Adjusting for the environment in DEA: A comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.     

On the Role of Weight Restrictions in Data Envelopment Analysis

This paper examines the role that weight restrictions play in Data Envelopment Analysis (DEA). It is argued that the decision to include a factor (input or output) in a DEA model represents an implicit judgement that the factor has a non-trivial weight. It therefore seems perverse to allow DEA to assign a trivial weight to that factor in assessing the efficiency of a unit. There is therefore a strong case for imposing restrictions on factor weights. However, many existing methods of weight restriction are in practice unwieldy. This paper proposes an alternative approach we term contingent weight restriction which is both practical and intellectually consistent with the DEA philosophy. The paper explores the implications of alternative methods of weight restriction using simulated data from a well known production process.     

Evaluating Productivity Growth Measures in the EU

In this paper we evaluate the productivity growth of the member countries in the European Union (EU) before and after the enforcement of the Maastricht convergence criteria in 1992. Total factor productivity was measured by employing the growth-accounting approach and Tornqvist indices. The evaluation of productivity growth was performed by employing the non-parametric method of Data Envelopment Analysis (DEA) in particular years and within three time periods. DEA was also used to evaluate the relative efficiency of EU members with respect to the convergence criteria with important implications for their economic integration. TFP growth was found to have contributed the most in the EU, while its share in the latest recessions was the smallest as compared to labor and capital share.     

Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming

Global supplier selection is a multi-goal multi-criteria problem which needs to consider both qualitative and quantitative factors. Which suppliers are the best and how much should be purchased from the selected suppliers is an important purchasing issue for manufacturers. Traditionally, decision makers can determine the best supplier from evaluating few suppliers with qualitative supplier selection criteria by using fuzzy analytic hierarchy process (FAHP), but evaluate dozens of global suppliers simultaneously or determine the order quantity from them will be complex and difficult. Meanwhile, decision makers can determine the order quantity form the suitable suppliers by using fuzzy goal programming (FGP); however, it is not easy to decide weights for each goal of global supplier selection with different supply chain strategies. This study integrated the FAHP and FGP (FAHP-FGP) method to be a new approach for global supplier selection in considering the manufacturer’s supply chain strategies. With FAHP-FGP method, the manufacturer can consistently integrate multi-manager’ opinions in determining weights of each goal and obtain the order quantities for suitable suppliers based on manufacturer’s strategies. To demonstrate the usefulness of the proposed method, a real-world case of a digital consumer products manufacturer is presented.     

Performance measurement and best-practice benchmarking of mutual funds: combining stochastic dominance criteria with data envelopment analysis

We propose a method for mutual fund performance measurement and best-practice benchmarking, which endogenously identifies a dominating benchmark portfolio for each evaluated mutual fund. Dominating benchmarks provide information about efficiency improvement potential as well as portfolio strategies for achieving them. Portfolio diversification possibilities are accounts for by using Data Envelopment Analysis (DEA). Portfolio risk is accounted for in terms of the full return distribution by utilizing Stochastic Dominance (SD) criteria. The approach is illustrated by an application to US based environmentally responsible mutual funds.     

Stochastic non-smooth envelopment of data for multi-dimensional output

The proposed method of Stochastic Non-smooth Envelopment of Data (StoNED) for measuring efficiency has to date mainly found application in the analysis of production systems which have exactly one output. Therefore, the objective of this paper is to examine the applicability of StoNED when a ray production function models a production technology with multi-dimensional input and output. In addition to a general analysis of properties required by a ray production function for StoNED to be applicable, we conduct a Monte Carlo simulation in order to evaluate the quality of the frontier and efficiencies estimated by StoNED. The results are compared with those derived via Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). We show that StoNED provides competitive estimates in regard to other methods and especially in regard to the real functional form and efficiency.     

