A supplier performance evaluation framework using single and bi-objective DEA efficiency modelling approach: individual and cross-efficiency perspective

In view of complexities associated with supplier performance evaluation based on traditional business criterions (such as costs, quality levels, and delivery timelines) and emerging criterions (such as those related to environmental sustainability), we in this research evolve two different supplier efficiency measurement models that unify such criterions possessing characteristics of both desirable and undesirable outputs. The first model is a single-objective DEA efficiency assessment model wherein both types of outputs are integrated into a single composite efficiency measure. Using data from suppliers of Hyundai Steel Company, we determine composite efficiencies of each of these suppliers thus ranking them in terms of an overall efficiency score that would be useful as far as the first-cut supplier discrimination is concerned. However, due to the relative inability of evolved single-objective efficiency model to perform trade-offs amongst desirable and undesirable outputs and, owing to unidimensionality aspects, we evolve a goal programming based bi-objective efficiency model wherein trade-offs can be performed between both conventional and emerging dimensions criterions leading to different supplier evaluations for varied scenarios. We also integrate our evolved models with the cross-efficiency view of efficiency determination in order to enable the decision-makers to achieve peer-to-peer evaluation and maximum discrimination amongst suppliers.     

Evaluating the efficiency of local municipalities in providing traffic safety using the Data Envelopment Analysis

The purpose of this study was to estimate the relative efficiency of 197 local municipalities in traffic safety in Israel during 2004–2009, using Data Envelopment Analysis (DEA). DEA efficiency is based on multiple inputs and multiple outputs, when their weights are unknown. We used here inputs reflecting the resources allocated to the local municipalities (such as funding), outputs include measures that reflect reductions in accidents (such as accidents per population), and intermediate variables known as safety performance indicators (SPI): measures that are theoretically linked to crash and injury reductions (such as use of safety belts). Some of the outputs are undesirable. Using DEA, the local municipalities were rank-scaled from the most efficient to the least efficient and required improvements for inefficient municipalities were calculated. We found that most of the improvements were required in two intermediate variables related to citations for traffic violations. Several DEA versions were used including a two-stage model where in the first stage the intermediate variables are the outputs, and in the second stage they are the inputs. Further analyses utilizing multiple regressions were performed to verify the effect of various demographic parameters on the efficiency of the municipalities. The demographic parameters tested for each local municipality were related to the size, age, and socio-economic level of the population. The most significant environmental variable affecting the efficiency of local municipalities in preventing road accidents is the population size of the local authority; the size has a negative effect on the efficiency. As far as we could determine, this is the first time that the DEA is used to measure the efficiency of local municipalities in improving traffic safety.     

How to assess sustainability of countries via inverse data envelopment analysis?

In this paper, two input-oriented and output-oriented inverse semi-oriented radial measures are presented. Such models are applied to determine resource allocation and investment strategies for assessing sustainability of countries. Our proposed models can deal with both positive and negative data. In our proposed inverse input-oriented data envelopment analysis (DEA) model, optimal inputs are suggested while outputs and efficiency score of decision-making unit (DMU) under evaluation are unchanged. Similarly, in our proposed inverse output-oriented DEA model, optimal outputs are proposed while inputs and efficiency score of DMU under evaluation are kept unchanged. For the first time, we propose two new inverse DEA models to handle resource allocation and investment analysis problems given sustainable development aspects in the presence of negative data. A case study is given for assessing sustainability of countries.     

面向零售商的供应链联盟合作伙伴选择研究

处于供应链末端的零售商在现代供应链中扮演着服务中心、信息搜集中心和预警中心的角色,满足了零售商的需求就意味着在很大程度上满足了市场需求.本文以零售商满意为切入点,以制造型企业作为供应链的核心企业来考虑供应链联盟合作伙伴的选择,在分析了供应商和分销商的不同特性之后为供应链上下游备选合作伙伴建立了不同的评价指标.文中采用传统的数据包络方法评价供应商的相对有效性,并以此为依据粗选上层供应商.对下层分销商采用作业成本法结合网络数据包络的方法展开相对有效性分析粗选分销商.在完成上下游合作伙伴的粗选之后,以零售商满意度最大化为目标建立了供应链联盟伙伴选择模型,据此选择出最理想的供应链联盟伙伴的组合.     

