Improved DEA models for measuring interval efficiencies of decision-making units

In a recent paper by Wang and Yang (2007) [6], a pair of bounded data envelopment analysis (DEA) models were proposed to measure the overall performances of a group of decision-making units (DMUs), which were characterized by interval efficiencies. In this paper, we show by a numerical example that the bounded DEA models are incapable of determining an efficiency interval for any DMU when there is a zero value for each output. A pair of improved bounded DEA models is thus proposed to overcome the drawback. Another example involving performance measurement of countries participating in the Athens 2004 Summer Olympic Games is presented to show that the proposed approach is an effective and practical method for performance analysis in real world situations.     

An integrated design methodology based on the use of group AHP-DEA approach for measuring lean tools efficiency with undesirable output

The selection of lean tools is one of the crucial factors for decision makers and practitioners in a competitive environment. A few efforts have been made based on problem selection. Conversely, numerical studies have been done on analytical hierarchy process (AHP)–data envelopment analysis (DEA) as well as DEA-undesirable variables separately. Thus, there is a shortage of lean practitioners as well as the methods involved. The present research aims at integrating AHP and DEA with desirable and undesirable factors to evaluate the lean tools and techniques and to rank the aspect of efficacy. We suggest a logical procedure to measure the efficacy of lean tools on leanness and to prioritize them as decision makers. In this extensive research, we apply the integrated multicriteria decision-making approach, including the hybrid groups AHP and DEA models with desirable and undesirable variables, to assess the relative efficiency of lean manufacturing tools and techniques. Case studies are used to demonstrate the lean implementation in companies while being validated by a panel of experts. The integration of these approaches has created synergy and shown to be even more powerful. Thus, the proposed integrated AHP-DEA model can evaluate and rank different alternatives while considering desirable and undesirable variables in the production processes.     

Performance evaluation of manufacturing systems based on dependability management indicators—case study: chemical industry

Dependability is a key decision factor in today’s global business environment affecting product cost and process. Dependability reflects user confidence in fitness for use by attaining satisfaction in product performance capability, delivery, service availability upon demand, and minimizing cost. The main objective of this study is to develop an integrated approach for evaluation of manufacturing systems based on dependability indicators for conducting a better dependability management system (DMS) through integration of the principal component analysis (PCA) and the data envelopment analysis (DEA). To achieve the objective of this study, an industrial sector—chemicals and chemical products in Iran—is selected as the case of this study in accordance with the International Standard for Industrial Classification of all economic activities (ISIC). Firstly, we define dependability indicators, for both inputs and outputs, based on IEC 60300. Due to the extra amount of indicators, we utilize a hierarchical structure to cluster the indicators for an easier analysis. Secondly, for reducing the number of some variables, we apply pair-wise comparison to assign weights and to unify the related sub-criteria to one main criterion. Finally, an integrated DEA–PCA approach is employed to assess the most and the least dependable units and to find critical indicators in macro and micro levels in order to make policy for implementation of DMS in manufacturing systems.     

Equivalent multiplier and envelopment DEA models for measuring efficiency under general network structures

Data envelopment analysis (DEA) has been shown to be a very useful mathematical programming tool to measure the relative efficiency of decision making units (DMUs), especially when the so-called internal network structure of the production process is taken into account. Under a network structure, however, two standard directions of modeling the production process may generally lead to a pair of multiplier and envelopment DEA models so that the outcomes are not necessarily equivalent, i.e. a network duality problem occurs. Although, the duality problem has recently been addressed for specific cases of network structures, for more complex structures, DEA models have only been able to be developed by following either the envelopment form or multiplier form. Investigating this duality problem, this paper also proposes DEA models for general network structures with two additional properties. Due to the first property, all factors in a general network structure including main inputs/outputs and/or intermediate inputs/outputs can be shared among the divisions while the second property assumes that a factor in a structure may be considered as both intermediate input/output and main input/output simultaneously. We will show that the proposed network DEA models cannot only deal with the already existing general network structures in the literature, but are also represented by dual multiplier and envelopment linear programming-based problems by which consistent outcomes can be obtained. A comprehensive numerical example will be presented to explain the properties and features of the suggested models.     