Rationality in supplier selection decisions: The effect of the buyer's national task environment

Our study sheds light on two fundamental questions in supply management: First, does adherence to highly rational decision processes help buyers make better supplier selection decisions? Second, is the influence of procedural rationality on decision effectiveness moderated by buyers’ (differently dynamic) task environments? We empirically test these questions using a sample of 150 supplier selection decisions taken in China and 150 decisions taken in Germany, countries that expose buyers to dynamic decision environments and stable decision environments, respectively. Our findings suggest that the influence of decision process rationality is stable across decision makers’ task environments. Both in China and in Germany, such process rationality is positively related with higher decision quality, and no significant differences in relationship strengths emerge between the two country samples.     

An analysis of operations efficiency in large-scale distribution systems

This research applies Data Envelopment Analysis (DEA) methodology to evaluate the efficiency of units within a large-scale network of petroleum distribution facilities in the USA. Multiple inputs and outputs are incorporated into a broad set of DEA models, yielding a comprehensive approach to evaluating supply chain efficiency. This study empirically separates three recognized, important and yet different causes of performance shortfalls which have been generally difficult for managers to identify. They are: (1) managerial effectiveness; (2) scale of operations and potential for a given market area (and efficiency of resource allocation given the scale); and (3) understanding the resource heterogeneity via programmatic differences in efficiency. Overall, the efficiency differences identified raised insightful questions regarding top management’s selection of the appropriate form and type of inputs and outputs, as well as questions regarding the DEA model form selected.     

Performance improvement decision aid systems (PIDAS) in retailing organizations using data envelopment analysis

This paper investigates the usefulness of Data Envelopment Analysis (DEA) models to aid decision making in multi-level retail organizations. It is argued that market efficiency is a key performance measurement dimension in retail organizations. The paper proposes three variants of market efficency that correspond to different tiers of management in a multi-level setting. The disentanglement of market efficiency will lead to the development of the Performance Improvement Decision Aid System (PIDAS) which seeks to classify units on clusters of different performance profile. The method is illustrated using data from a restaurant chain in the UK.     

Performance evaluation using data envelopment analysis in the presence of time lags

Data Envelopment Analysis (DEA) is a methodology that computes efficiency values for decision making units (DMU) in a given period by comparing the outputs with the inputs. In many applications, inputs and outputs of DMUs are monitored over time. There might be a time lag between the consumption of inputs and the production of outputs. We develop an approach that aims to capture the time lag between the outputs and the inputs in assigning the efficiency values to DMUs. We propose using weight restrictions in conjunction with the model. Our computational results on randomly generated problems demonstrate that the developed approach works well under a large variety of experimental conditions. We also apply our approach on a real data set to evaluate research institutions.     

DEA方法在学习成绩相对有效性评价中的应用

针对目前教育体制中的一些问题,运用数据包络分析(DEA)方法,对学生学习成绩的相对有效性进行评价,为学校考核学生成绩提出更公平有效的方法,从而提升学校对学生的管理水平。     

On the Interpretation of Value Efficiency

Halme et al., Management Science, 45, 103–115 (1999) have proposed Value Efficiency Analysis as an approach to incorporate preference information in Data Envelopment Analysis (DEA). Generally, a value function includes only ordinal information and thus a value efficiency score does not provide information on the value difference. The score only describes the improvements in the input/output values that are needed in order to make the Decision-Making Unit (DMU) as preferred as the Most Preferred Solution (MPS). This Paper discusses two sets of additional assumptions that enable us to give the efficiency score a value difference interpretation.     

A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016

In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided.     

DEA and Dynamic Yardstick Competition in Scandinavian Electricity Distribution

Multi-period multi-product regulatory schemes for electricity distributors are presented, based on cost information from a productivity analysis model and an agency theoretical decision model. The proposed schemes are operational and demonstrate considerable advantages compared to the popular CPI-X revenue cap regulation. The schemes avoid arbitrariness, excessively high or negative informational rents as well as ratchet effects and they promote rapid productivity catch-up by making full use of available data. More generally, the paper contributes to the theoretical unification between firm-based Data Envelopment Analysis (DEA) productivity models and micro-economic reimbursement theories.JEL Classification: D24, D82, L12, L51, L94     

