Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster

Homogeneity and best practice analyses in hospital performance management: an analytical framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals

A DEA-Based Approach to Evaluate the Efficiency of Non-Homogeneous Service Locations

This study aims at evaluating the performance of a company, ‘XYZ Company’, that has 115 service locations. Because of its ability of handling large numbers of inputs and outputs, and removing the need of predefining the factors’ weights, Data Envelopment Analysis (DEA) is used. DEA is benchmark tool that measures the efficiency of entities with respect to each other by assessing their performance of utilizing inputs to produce outputs. Researchers have developed several DEA models, all of which have different characteristics.A main assumption of DEA is that the entities are homogeneous – i.e. operating under similar conditions, which is not applicable sometimes. Thus, various approaches have been introduced to relax the homogeneity assumption. In this study, we propose an approach that estimates the efficiency over some stages, obtains efficiency scores from each stage, and then calculates the final weighted score by assigning a higher weight to the stage that represents the actual conditions of the entity more clearly.We apply three DEA models, utilizing the proposed approach to overcome the entities’ heterogeneity, to the data set of XYZ Company. Then, we compare the results of the three models, analyze the efficiency scores of the 115 service locations, and provide some major findings

An analysis of productive efficiency and innovation activity using DEA: An application to Spain's wood-based industry

This paper intends to analyze the relationship between productive efficiency and innovation activity in Spain’s wood-based industry. The methodology includes two levels of analysis. First, a non-parametric technique (data envelopment analysis, DEA)is applied with several inputs and outputs associated to economic and financial data. In a second stage, a logistic regression model explores the relationship between the property of efficiency and innovation activity indicators. This approach is used to analyze a set of firms in the following sectors: lumber and wood products, pulp and paper and wood furniture. Results do not show the existence of significant links between firm’s efficiency and innovation activities. This outcome is consistent with a low firm priority toward R&D as a means to achieve competitiveness and an innovation strategy followed by many Spanish firms based on the acquisition of embodied technology available in international markets. In order to improve competitiveness in the long run, efforts should be made by Spanish wood-based firms to increase their production of in-house technologies

Efficiency analysis of information technology and online social networks management : an integrated DEA-Model assessment

This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business ?social Media? activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model?s robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company?s ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN

Advancing efficiency analysis using data envelopment analysis: the case of German health care and higher education sectors

The main goal of this dissertation is to investigate the advancement of efficiency analysis through DEA. This is practically followed by the case of German health care and higher education organizations. Towards achieving the goal, this dissertation is driven by the following research questions: 1.How the quality of the different DEA models can be evaluated? 2.How can hospitals’ efficiency be reliably measured in light of the pitfalls of DEA applications? 3.In measuring teaching hospital efficiency, what should be considered? 4.At the crossroads of internationalization, how can we analyze university efficiency? Both the higher education and the health care industries are characterized by similar missions, organizational structures, and resource requirements. There has been increasing pressure on universities and health care delivery systems around the world to improve their performance during the past decade. That is, to bring costs under control while ensuring high-quality services and better public accessibility. Achieving superior performance in higher education and health care is a challenging and intractable issue. Although many statistical methods have been used, DEA is increasingly used by researchers to find best practices and evaluate inefficiencies in productivity. By comparing DMU behavior to actual behavior, DEA produces best practices frontier rather than central tendencies, that is, the best attainable results in practice. The dissertation primarily focuses on the advancement of DEA models primarily for use in hospitals and universities. In Section 1 of this dissertation, the significance of hospital and university efficiency measurement, as well as the fundamentals of DEA models, are thoroughly described. The main research questions that drive this dissertation are then outlined after a