A new fuzzy network data envelopment analysis model for measuring the performance of agility in supply chains

Data envelopment analysis (DEA) is a linear programming method for assessing the efficiency and productivity of organizational units called decision-making units (DMUs). We propose a new network DEA (NDEA) model for measuring the performance of agility in supply chains. The uncertainty of the input and output data is modeled with linguistic terms parameterized with fuzzy sets. The proposed fuzzy NDEA model is linear and independent of the α-cut variables. The linear feature allows for a quick identification of the global optimum solution and the α-cut independency feature allows for a significant reduction in the computational efforts. We show that our model always generate solutions within a bounded feasible region. Our model also eliminates the potential for conflict by producing unique interval efficiency scores for each DMU. The proposed model is used to measure the performance of agility in a real-life case study in the dairy industry.     

A note on data envelopment analysis with missing values: an interval DEA approach

Smirlis et al. (Appl Math Comput 177(1):1–10, 2006) have proposed a pair of interval data envelopment analysis (DEA) models for computation of the efficiency of decision-making units (DMUs) in the presence of missing data. In this paper, we show that the interval DEA models presented by Smirlis et al. have some drawbacks due to the use of variable production frontier for computation of the efficiency intervals of DMUs. To overcome these drawbacks, this paper presents new interval DEA models based on interval arithmetic. It is shown that the proposed interval DEA models do not need extra variable changes and use a fixed, unified production frontier for computation of the efficiency intervals of the DMUs with interval input and output data. A numerical example is presented to illustrate the potential applications of the new interval DEA models and their effectiveness for measuring the interval efficiencies of the DMUs.     

A decision support system for rapid one-of-a-kind product development

A decision support system (DSS) is a specific class of computerized information systems that support decision-making activities. Such a system has become paramount in supporting manufacturing activities with the development of World Wide Web (WWW) technology in recent years. The research and development of a knowledge-based DSS dedicated to rapid one-of-a-kind products (OKPs), however, has, to the author’s knowledge, rarely been directly studied. This work presents a knowledge-based DSS for supporting decision-making activities in developing OKPs using broad knowledge base content. The underlying architecture of the proposed system is a system that manages and optimizes the data, information and knowledge in the product development process. A knowledge structure model is proposed in this paper, and a comprehensive set of expert knowledge and analytical/numerical methods are integrated into the system to automate intelligent decision-making. Case studies have shown that the knowledge-based DSS is able to help OKP companies develop new products more quickly by sharing optimization tools, information and knowledge to reduce possible errors and rework.     

Input and output scaling in advanced manufacturing technology: theory and application

This paper suggests new data envelopment analysis (DEA) models for input and output scaling in advanced manufacturing technology (AMT). For a given group of AMT observations using the traditional DEA models, it is not possible to evaluate the units when a specified input (or specified output) is required to be scaled for all units. The paper provides theoretical results for obtaining the relationship between the original AMT observations and the corresponding scaled data. Also, the paper uses numerical illustrations to show the usefulness of the suggested contribution.     

An integrated fuzzy DEA–fuzzy C-means–simulation for optimization of operator allocation in cellular manufacturing systems

This paper presents an integrated fuzzy data envelopment analysis (FDEA), fuzzy C-means and computer simulation for optimization of operator allocation in cellular manufacturing systems (CMS). This is a challenging issue in flexible manufacturing systems in general and in CMS in particular. A computer simulation model, which considers various operators layout, is developed. For considering more information from computer simulation report, the output data is fuzzified. FDEA is utilized to assess simulation alternatives in various levels of uncertainty. In addition, FDEA ranking of decision-making units are clustered by fuzzy C-means method. Each of the clusters indicates a degree of desirability for operator allocation. Previous studies only utilize multivariate analysis methods and simulation, whereas this study uses an integrated simulation, fuzzy DEA, fuzzy C-means, and fuzzy indicators. Furthermore, previous CMS studies consider only one type of product, whereas this study considers multiproduct for CMS modeling through simulation. Moreover, more robust CMS assessment indicators are used in the proposed model. A practical case study illustrates the effectiveness and superiority of the proposed methodology. In addition, we compared the results of U shape with other production types, namely spiral, straight, L, W, zigzag, and Z. Moreover, we show that it dominates the other production types.     

