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.     

Evaluating bid item prices to support contractor selection ‐ a case study

Abstract

Bid price is a highly‐weighted appraisal criterion in a multi‐criteria evaluation method to select contractors for construction projects. By evaluating both the magnitude and reasonableness of the bid price, this study presents an electronically‐facilitated model for evaluating bid prices. The model defines a reasonable total price for a project and a reasonable cost for each cost category (or bid item) by considering the project prices estimated by the project owner and submitted by all qualified bidders. Scoring systems are then employed to score the prices submitted by bidders, and weighting methods are used to integrate the derived scores. The bidder with the highest integrated score receives the most favorable price appraisal. The merits of the proposed model are demonstrated by its successful application to a recent public construction project in Taiwan.     

Model for analysis of heterogeneity in product acquisition procurement

The heterogeneity of vendors is conceived of as a critical factor in determining the most appropriate tender selection method for product acquisition. This measure's adoption was due to the difficulty of finding the best products at the least cost as stipulated in Taiwan's Government Procurement Law. Although quality/performance is considered a major contributor, there is no objective method to measure the heterogeneity of vendors. This article presents a new model, namely Price/Performance Analysis Model (P/PAM), that combines the economic utility function, price/performance relationship, and price elasticity for the purpose of establishing a quantitative model to analyze the heterogeneity of a product procurement. A case was selected from the literature to demonstrate the applicability of the proposed P/PAM.     

A comparison of stochastic dominance and stochastic DEA for vendor evaluation

Vendor selection involves decisions balancing a number of conflicting criteria. Data envelopment analysis (DEA) is a mathematical programming approach capable of identifying non-dominated solutions, as well as assessing relative efficiency of dominated solutions. A simple multi-attribute utility function can be applied to a small set of alternatives, providing a tool to assess relative value, but is subject to error if estimated measures are not precise. This paper compares stochastic DEA with a multiple-criteria model in a vendor selection model involving multiple criteria, reporting simulation experiments varying the degree of uncertainty involved in model parameters.     

Supplier selection using analytic network process and data envelopment analysis

The present study intends to present a supplier selection method, which also considers green indicators due to environmental protection issues, by using an analysis network process (ANP) as well as data envelopment analysis (DEA). ANP which is able to consider the interdependency between criteria releases the constraint of DEA that the users cannot set up criteria weight preferences. The model evaluation results for high-tech industry show that the proposed method can provide not only reasonable outcomes with a smaller number of decision making units, but can also provide more consistent results.     

基于DEA的供应商选择方法研究

考虑到现有方法的局限性,本文通过应用锥比率的C^2WH模型给出了不需要预先给定权重但可以体现决策者偏好的供应商选择方法。并应用这种方法对某型号零件的供应商的选择问题进行了研究。最后给出了在应用该方法进行供应商选择时,一些特殊情形的处理方法。     

DEA cross-efficiency evaluation based on satisfaction degree: an application to technology selection

Data envelopment analysis (DEA) has been extended to cross-efficiency evaluation to provide better discrimination and ranking of decision-making units (DMUs). However, the non-uniqueness of optimal weights in the traditional DEA models (CCR and BCC models) has reduced the usefulness of the DEA cross-efficiency evaluation method. To solve this problem, we introduce the concept of the satisfaction degree of a DMU towards a set of optimal weights for another DMU. Then, a new DEA cross-efficiency evaluation approach, which contains a maxmin model and two algorithms, is proposed based on the satisfaction degrees of the DMUs. Our maxmin model and algorithm 1 can obtain for each DMU an optimal set of weights that maximises the least satisfaction degrees among all the other DMUs. Further, our algorithm 2 can then be used to guarantee the uniqueness of the optimal weights for each DMU. Finally, our approach is applied to a real-world case study of technology selection.     

Combining balanced scorecard with data envelopment analysis to conduct performance diagnosis for Taiwanese LED manufacturers

Light emitting diode (LED) is a popular component to replace the traditional lighting source or advertising sign display. In 2014, high-brightness LED has a strong growth in backlight display, mobile appliances, automotive devices and outdoor illumination. However, emerging technologies in compound materials, epitaxying, packaging and new entrants result in a scale-based economy and intensively competitive environment. Inspired by the concept of business analytics, this paper proposes a novel framework to conduct corporate diagnosis for Taiwanese LED manufacturers: (1) balanced scorecard is fused with data envelopment analysis to address the impact of operational efficiency on performance outcomes, (2) financial and non-financial indicators are incorporated into the process of performance measurement, (3) the intricate causalities between key performance indicators (KPIs) and multiple outcomes (i.e. earnings per share and return on equity) are captured and (4) managerial insights are provided to indicate adaptive adjustment on significant KPIs. More importantly, a data-set comprising representative Taiwanese LED companies spanned from 2010 to 2014 is used to justify the validity of the proposed framework.     