brief review of the considerations that must be taken into account when employing DEA. Section 2 consists of a summary of the four contributions. Each contribution is presented in its entirety in the appendices. According to these contributions, Section 3 answers and critically discusses the research questions posed. Using the Translog production function, a sophisticated data generation process is developed in the first contribution based on a Monte Carlo simulation. Thus, we can generate a wide range of diverse scenarios that behave under VRS. Using the artificially generated DMUs, different DEA models are used to calculate the DEA efficiency scores. The quality of efficiency estimates derived from DEA models is measured based on five performance indicators, which are then aggregated into two benchmark-value and benchmark-rank indicators. Several hypothesis tests are also conducted to analyze the distributions of the efficiency scores of each scenario. In this way, it is possible to make a general statement regarding the parameters that negatively or positively affect the quality of DEA estimations. In comparison with the most commonly used BCC model, AR and SBM DEA models perform much better under VRS. All DEA applications will be affected by this finding. In fact, the relevance of these results for university and health care DEA applications is evident in the answers to research questions 2 and 4, where the importance of using sophisticated models is stressed. To be able to handle violations of the assumptions in DEA, we need some complementary approaches when units operate in different environments. By combining complementary modeling techniques, Contribution 2 aims to develop and evaluate a framework for analyzing hospital performance. Machin learning techniques are developed to perform cluster analysis, heterogeneity, and best practice analyses. A large dataset consisting of more than 1,100 hospitals in Germany illustrates the applicability of the integrated framework. In addition to predicting the best performance, the framework can be used to determine whether differences in relative efficiency scores are due to heterogeneity in inputs and outputs. In this contribution, an approach to enhancing the reliability of DEA performance analyses of hospital markets is presented as part of the answer to research question 2. In real-world situations, integer-valued amounts and flexible measures pose two principal challenges. The traditional DEA models do not address either challenge. Contribution 3 proposes an extended SBM DEA model that accommodates such data irregularities and complexity. Further, an alternative DEA model is presented that calculates efficiency by directly addressing slacks. The proposed models are further applied to 28 universities hospitals in Germany. The majority of inefficiencies can be attributed to “third-party funding income” received by university hospitals from research-granting agencies. In light of the fact that most research-granting organizations prefer to support university hospitals with the greatest impact, it seems reasonable to conclude that targeting research missions may enhance the efficiency of German university hospitals. This finding contributes to answering research question 3. University missions are heavily influenced by internationalization, but the efficacy of this strategy and its relationship to overall university efficiency are largely unknown. Contribution 4 fills this gap by implementing a three-stage mathematical method to explore university internationalization and university business models. The approach is based on SBM DEA methods and regression/correlation analyses and is designed to determine the relative internationalization and relative efficiency of German universities and analyze the influence of environmental factors on them. The key question 4 posed can now be answered. It has been found that German universities are relatively efficient at both levels of analysis, but there is no direct correlation between them. In addition, the results show that certain locational factors do not significantly affect the university’s efficiency. For policymakers, it is important to point out that efficiency modeling methodology is highly contested and in its infancy. DEA efficiency results are affected by many technical judgments for which there is little guidance on best practices. In many cases, these judgments have more to do with political than technical aspects (such as output choices). This suggests a need for a discussion between analysts and policymakers. In a nutshell, there is no doubt that DEA models can contribute to any health care or university mission. Despite the limitations we have discussed previously to ensure that they are used appropriately, these methods still offer powerful insights into organizational performance. Even though these techniques are widely popular, they are seldom used in real clinical (rather than academic) settings. The only purpose of analytical tools such as DEA is to inform rather than determine regulatory judgments. They, therefore, have to be an essential part of any competent regulator’s analytical arsenal