An efficient DEA method for ranking woven fabric defects in textile manufacturing

This paper deals with the problem of ranking woven fabric defects (WFDs) observed in textile manufacturing using a data envelopment analysis (DEA) method. The paper shows that the optimal solutions of DEA models for decision-making units (DMUs) with multiple inputs can be found without the need of solving the corresponding models. The paper performs a mean–variance analysis for determining the most important statistical factors of WFDs in terms of multiple inputs. The paper also ranks the observed WFDs from the worst preferred using the suggested DEA formulation. The contribution of this study can be explained as follows. It introduces a new application for DEA method in textile manufacturing for ranking fabric defects. This is significant in defining rich project in reducing defects through prioritizing of quality specification of fabric defects by Six Sigma experts. Also, the result of this paper can be obtained using an efficient DEA method without the need of solving the corresponding DEA models for any sample size of fabric defects.     

Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values

Data envelopment analysis (DEA) is an important managerial tool for evaluating and improving the performance of decision making units. The existing DEA models are mostly limited to static environment using crisp data and are time-consuming and also have weak discriminating power. The aim of this work is to introduce a new fuzzy dynamic DEA model with missing values, which benefits from strengths of multi-objective modeling to overcome weakness and drawbacks of the classic DEA models. To check for quality and accuracy of the proposed model, this paper offers a comparative study to compare the discriminating power and computational efforts of the model with two problems in the literature taken as benchmarks. Also, this paper presents a real application of the fuzzy dynamic DEA model for assessing and ranking the level of performance for 56 railways around the globe using real data gathered from credible sources. The numerical case illustrates the model and the result may be used by railways to improve their performance efficiency compared to the best in the sample. Results for the comparative study and the real case reveal significant improvement in computational time and discriminating power.     

Performance measurement of decision-making units under uncertainty conditions: An approach based on double frontier analysis

Data envelopment analysis (DEA) is an approach to measure the relative efficiency of a set of decision-making units (DMUs) which uses multiple inputs to produce multiple outputs. In real world situations, due to uncertainty, DEA is sometimes faced with imprecise inputs and/or outputs. Therefore, performance measurement must often be performed under uncertainty conditions. Generally, the performance of DMUs can be evaluated from two perspectives—optimistic and pessimistic. As a result, two different evaluations are obtained for each DMU. In this article, we first obtain the efficiencies of the DMUs under evaluation from both optimistic and pessimistic views. The optimistic view evaluates each DMU with a set of the most desirable weights; the efficiencies measured by the optimistic approach are called optimistic efficiencies. The pessimistic view evaluates each DMU with a set of the most undesirable weights; the efficiencies measured by the pessimistic approach are called pessimistic efficiencies. Then it is shown that the outcomes of these two evaluations are conflicting with each other, being undoubtedly biased, unrealistic, and unconvincing. To overcome this problem, we propose a new measure of overall performance which is used for integrating the measures obtained from optimistic and pessimistic views and we will use it to identify the DMU with the best performance under uncertainty conditions. Also, we propose new fuzzy DEA models that evaluate a DMU from the pessimistic perspective in a fuzzy context. The proposed measure will be shown with two numerical examples, including the selection of a flexible manufacturing system.     

Technology selection in the presence of fuzzy data and dual-role factors

Technology selection is an important part of management technology. One of the models which is used for technology selection is data envelopment analysis (DEA). Conventional DEA models require input and output data to be precisely known, and also they assume that decision making units do not have dual-role factor, but this is not always the case in real applications, such as technology selection. In this regard, a model for technology selection in the presence of fuzzy data and dual-role factors is developed in the present study. A numerical example demonstrates the application of the proposed method.     