An innovation funnel process for set-based conceptual design via DOE exploration,DEA selection and computer simulation

This paper presents a conceptual design approach including pattern creation from designers, alternative exploration with a DOE matrix, alternative analysis via computer simulation and alternative selection by DEA analysis. Designers possessing domain knowledge create various design patterns to meet the requirements of product performance and customer expectations. Then, based on these design patterns, the alternatives, considered as decision-making units (DMUs), are extracted from various quality level combinations by following the use of the DOE matrix. The nature of the DOE matrix ensures that distinctive representatives are constructed for all design alternatives. The total alternatives (DMUs) consist of the alternatives associated with all the patterns. Computer simulation with ANSYS software is introduced to convert the quality level combination of each alternative (DMU) into simulated outputs, which are further categorised into DEA inputs and DEA outputs for DEA frontier analysis. Four DEA methods, CCR-min input, CCR-max output, BCC-min input and BCC-max output, are used for analysing typical market representatives resulting from market uncertainty. The found efficiencies are used to rank and select the explored alternatives (DMUs) for the next stage of the detailed design. A bike-frame product is chosen as an example to demonstrate the proposed approach. The results clearly show that the proposed approach enables designers to economically select appropriate design alternatives that satisfy performance expectations during the conceptual design stage.     

Consistent and robust ranking in imprecise data envelopment analysis under perturbations of random subsets of data

Data envelopment analysis (DEA) is a non-parametric method for measuring the relative efficiency of a set of decision making units using multiple precise inputs to produce multiple precise outputs. Several extensions to DEA have been made for the case of imprecise data, as well as to improve the robustness of the assessment for these cases. Prevailing robust DEA (RDEA) models are based on mirrored interval DEA models, including two distinct production possibility sets (PPS). However, this approach renders the distance measures incommensurate and violates the standard assumptions for the interpretation of distance measures as efficiency scores. We propose a modified RDEA (MRDEA) model with a unified PPS to overcome the present problem in RDEA. Based on a flexible formulation for the number of variables perturbed, MRDEA calculates the empirical distribution for the interval efficiency for the case of a random number of variables affected. The MRDEA approach also decreases the computational complexity of the RDEA model, as well as significantly increases the discriminatory power of the model without additional information requirements. The properties of the method are demonstrated for four different numerical instances.     

Effectiveness of maritime safety control in different navigation zones using a spatial sequential DEA model: Yangtze River case

This paper aims to analyze the effectiveness of maritime safety control from the perspective of safety level along the Yangtze River with special considerations for navigational environments. The influencing variables of maritime safety are reviewed, including ship condition, maritime regulatory system, human reliability and navigational environment. Because the former three variables are generally assumed to be of the same level of safety, this paper focuses on studying the impact of navigational environments on the level of safety in different waterways. An improved data envelopment analysis (DEA) model is proposed by treating the navigational environment factors as inputs and ship accident data as outputs. Moreover, because the traditional DEA model cannot provide an overall ranking of different decision making units (DMUs), the spatial sequential frontiers and grey relational analysis are incorporated into the DEA model to facilitate a refined assessment. Based on the empirical study results, the proposed model is able to solve the problem of information missing in the prior models and evaluate the level of safety with a better accuracy. The results of the proposed DEA model are further compared with an evidential reasoning (ER) method, which has been widely used for level of safety evaluations. A sensitivity analysis is also conducted to better understand the relationship between the variation of navigational environments and level of safety. The sensitivity analysis shows that the level of safety varies in terms of traffic flow. It indicates that appropriate traffic control measures should be adopted for different waterways to improve their safety. This paper presents a practical method of conducting maritime level of safety assessments under dynamic navigational environment.     

Supplier evaluation and selection: an augmented DEA approach

Evaluating and selecting suppliers is an essential part of effectively managing today's dynamic and global supply chains. In this paper, we propose a supplier evaluation and selection methodology based on an extension of data envelopment analysis (DEA) that can evaluate suppliers in an efficient manner. Through the incorporations of a range of virtual standards, the proposed methodology termed augmented DEA, has enhanced discriminatory power over basic DEA models to rank suppliers. In addition, weight constraints are introduced to reduce the possibility of having inappropriate input and output factor weights. We demonstrate the application of augmented DEA with comparison experiments and find that the augmented DEA model has advantages over the basic DEA model as well as the cross-efficiency and super-efficiency models. Finally, we present a case application with data obtained from a communication and aviation electronics company to demonstrate the applicability and use of augmented DEA.     

