUs) with complex inputs and outputs. The values of the input and output data in real-world problems appear sometimes as fuzzy complex number. For dealing with these types of data in DEA, we need to design a new model. This paper proposes a DEA model with triangular fuzzy complex numbers and solve it by using the concept of the data size and the α-level approach. This method transforms DEA model with fuzzy complex data to a linear programing problem with crisp data. In the following, a ranking model is also developed using the above approach to rank the efficient DMUs. The proposed method is presented for the first time by the authors and there is no similar method. Finally, we present a case study in the generators of the steam power plants to demonstrate the applicability of the proposed methods in the power industry

Measuring performance with common weights: network DEA

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In conventional data envelopment analysis (DEA), a production system has been seen as a black box for measuring the efficiency without any attention to what is happening inside the system. However, in practice, performance improvement often requires observing the internal structure of the producing system in order to find the sources of inefficiencies. In addition, weight flexibility as a key property of the multiplier DEA models allows a system to totally disregard an assessment factor, either input or output, from the evaluation process by assigning a value of zero or epsilon to its weight. This paper contributes to the existing literature by proposing a common-weights DEA model when the production system includes a number of interrelated processes. To this end, we propose an aggregate DEA model to calculate the most favourable common weights for determining the efficiency of all production systems and their processes at the same time. Our proposed aggregate model not only is linear for equitably evaluating the producing units on the same scale, but also enables us to deal with the mixed network structures. Furthermore, the network system is decomposed into a series system to build a relational network DEA model that emphasises separate relatedness. This greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in network DEA models including the zero value and fluctuating weights and multiple solutions. Managerially speaking, this paper aims to provide the top management team of a production system with an integrated framework to shape a better strategic decision process about firm performance, which is to treat the sources of inefficiencies and ultimately take corrective actions over the long run. Put differently, the proposed framework helps top managers make proper decisions in complex situations with a view of improving a firm’s efficiency in all production divisions, which can be identified as a core competency leading to competitive advantages of the organisation. In the context of performance management, our study is equipped with a simple numerical example and a case study of the non-life insurance companies to demonstrate the applicability of the proposed common-weights network model

Ef?ciency analysis in two-stage structures using fuzzy data envelopment analysis

Abstract Two-stagedataenvelopmentanalysis(TsDEA)modelsevaluatetheperformance of a set of production systems in which each system includes two operational stages. Taking into account the internal structures is commonly found in many situations such as seller-buyer supply chain, health care provision and environmental management.ContrarytoconventionalDEAmodelsasablack-boxstructure,TsDEA providesfurtherinsightintosourcesofinef?cienciesandamoreinformativebasisfor performance evaluation. Inaddition, ignoring the qualitative and imprecise data leads to distorted evaluations, both for the subunits and the system ef?ciency. We present the fuzzy input and output-oriented TsDEA models to calculate the global and pure technicalef?cienciesofasystemandsub-processeswhensomedataarefuzzy.Tothis end,weproposeapossibilisticprogrammingproblemandthenconvertitintoadeterministicintervalprogrammingproblemusingthe?-levelbasedmethod.Theproposed methodpreservesthelinkbetweentwostagesinthesensethatthetotalef?ciencyofthe system is equal to the product of the ef?ciencies derived from two stages. In addition to the study of technical ef?ciency, this research includes two further contribution

Centralized resource allocation to create New Most Productive Scale Size (MPSS) DMUs

Data envelopment analysis (DEA) is a mathematical programming - based technique to evaluate the performance of a homogeneous group of decision-making units (DMUs) with multiple inputs and outputs. One of the DEA applications involves aggregating input resources and reallocating them to create efficient DMUs.The present study employs the centralized resource allocation (CRA) approach to develop a model for creating new DMUs. These new DMUs are the most productive scale size (MPSS), and all new DMUs lie on a strong supporting hyperplane. In this case, a dual model is used to obtain the strong supporting hyperplane which all new DMUs lie on. This hyperplane is derived by solving the dual model and generating a common set of weights. Then, it is shown that all new DMUs lie on a strong supporting hyperplane, and an MPSS facet is the intersection of this hyperplane with the production possibility set (PPS)