The most efficient unit without explicit inputs: An extended MILP-DEA model

Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of each decision making units (DMUs) with multiple inputs and multiple outputs. DEA and Discriminant Analysis (DA) are similar in classifying units to exhibit either good or poor performance. On the other hand, selecting the most efficient unit between several efficient ones is one of the main issues in multi-criteria decision making (MCDM). Some proponents have suggested some approaches and claimed their methodologies involve discriminating power to determine the most efficient DMU without explicit input.     

Evaluating and ranking sustainable suppliers by robust dynamic data envelopment analysis

One of the advantages of data envelopment analysis (DEA) is to determine benchmarks for inefficient decision making units (DMUs). However, determination of the benchmarks is the result of past performance of DMUs. In other words, the benchmarks do not provide any recommendation for improvement of future efficiency of DMUs. On the other hand, in dynamic DEA models often no DMU gets the efficiency score of unity. In this case, although we can rank the DMUs, we cannot introduce an efficient DMU and benchmarks. To overcome these shortcomings we propose a dynamic ideal DMU using dynamic DEA and scenario-based model of robust. A case study showing the model in use provided.     

Supplier selection by the new AR-IDEA model

Traditionally, supplier-selection models have been based on cardinal data with less emphasis on ordinal data. However, with the widespread use of manufacturing philosophies such as just-in-time (JIT), emphasis has shifted to the simultaneous consideration of cardinal and ordinal data in the supplier-selection process. The application of data envelopment analysis (DEA) for supplier-selection problems is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. The objective of this paper is to propose a new pair of assurance region-imprecise data envelopment analysis (AR-IDEA) model for selecting the best suppliers in the presence of both weight restrictions and imprecise data. A numerical example demonstrates the application of the proposed method.     

Finding the best asset financing alternative: A DEA–WEO approach

Measurement of performance is an important activity in identifying weaknesses in managerial efficiency and devising goals for improvement. Data envelopment analysis (DEA) is a mathematical quantitative approach for measuring the performance of a set of similar units. Toloo (2013) extended a DEA approach for finding the most efficient unit considering a data set without explicit inputs. The aim of this paper is to develop DEA models without explicit outputs, henceforth called DEA–WEO, to find the most efficient unit when outputs are not directly considered. The suggested models directly utilize the data without the need of adding a virtual output, whose value is equal to for all units. A real data set involving 139 different alternatives for long-term asset financing provided by Czech banks and leasing companies is taken to illustrate the potential application of the proposed approach.     

Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models

The sensitivity of various features that are characteristics of machine performance may vary significantly under different working conditions. Thus it is critical to devise a systematic feature extraction (FE) approach that provides a useful and automatic guidance on using the most effective features for machine performance prediction without human intervention. This paper proposes a locality preserving projections (LPP)-based FE approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The effectiveness of the proposed approach for bearing defect and severity classification is evaluated experimentally on bearing test-beds. Furthermore, a novel health assessment indication, Gaussian mixture model (GMM)-based negative log likelihood probability (NLLP) is developed to provide a comprehensible indication for quantifying bearing performance degradation. The proposed approach has shown to provide better performance than using regular features (e.g., root mean square (RMS)). The experimental results indicate potential applications of LPP-based FE and GMM as effective tools for bearing performance degradation assessment.     

A new DEA model for technology selection in the presence of ordinal data

This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach.     

A cost efficiency approach for strategic vendor selection problem under certain input prices assumption

Strategic vendor selection problem (VSP) has been investigated in different purchasing literature during the last two decades. Indeed, senior purchasing managers always deal with such crucial decisions. Manufacturing managers in the global market are faced with challenging and complex tasks very similar to VSP. Increasing outsourcing and opportunity provided by automotive industry to the worldwide markets make these decisions, even more, complex. Various methodologies, from simple weighted scoring methods to complex mathematical programming models, are introduced to tackle the VSP.Data envelopment analysis (DEA) is a non-parametric method in operations research and economics for evaluating the productive efficiency of decision-making units (DMUs). This study utilizes the proposed approach in Toloo and Ertay (2014) to develop a method for finding the most cost efficient DMU when the prices are fixed and known. A case study of an automotive company located in Turkey is adapted from the literature to illustrate the potential application of the suggested approach.