Measuring banking efficiency in the pre- and post-liberalization environment : evidence from the Turkish banking system

The authors examine banking efficiency before and after liberalization, drawing on Turkey's experience. They also investigate the scale effect on efficiency by type of ownership. Their findings suggest that liberalization programs were followed by an observable decline in efficiency, not an improvement. During the study period Turkish banks did not operate at the optimum scale. Another unexpected result was that efficiency was no different between state-owned and privately owned banks. Banks that were privately owned or foreign owned had been expected to respond better to liberalization, because they were smaller and more dynamically structured, but they were no more efficient than state-owned banks. One reason for the systemwide decline in efficiency might have been the general increase in macroeconomic instability during the period studied.Financial Crisis Management&Restructuring,Banks&Banking Reform,Environmental Economics&Policies,Financial Intermediation,Economic Theory&Research

Effets des pratiques de récolte sur l'efficience grâce à une approche benchmarking/ Effects of spatial forest harvesting practices on efficiency through a benchmarking approach

L'exploitation forestière dans le premier maillon de la chaîne d'approvisionnement en bois et est l’activité responsable de l'extraction et du transport du bois de la forêt vers les autres industries. La planification se fait par le biais de la gestion forestière qui, au Québec, a une approche écosystémique. Les stratégies de gestion forestière qui sont appliquées s'inspirent donc des perturbations naturelles qui se produisent dans chaque type de forêt, y compris l'organisation spatiale des récoltes et la pratique de la récolte qui suit un gradient nord-sud. Les perturbations naturelles les plus fréquentes dans le sud sont caractérisées par de petites brèches créées par les chablis et les chutes d'arbres. Le principal agent de perturbation au nord est le feu qui couvre de grandes surfaces du territoire. L'objectif de cette étude est de comparer les stratégies d'aménagement forestier actuelles axées sur l'organisation spatiale de la récolte effectuée dans les différentes unités d'aménagement forestier (latitudes) de l'ouest du Québec, en se basant sur une méthode de mesure de l'efficacité appelée Data Envelopement Analysis (DEA), et en mettant l'accent sur l’efficacité. Les stratégies d'aménagement forestier ont été décrites en termes de variables spatiales telles que la superficie, la forme des sites de récolte et la dispersion des peuplements récoltés, de variables non spatiales telles que les pratiques de récolte (coupe partielle ou coupe à blanc) et le volume récolté par espèce, et d'autres variables associées au secteur forestier telles que les kilomètres de routes construites, entre autres. L’efficacité du régime de gestion sera évaluée en fonction du coût de l'approvisionnement en bois ($/m3). Plus précisément, 1) nous documentons et sélectionnons des variables non spatiales et d'autres variables associées (intrants) qui affectent l’efficience (par exemple, le coût d'approvisionnement en bois); 2) nous identifions les variables spatiales (par exemple, l'indice de forme, la taille des parcelles, la juxtaposition) sur la base des empreintes spatiales des pratiques forestières sur un an dans un gradient nord-sud; et 3) nous identifions les variables qui affectent l’efficience dans le cadre du gradient. Les résultats montrent 3 valeurs d'efficacité calculées pour 50 sites de récolte dans l'est du Canada. La valeur d'efficacité globale ou agrégée est de 72 Forest harvesting in the first link of the wood supply chain, which is the sector that is responsible for extraction and transportation of wood from the forest for processing by other industries. Planning of this activity in the Province of Quebec is conducted through forest management that uses an ecosystem-based approach. Forest management strategies that are applied are therefore inspired by natural disturbances that occur in each type of forest including the spatial organization of harvests and the harvesting practice that follows a north-to-south latitudinal gradient. Natural disturbance that is more frequent in the south is characterized by small-sized gaps created by windthrows and by tree falls. The principal agent of disturbance in the north are fires, which cover large areas of the territory. The objective of this study is to compare the forest management strategies in the different forest management units (latitudes) in western Quebec, focus on the spatial organization of the harvest activity through the efficiency, based on a benchmarking method of efficiency measure called Data Envelopment Analysis (DEA). The forest management strategies were described in terms of spatial variables such as the size area, shape of harvested sites and dispersion of harvested stands, and non-spatial variables of forest management such as harvest practices (partial cut or clear-cutting) and volume harvested per species, and other variables associated with the forest sector such as constructed kilometres of roads, among others. The efficiency of the management regime will be evaluated according to the wood procurement cost ($/m3). Specifically, 1) we document and select non-spatial variables and other associated variables (input) that affect efficiency (e.g., wood procurement cost and profit margin) 2) Identify spatial variables (e.g., shape index, patch size, juxtaposition) based on spatial footprints of forest practices over one year within a north-south gradient, and 3) Identify variables that affect the performance within the gradient. The results show 3 efficiency values calculated for 50 harvested sites in eastern Canada. The overall or aggregate efficiency is 72% with a high variation of ±23%, for the pure technical efficiency is 89% (± 9%) and for the scale efficiency it is 79% (±19%). It was proved that spatial variables are important to determine the efficiency of harvesting wood at a low cost, among the variables evaluated those related to the forest roads (distance to the mills and kilometres constructed roads) and the dispersion (proximity index) of the harvested sites showed to be the most important. From our results, we show both efficient and inefficient sites in the latitudinal gradient, harvesting sites do not present a single pattern depending on the latitude in which they are found as suggested by ecosystem forest management. When the efficiency was dividing according to the different latitudes, there is a tendency of higher efficiency values in the south but with more variation on the commercial value of the wood, the higher road density of the zone, and the taxes. Finally, the method allows the identification of reduction targets in each of the variables so that inefficient units could reach an efficient level. Globally, a reduction of an average of 2.8 km for road construction could increase efficiency by 37%, a reduction of 41 km to the mill by 29%, and a reduction of the dispersion (proximity index) by 21%

Service enterprise productivity in action: measuring service productivity

© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to measure service productivity using the Service Enterprise Productivity in Action (SEPIA) model. The research operationalises only one of the five stakeholder groups, the customer interface which incorporates service complexity (SC), customer interactions, customer channel, customer loyalty (CL) (new) as inputs, and CL (referred and repeat) and willingness to pay as output measures. Design/methodology/approach: The research extends our understanding of existing service productivity models with the development of the SEPIA model. Data were collected from 14 organisations operating in the Australian travel and tourism industry, which was analysed using a data envelopment analysis input oriented variable return to scale method as applied to the SEPIA model customer interface. Findings: Four key findings from the research include: customer choice and their ability to pay is a determinant of service productivity; service productivity is a two stage process when measured; SC is not categorical; and quality business systems do impact service productivity. Research limitations/implications: A limitation of this research is that only one (customer) of the five key stakeholders, customer, employee, manager, supplier and shareholder, was operationalised in this research paper. Practical implications: The operationalisation of the SEPIA customer interface using transactional data and measuring non-financial, intangible factors of productivity provide managers with insights on what services to offer, when to invest in or promote the use of technology and whether to spend marketing effort on customer acquisition or customer retention. Originality/value: The SEPIA model positions service firms within a social and service value network and provides a range of customer measures that extend the current capital (K), labour (L), energy (E), materials (M) and service (S), KLEMS measure of productivity and can be used to show the impact customers have on service productivity