Group decision making using Fuzzy TOPSIS

Today, in the real world, many quantitative and qualitative factors like the quality, price, flexibility and efficiency of the delivery process should be considered in decision making problems. Therefore, linguistic variables can be expressed in trapezoidal or triangular fuzzy numbers are used to assess the rating of each alternative and the weight of each criterion. Hence, the aim of this paper is to extend the TOPSIS method to decision-making problems with fuzzy data to find appropriate alternative under vague and ambiguous environment with team of decision making. According to the concept of the TOPSIS method in group multiple criteria decision making (GMCDM) problem, an index of closeness coefficient is defined to determine the ranking order of all alternatives by calculating the distance to the both fuzzy ideal solution and fuzzy anti-ideal solution based on approach of ranking of the fuzzy numbers simultaneously. Finally, an example is given to highlight the procedure of the proposed method

A novel Data Envelopment Analysis model with complex numbers : measuring the efficiency of electric generators in steam power plants

The output of a generator in power plant is the electricity, and it consists of two parts, active and reactive power. These quantities are expressed as complex numbers in which the real part is the active power and the imaginary part is the reactive power. Reactive power plays an important role in an electricity network. Ignoring it will exclude a lot of information. With regard to the importance of the generators in power plants, surely, calculating the efficiency of these units is of great importance. Data Envelopment Analysis (DEA) is a nonparametric approach to measure the relative efficiency of Decision-Making Units (DMUs). Since the generators data are complex numbers, thus, if we the use classical DEA models in order to measure the efficiency of the generators in power plants, the reactive power cannot be considered, and the measurement is limited to the real number of electric power. In this paper, a new DEA model with complex numbers is developed in order to assess the performance of the power plant generators

Industrial parts change recognition model using machine vision, image processing in the framework of industrial information integration

This paper presents the industrial parts change recognition model using machine vision image processing in the framework of industrial information integration and it is applied research’s category. Therefore, this study implements a new industrial information integration engineering (IIIE)system by combining components that have previously been expressed separately by previous research to develop inspection of industrial parts and improve its quality and accuracy of human visual inspection status and this is the innovative aspect of this research. We used machine vision to improve human vision in change recognition in objects such as cracks, fractures… and that has been the research issue. So, this study aims to aggregate different tools identified by other researchers in change recognition in different parts because human vision is weak, and current mechanical tools such as sensors have several problems such as calibration and accurate maintenance and repair and errors. The proposed model can be used in two fields, first, recognition of the difference between family-made industrial parts with the standard sample in the production lines online, second, monitoring and controlling changes in the working industrial parts such as moving rails, train wheels, brake discs, clutch plates, various motor parts, etc. In the first case, every manufacturer needs to produce parts, such as the standard prototype for the production line. Therefore, if the machinery of the production line is out of calibration for any reason, products will be made out of standard, which would mean a reduction in the quality or an increase in production costs. Therefore, online monitoring of production lines with the help of machine vision is important in reducing production costs. In the second case, recognition of the timely change in the working industrial parts can also prevent accidents. In other words, timely detection of failure can be effective in preventing accidents in addition to reducing costs. For example, the timely detection of a train wheel or train rail wear and its timely repair or replacement will prevent the occurrence of a rail accident, which is one of the applications of the proposed model. Since the results of this study are compared with those of industrial parts with standard samples, the results are sufficiently valid. The study was conducted by the authors and no organization was involved

How do customers evaluate hotel service quality? An empirical study in Tehran hotels

The purpose of this study is to investigate the dimensions of hotel service quality, to assess relative importance of them and to evaluate service quality of Tehran hotels in terms of guests’ perspectives. The paper examines the reliability and validity of the designed scale based on SERVQUAL model. A cross-sectional research based on SERVQUAL model conducted on nine hotels in Tehran (n=1080). Several statistical analyses such as EFA, CFA, Linear regression and t-test were applied to analyze the data. Five service quality dimensions were identified and named as “tangibles”, “problem solving”, “service supply”, “empathy” and “security”. Even though our findings confirmed five dimensional SERVQUAL constructs, some dimensions have been identified differing from SERVQUAL scale dimensions. Finding showed that the best overall service quality predictor is “tangibles” followed by “service supply”, “problem solving”, “assurance” and “empathy”