Estimating the Potential Gains from Mergers

We introduce simple production economic models to estimate the potential gains from mergers. We decompose the gains into technical efficiency, size (scale) and harmony (mix) gains, and we discuss alternative ways to capture these gains. We propose to approximate the production processes using the non-parametric Data Envelopment Analysis (DEA) approach, and we use the resulting operational approach to estimate the potential gains from merging agricultural extension offices in Denmark.JEL Classification: D20, L11, L22, P41, G34     

Technical efficiency and the competitive behavior of hospitals

This paper examines whether the competitive behavior of hospitals influences the extent to which their productive efficiency deviates from best-practice standards. An index of technical inefficiency is constructed by means of Data Envelopment Analysis (DEA) for 189 acute-care hospitals in the State of Florida in 1989. A regression model is then specified that estimates the impact of competitive dynamics in local hospital markets over the period 1982–1988 on these 1989 DEA efficiency scores, controlling for a set uf internal and external constraints on managerial decision making. Among other things, the empirical analysis shows that these ratings are systematically accounted for by both the nature and vigor of hospital competition, with price leaders in highly competitive markets in particular shown to be more efficient. The public policy implications of these findings are discussed.     

Sustainable Supplier Selection in Megaprojects: Grey Ordinal Priority Approach

Due to mounting environmental and social challenges, supplier selection has become one of the most critical tasks of project-oriented organizations. Because supplier selection can affect the long-term success and profitability of the organizations and their projects, directly, embracing sustainability can add value in the equation. Considering sustainability measures can positively guide project managers in making better decisions for the projects in the long term. Therefore, the current study attempts to provide a conceptual model for selecting the best supplier based on a sustainability framework in megaprojects. Meanwhile, decision-making methods can be employed as a proper tool to find the best supplier. Ordinal priority approach (OPA) is a recent development in multiple criteria decision making (MCDM), while it has many benefits compared with other methods like analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). However, this method cannot consider multiple ranks during the decision-making process, and using an uncertainty approach feels strongly. Grey systems theory (GST) can consider uncertainties with no need for large sample or proposing membership function. Hence, the current study employed the GST to consider multiple ranks for criteria and alternatives in the OPA method. This is the first time that a sustainable supplier selection framework has been presented for megaprojects with the aid of the Grey OPA (OPA-G) method. Finally, a case study has been examined to evaluate the performance of the proposed approach. The results show that the proposed approach can be used in real-world situations and it has acceptable performance under uncertainty conditions.     

Using data envelopment analysis to measure the efficiency of not-for-profit organizations: A critical evaluation—comment

This note responds to Nunamaker (1985) who supposedly deals with deficiencies in Data Envelopment Analysis (DEA) as an approach for (1) measuring efficiencies of not-for-profit entities identified as Decision Making Units (DMUs) and (2) locating sources and amounts of inefficiencies in each of the inputs used and in each of the outputs produced by each DMU. Corrections and comments are offered with references supplied for interested readers who wish to examine more detailed treatments of the topics covered.     

Using data envelopment analysis to measure the efficiency of non-profit organizations: A critical evaluation

This study examines the potential effects of variable set expansion and data variations upon the efficiency scores generated using the Data Envelopment Analysis (DEA) model. It was found that variable set expansion (either through disaggregation of existing variables or addition of new factors) should produce an upward trend in efficiency scores. In addition, ample opportunity exists for ‘decision-making units’ to increase their efficiency scores through manipulation of reported data. In real-world applications of DEA, these problems must be resolved as much as possible (e.g. increased audit of data) in order to improve DEA's practical usefulness and reliability.     

山东省企业技术创新能力测度的实证研究

通过对山东省几个有代表性的地区的技术创新能力的测定找出山东省技术创新能力与其他各地存在的差异;经过分析,论文将采用DEA(数据包络分析)来测度山东省企业技术创新能力,从而发现山东省技术创新能力的不足之处。根据DEA分析结果,山东省得分仅高于西部地区,低于东部总体水平、中部总体水平、辽宁、浙江、广东、江苏各地区。鉴于上述结果分析,发现山东省的技术创新能力仍处于一个偏重投入而轻产出的水平,需要采用相应的措施来解决。