Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

Increasing Sustainability of Logistic Networks by Reducing Product Losses: A Network DEA Approach

This paper considers a multiproduct supply network, in which losses (e.g., spoilage of perishable products) can occur at either the nodes or the arcs. Using observed data, a Network Data Envelopment Analysis (NDEA) approach is proposed to assess the efficiency of the product flows in varying periods. Losses occur in each process as the observed output flows are lower than the observed input flows. The proposed NDEA model computes, within the NDEA technology, input and output targets for each process. The target operating points correspond to the minimum losses attainable using the best observed practice. The efficiency scores are computed comparing the observed losses with the minimum feasible losses. In addition to computing relative efficiency scores, an overall loss factor for each product and each node and link can be determined, both for the observed data and for the computed targets. A detailed illustration and an experimental design are used to study and validate the proposed approach. The results indicate that the proposed approach can identify and remove the inefficiencies in the observed data and that the potential spoilage reduction increases with the variability in the losses observed in the different periods.Ministerio de Ciencia DPI2017-85343-PFondo Europeo de Desarrollo Regional DPI2017-85343-

A hybrid method of GRA and DEA for evaluating and selecting efficient suppliers plus a novel ranking method for grey numbers

Purpose: Evaluation and selection of efficient suppliers is one of the key issues in supply chain management which depends on wide range of qualitative and quantitative criteria. The aim of this research is to develop a mathematical model for evaluating and selecting efficient suppliers when faced with supply and demand uncertainties. Design/methodology/approach: In this research Grey Relational Analysis (GRA) and Data Envelopment Analysis (DEA) are used to evaluate and select efficient suppliers under uncertainties. Furthermore, a novel ranking method is introduced for the units that their efficiencies are obtained in the form of interval grey numbers. Findings: The study indicates that the proposed model in addition to providing satisfactory and acceptable results avoids time-consuming computations and consequently reduces the solution time. To name another advantage of the proposed model, we can point out that it enables us to make decision based on different levels of risk. Originality/value: The paper presents a mathematical model for evaluating and selecting efficient suppliers in a stochastic environment so that companies can use in order to make better decisions.Peer Reviewe

Sustainability-oriented efficiency of retail supply chains: a combination of Life Cycle Assessment and dynamic network Data Envelopment Analysis

Assessing the efficiency of retail supply chains (RSCs) requires analytical tools that address the different activities involved in these chains. In this sense, dynamic network Data Envelopment Analysis (DEA) arises as a suitable method to evaluate the operational performance of RSCs over a period of time. However, its use for sustainability-oriented efficiency assessment constitutes a knowledge gap that limits its applicability for thorough decision-making processes, e.g. at the retail company level. This article fills this gap through the combination of Life Cycle Assessment (LCA) and dynamic network DEA. A novel five-step LCA + DEA approach is proposed and applied to a case study of 30 RSCs in Spain for the period 2015-2017. In this case, the supply chain structure involves three divisions: central distribution, operation of retail stores, and home delivery. Both overall- and term-efficiency scores were found to widely range from 0.38 to 1.00, with only 1 RSC deemed efficient. Regarding divisional efficiency, store operation was found to generally show significantly higher efficiency scores than the distribution divisions. The link between long distribution distances and low efficiency stresses the relevance of integrating a network perspective into the efficiency assessment. In addition to efficiency scores, the LCA + DEA approach enriches the assessment by providing environmental, operational and socio-economic benchmarks to further support the management of RSCs from a sustainability perspective.publishe

Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with reverse logistics

Introduction− Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia.Objective−The aim is to measure the relative perfor-mance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics.Methodology−A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the net-work; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system.Results− KPIs are defined, data is collected and KPI’s for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system are analyzed to find options for improving the system.Conclusions−Reverse logistics, brings numerous ad-vantages for companies. The analysis of the indicators allows logistics managers involved to make relevant deci-sions for higher performance. The DEA model identifies which DCs have a relative superior and inferior perfor-mance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network.Introducción− El análisis envolvente de datos (DEA), se usa para medir el desempeño relativo de una serie de centros de distribución (DCs), utilizando indicadores clave basados en logística inversa para una empresa que produce suministros eléctricos y electrónicos en Colombia.Objetivo− Medir el rendimiento relativo de los centros de distribución en función de indicadores clave (KPI) de una red de abastecimiento con logística inversa.Metodología− Se aplica un modelo DEA a través de 5 pasos: Selección de KPIs; Recopilación de datos para los 18 DCs en la red de distribución; Se construye y ejecuta el modelo DEA; Identificar los DCs que serán el foco de la mejora; Analizar los DCs que restringen o disminuyen el rendimiento total del sistema.Resultados− Inicialmente se definen KPI, a partir de los datos recolectados y se presentan los KPI para cada DCs. Se ejecuta el modelo DEA y se determinan las eficiencias relativas para cada DCs. Posteriormente, se realiza un análisis de la frontera y se analizan los DCs que limitan o reducen el rendimiento del sistema en busca de opciones para mejorar el sistema.Conclusiones− La logística inversa, trae numerosas ven-tajas para las empresas. El análisis de los indicadores permite a los gerentes de logística tomar decisiones rel-evantes para mejorar el desempeño del sistema. El mod-elo DEA identifica a los DCs que presentan rendimientos relativamente superiores e inferiores; lo cual facilita la toma de decisiones informadas para cambiar, aumentar o disminuir los recursos y las actividades, o aplicar las mejores prácticas que optimicen el rendimiento de la red

A new DEA-GAHP method for supplier selection problem

Supplier selection is one of the most important decisions made in supply chain management. Supplier evaluation problem has been in the center of supply chain researcher’s attention in these years. Managers regard some of these studies and methods inappropriate due to simple, weight scoring methods that generally are based on subjective opinions and judgments of decision maker units involved in the supplier evaluation process yielding imprecise and even unreliable results. This paper seeks to propose a methodology to integrate data envelopment analysis (DEA) and group analytical hierarchy process (GAHP) for evaluating and selecting the most efficient supplier. We develop a methodology, which consists of 6 steps, one by one has been introduced in lecture and finally applicability of proposed method is indicated by assessing 12 suppliers in a numerical example

EVALUATION OF IRANIAN SMALL AND MEDIUM-SIZED INDUSTRIES USING THE DEA BASED ON ADDITIVE RATIO MODEL – A REVIEW

Data Envelopment Analysis (DEA) is a prominent procedure in the decision-making process with a pivotal role in the sustainable development assay. Project identification is the first step of sustainability assessment in the Environmental Impact Assessment (EIA) program for the industrial projects prior to complete establishment. The present review research comprised 405 Iranian industries assessment regarding both input and output criteria via DEA integrated with the ratio model of Additive Ratio ASsessment (ARAS) and weighing systems of Kendall and Friedman's tests supported by SPSS software. The findings deployed a classification for Iranian industries pertaining to industries' nominal capacity in certain clusters. Also, the current review paved the pathway towards executing both energy and materials streams in industries

Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks

For many years, supplier selection as an important multi-criteria decision has attracted both the researchers and practitioners. Recently, high incidences of natural disasters, terrorism attacks, labor strikes, and other kinds of risks, also known as disruptions, indicate the vulnerability of procurement process to these unpredicted events. In this study, a new framework is introduced to select suppliers while considering the supply risks. In the proposed framework, an expert is asked to determine the reliability of each procurement element (i.e., production, transportation, and communication) based on some proposed risk factors. Then, a distinct Multi-Layer Perceptron (MLP) network is trained to play the role of the expert opinion for estimating the reliability scores of each procurement. In addition to reliabilities, the Data Envelopment Analysis (DEA) is used to take into account the conventional selection criteria: price, delivery, quality, and capacity. A set of Pareto-optimal suppliers is obtained from the combination of efficiencies and reliability scores. Finally, the decision maker is recommended to choose between the non-dominated suppliers. Obtained experiment results indicate the effectiveness of the proposed framework

Supplier evaluation and selection in fuzzy environments: a review of MADM approaches

In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described by fuzzy sets. This study provides a review of applications of MADM approaches for evaluation and selection of suppliers in a fuzzy environment. To this aim, a total of 339 publications were examined, including papers in peer-reviewed journals and reputable conferences and also some book chapters over the period of 2001 to 2016. These publications were extracted from many online databases and classified in some categories and subcategories according to the MADM approaches, and then they were analysed based on the frequency of approaches, number of citations, year of publication, country of origin and publishing journals. The results of this study show that the AHP and TOPSIS methods are the most popular approaches. Moreover, China and Taiwan are the top countries in terms of number of publications and number of citations, respectively. The top three journals with highest number of publications were: Expert Systems with Applications, International Journal of Production Research and The International Journal of Advanced Manufacturing Technology