Data envelopment analysis with multiple modes of functioning. Application to reconfigurable manufacturing systems

In principle, data envelopment analysis (DEA) does not consider the possibility, which can occur in practice, of a production system being able to operate in different modes of functioning. In this paper, a new DEA modelling approach is proposed in which the different modes of functioning are taken into account and included in the analysis. The observed input consumption and output production in each mode of functioning is used to derive a mode-specific technology. The overall DEA technology aggregates these mode-specific technologies according to their respective time allocations. The proposed model computes a target operating point for each mode of functioning so that the operation of the overall system is efficient. The proposed approach is applied to assess the technical, cost and allocative efficiency of a reconfigurable manufacturing system. The inputs considered are modules/tools usage, labour and energy consumption. The outputs are the number of units produced of each part type. The production possibility set is determined by previous observations of the system functioning, from which the best practices can be identified. Technical, cost and allocative efficiency scores can be computed. The proposed approach not only generates input cost savings but also lead time reductions.     

A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers

Selection of advanced manufacturing technologies (AMTs) is an important yet complex decision which requires careful consideration of various performance criteria. This paper proposes the use of data envelopment analysis (DEA) with double frontiers for the selection of AMTs, which considers not only the best (optimistic), but also the worst (pessimistic) relative efficiencies of each AMT. Compared with the traditional DEA, the DEA approach with double frontiers can identify the best AMT correctly and easily without the need to impose any weight restriction or the need to calculate the cross-efficiency matrix, which requires a large number of computations and may also result in inconsistent conclusions by aggressive and benevolent cross-efficiency models. Four numerical examples are examined using the DEA approach with double frontiers to illustrate its simplicity and usefulness in AMT selection and justification.     

Determinants of scale efficiency in the Brazilian 3PL industry: a 10-year analysis

This paper focuses on the Brazilian third-party logistics (3PL) sector which, facing increasing competition, offers clients a wide variety of services/IT technologies in the quest to bolster efficiency. The main research objective is to determine which variables significantly impact the 3PLs' scale efficiency by applying a two-stage DEA model. Based on an unbalanced panel model, secondary data from the annual study published by Revista Tecnologística (years 2001–2010) were collected and analysed. The required inputs and outputs for the analysis were identified, as were the contextual variables that can affect the scale efficiency of the 3PLs. The results support findings in the literature regarding the role coordination processes play in logistics performance. Managerial implications for both shippers and 3PLs are discussed.     

Investigation of layered elastic theory prediction accuracy for asphalt concrete pavement design using micromechanical viscoelastic finite element modeling

In this study, predictions of full-scale micromechanical (MM) finite element (FE) models, developed from X-ray computed tomography images of asphalt concrete samples that were sawn from the accelerated pavement test sections, were used to evaluate the accuracy of layered elastic theory (LET) models that are used in pavement design today. First, MM FE and LET models were both calibrated using the measured strain gauge responses. Predictions of calibrated models were compared to evaluate the reasonableness of LET model outputs at high temperatures. Second, asphalt concrete stiffnesses measured in the laboratory were directly used for LET model development without performing any strain gauge calibration to evaluate the actual predictive capability of LET models in pavement design by using the calibrated MM FE model outputs as the ground truth. Recommendations were also made for future use of the MM FE models to improve the predictive capability of LET models.     

A system tradeoff model for processing options for household plastic waste

With the "Containers and Packaging Recycling Law", Japan has shown a firm conviction towards the promotion of recycling. Waste can be "recycled", i.e. resource value of waste material can be recovered, in many ways, from material recycling to energy recycling. Alternatively, waste can be reduced or disposed of in landfills. A system tradeoff model is developed from component process technology models of six different recycling and disposal options for household plastic waste processing: plastic pellet production, refuse derived fuel production, oil production, waste incineration to produce electricity, use of waste plastic as a coke substitute, and incineration for volume reduction. These technologies are compared with the case where all waste plastic is land filled. Models based on plant data, laboratory experiments, and theoretical considerations of scale effects and mass balances are developed to calculate the cost, energy consumption, CO₂ emission, and land fill occupancy. The models also calculate the valued products of each technology and convert them into cost, energy, CO₂, and landfill occupancy using life cycle inventory data. These values are subtracted from the outputs of the waste processing models to obtain overall performances for each technology. The overall tradeoff system model is then used to evaluate several scenarios of plastic recycling and disposal technologies in Tokyo. Electronic Publication     

Evaluation and interpretation of knowledge production efficiency

We propose an improved Data Envelopment Analysis (DEA) model to evaluate the efficiency of research groups in the area of information science in PR China. By taking the research groups as Decision Making Units (DMUs), the budget of the projects and size of the groups as inputs and the quantity and quality of publications produced by the groups as outputs of the model, the relative efficiencies of 21 research projects are evaluated. Then, we move to focus on the issues of knowledge management of the organizations that undertook these projects and attempt to explore the underlying reasons of high research efficiency. Through integrating the evaluation outcomes into research process, three indicators of knowledge management are identified for the best practice groups with highest research efficiency. The findings verify that the proposed model is valid and practical to assess research performances on the basis of bibliometric indicators.     