Mutual fund performance evaluation using data envelopment analysis with new risk measures

The data envelopment analysis (DEA) technique has been found very useful for evaluating the mutual fund performance. This applied study extends previous results in two ways: to properly reflect the pervasive skewness and leptokurtosis in return distributions of actively managed funds, new risk measures value-at-risk (VaR) and conditional value-at-risk (CVaR) are introduced into inputs of the existing DEA models; to fairly evaluate the relative performance of the same fund during different time periods, we creatively treat the same fund during different periods as different decision making units. Except for confirming current empirical conclusions, detailed empirical analyses using data of the Chinese mutual fund market show that, VaR and CVaR, especially their combinations with traditional risk measures, are very helpful for comprehensively describing return distribution properties and fund characteristics such as the asset allocation structure, which, in turn, can better evaluate the overall performance of mutual funds. Treating the same fund during different time periods as different funds can not only show the specific performance variation, but reveal the reasons for that variation.

机构选型的改进多级模糊综合评判

 机构选型多级模糊评判的核心计算是实现隶属度转换；但是,现有隶属度转换方法包含冗余性,表现在指标隶属度中对目标分类不起作用的冗余部分也被用于计算目标隶属度.为此,用基于熵的数据挖掘方法,通过挖掘隐藏在各指标隶属度中关于目标分类的知识信息定义指标区分权；用区分权清除指标隶属度中对目标分类不起作用的冗余数值并提取有效值；有效值经指标重要性权重转化为可比值；用可比值计算目标隶属度实现隶属度转换.由此建立机构选型的改进模糊评判模型.     

运用WA-Delphi的钢铁企业经营效率评价方法

进行经营效率评价时充分考虑到定量分析和定性分析两个方面,把二者有机结合起来设置效率评价的相应指标,并在已有的绩效评价方法基础上,建立了一种新的采用权重分配的德尔菲法和数据包络分析相结合的评价方法。实证研究结果表明该模型对企业运行效率的评价是有效和可靠的。     

A note on the application of the data envelopment analytic hierarchy process for supplier selection

In a very recent paper by Sevkli et al. (Sevkli, M., Koh, S.C.L., Zaim, S., Demirbag, M. and Tatoglu, E., 2007. An application of data envelopment analytic hierarchy process for supplier selection: a case study of BEKO in Turkey. International Journal of Production Research, 45 (9), 1973–2003), the data envelopment analytic hierarchy process (DEAHP) was claimed to outperform the analytic hierarchy process (AHP) method and provide a better decision for supplier selection. This note illustrates the weaknesses of the DEAHP, demonstrating the invalidity of the above claim. The case study of BEKO in Turkey is re-examined and shows that extra verification is needed to come to a decision.     

公交企业运营绩效的信息熵与SE-DEA组合评价方法

提出了基于信息熵与超效率DEA模型(SE-DEA)的公交企业运营绩效组合评价方法,弥补了传统DEA模型无法对多个有效率单元再排序的缺陷,并降低了SE-DEA模型受指标维度影响的风险,进一步提高了其判别能力。以12家公交企业为研究对象,将公交行业监管、公交企业运营服务与乘客出行需求相结合,基于组合评价方法对公交企业运营绩效进行评价,并与SE-DEA模型得到的评价结果进行对比分析。结果表明,公交企业运营绩效值波动较大;组合评价方法所得到的公交企业运营绩效排序结果与SE-DEA模型得到的排序结果在总体趋势上是一致的,但两者测算的运营绩效值相差很大,且前者的判别能力比后者更强。根据评价结果提出可行建议,以便于顺利实施"优先发展公共交通"的战略。     

A modified common weight model for maximum discrimination in technology selection

This paper provides a modification of a recently developed common weight model for technology selection. The discrimination power of the existing model depends on a discriminating parameter. Although larger values of this parameter can give more discrimination, it should also be small enough to guarantee the existence of at least one efficient DMU. Hence, different values of this parameter should be examined to determine the proper value. In the model proposed here, the largest possible value of the parameter is utilised to maximise the discrimination power, and the existence of an efficient DMU is guaranteed by adding a few constraints. In addition, the paper presents comments on existing models, and the properties and the advantages of the model are explained. The contents of the paper are illustrated by several numerical examples.     

A hierarchical model for optimal supplier selection in multiple sourcing contexts

This paper addresses a crucial objective of the strategic purchasing function in supply chains, i.e. optimal supplier selection. We present a hierarchical extension of the data envelopment analysis (DEA), the most widespread method for supplier rating in the literature, for application in a multiple sourcing strategy context. The proposed hierarchical technique is based on three levels. First, a modified DEA approach is used to evaluate the efficiency of each supplier according to some criteria proposed by the buyer. Second, the well known technique for order preference by similarities to ideal solution (TOPSIS) is applied to rank the maximally efficient suppliers given by the previous step. Third and finally, a linear programming problem is stated and solved to find the quantities to order from each maximally efficient supplier in the multiple sourcing context. The presented approach is able to straightforwardly discern between efficient and inefficient partners, avoid the confusion between efficient and effective suppliers and split the supply in a multiple sourcing context. The hierarchical model is applied to the supply of a C class component to show its robustness and effectiveness, while comparing it with the DEA and TOPSIS approaches.     

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand one's own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a country's road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the model's input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.     

Using common weights and efficiency invariance principles for resource allocation and target setting

Data envelopment analysis (DEA) has proven to be a useful technique for evaluating the relative performance of comparable and homogeneous decision-making units (DMUs). In recent years, DEA-based resource allocation and target setting approaches have gained more and more attention from both practitioners and academic researchers. In this paper, we propose a new mechanism to simultaneously adopt the principles of common weights and efficiency invariance in allocating multiple resources and setting multiple targets among DMUs. To obtain the final plan, we minimise the deviation between the possible plan based on common weights and another feasible plan emphasising efficiency invariance. If the minimum deviation equals zero, one optimal plan will be determined. In general situations, however, the proposed approach will present two plans that have a non-zero deviation. One is generated using a common set of weights for all DMUs in such a way that the change of efficiencies is minimised, while the other is generated by strictly keeping efficiency scores unchanged yet having similar or even identical weights on input–output measures for each DMU to the utmost extent. The efficacy and usefulness of the proposed approach are demonstrated using a numerical example from previous literature and an empirical application to an urban bus company in China.     

A new DEA common-weight multi-criteria decision-making approach for technology selection

This paper addresses an advanced manufacturing technology selection problem by proposing a new common-weight multi-criteria decision-making (MCDM) approach in the evaluation framework of data envelopment analysis (DEA). We improve existing technology selection models by giving a new mathematical formulation to simplify the calculation process and to ensure its use in more general situations with multiple inputs and multiple outputs. Further, an algorithm is provided to solve the proposed model based on mixed-integer linear programming and dichotomy. Compared with previous approaches for technology selection, our approach brings new contributions. First, it guarantees that only one decision-making unit (DMU) (referring to a technology) can be evaluated as efficient and selected as the best performer while maximising the minimum efficiency among all the DMUs. Second, the number of mixed-integer linear programs to solve is independent of the number of candidates. In addition, it guarantees the uniqueness of the final optimal set of common weights. Two benchmark instances are used to compare the proposed approach with existing ones. A computational experiment with randomly generated instances is further proceeded to show that the proposed approach is more suitable for situations with large datasets.     

Enhancing multivariate pattern analysis for magnetoencephalography through relevant sensor selection

While the popularity of multivariate pattern classification is growing rapidly in magnetoencephalography (MEG) data analysis, the analysis pipelines used by the neuroscience community are still missing some fundamental machine-learning techniques and principles that would increase their effectiveness. Here, we show that MEG decoding accuracy improves significantly with the addition of feature selection methods to the analysis pipeline. We compare one unsupervised and two supervised feature reduction methods in the current study. Our results show that supervised feature selection methods like statistical dependency and mutual information improve decoding performance and attain higher session-to-session reliability compared to unsupervised dimensionality reduction methods like principal component analysis. Furthermore, we demonstrate that the selected sensors in the data related to a visual task at each time point are consistent with the pattern reflecting the sweep of information in the ventral visual pathway.     

Reliability and economic analysis and improvement of complex mechanical products based on DEA: An empirical case study of hydraulic products

Complex mechanical products involve many specialized fields such as machinery, electronics, hydraulics, and computers, so their initial cost is usually higher. In addition, regular and irregular maintenance are also needed to extend their service life, so that the cost of the product in its life cycle will be increased. Therefore, for the manufacturing enterprises of complex mechanical products, it is very important to achieve the best matching between reliability and economy. In order to survive in the fierce market competition, it is necessary to compare with other similar products in the market, so as to clearly understand the level of their products, find out the gap, and improve it. However, the existing product reliability assessment methods based on probability and statistics theory cannot provide such help for them to solve this problem. Therefore, this paper puts forward a method to evaluate and improve complex mechanical products by using a data envelopment analysis (DEA) model considering the reliability and economy of product comprehensively. Through the relative effectiveness of DEA, the same type of products on the market can be compared, providing specific method for enterprises to evaluate and improve their own product level to achieve the best matching of economy and reliability. This paper introduces the principle and steps of this method and takes the hydraulic component manufacturing enterprise as an example to carry out practical research, which verifies the feasibility and effectiveness of this method.