模糊权重约束的DEA模型及其应用

基于Zadeh模糊优越集定义,应用模糊权重约束DEA模型对医院服务效率进行评价.通过最大化模糊隶属函数来确定投入与产出权重的上限和下限,以避免传统DEA模型中权重为0的缺陷.实例选取期末实有床位数和医护人员数为两个投入变量,总诊疗人次和总出院人次为两个产出变量,评价广州市15所医院的相对服务效率.评价结果对医院管理决策更具客观性和实用性.     

Efficiency analysis of supply chain processes

We present an integrated benchmarking approach. To analyse the performance of inter-organizational (supply chain) processes at company level we combine dependency analysis and data envelopment analysis (DEA). DEA has been proven to be a powerful tool for benchmarking processes and for identifying the most efficient ones. Before using DEA analysis the inputs and outputs as well as the relevant dependencies have to be identified. To support the determination of input and output variables we propose to use dependency analysis. We illustrate the application of this integrated approach by analysing the results of an empirical benchmarking study of 65 European and North American companies. The study shows that make-to-stock is still the predominating manufacturing strategy of the analysed industries. Therefore, we utilize different DEA models with weight restrictions only for companies with a make-to-stock strategy. The results support our basic hypotheses that efficient supply chains lead to high financial performance. Furthermore they indicate improvement potential for the benchmarked supply chain processes.     

Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains

Data envelopment analysis (DEA) is a method for measuring performance of decision making units (DMUs). Conventional DEA models view DMUs as black boxes. Network DEA (NDEA) models have been developed to overcome this shortfall. This paper develops a new NDEA model based on modified enhanced Russell measure model. This paper measures performance of humanitarian supply chains (HSCs) by an NDEA model. Capabilities of the proposed model are addressed by theorems. However, in the real world, there might be stochastic data. This paper presents a stochastic version of the proposed NDEA model to measure the performance of HSCs. We analyse main properties of our model. We present a case study to demonstrate the applicability of the proposed model.     

An application of value-based DEA to identify the best practices in primary health care

In 2005, a new phase for primary health care (PHC) in Portugal began, leading to the voluntary and independent creation of Family Health Units. The impact of this reform is now being witnessed, which justifies the need to evaluate its results as far as best practices in PHC are concerned. This work uses a value-based data envelopment analysis (DEA) method, which combines an additive DEA model with multi-criteria decision analysis concepts, to allow incorporating managerial preferences in the performance assessment of 12 health centres in a Portuguese region. Two models are presented for evaluating access to PHC, which resulted from discussions with a group of decision makers. This analysis helped the decision makers in the identification of best practices, sources of inefficiency and gaps relatively to the best practices, thus leading to shape opportunities for improvement.     

Proposing integrated Shannon’s entropy–inverse data envelopment analysis methods for resource allocation problem under a fuzzy environment

In this study, a two-phase methodology for resource allocation problems under a fuzzy environment is proposed. In the first phase, the imprecise Shannon’s entropy method and the acceptability index are suggested, for the first time in the literature, to select input and output variables to be used in the data envelopment analysis (DEA) application. In the second step, an interval inverse DEA model is executed for resource allocation in a short run. In an effort to exemplify the practicality of the proposed fuzzy model, a real case application has been conducted involving 16 cement firms listed in Borsa Istanbul. The results of the case application indicated that the proposed hybrid model is a viable procedure to handle input–output selection and resource allocation problems under fuzzy conditions. The presented methodology can also lend itself to different applications such as multi-criteria decision-making problems.     

Building performance standards into data envelopment analysis structures

Data Envelopment Analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer Decision-Making Units (DMUs). DEA is particularly useful where no a priori information on the trade-offs or relations among various performance measures is available. However, it is very desirable if “evaluation standards,” when they can be established, be incorporated into DEA performance evaluation. This is particularly important when service operations are under investigation, because service standards are generally difficult to establish. A number of approaches have been developed to incorporate evaluation standards into DEA as reported in the literature. These approaches tend to be rather indirect, focusing primarily on the multipliers in the DEA models. This paper introduces a new way of building performance standards directly into the DEA structure. Based upon the conventional DEA model and an activity matrix, a set of standard DMUs can be generated and incorporated directly into the DEA analysis. The proposed approach is applied to a sample of 100 branches of a major Canadian bank where time standards are used to generate a set of standard bank branches.