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 Decomposed Data Analysis Approach to Assessing City Sustainable Development Performance: A Network DEA Model with a Slack-Based Measure

This paper deals with urban sustainable development in China. We propose a network data envelopment analysis (DEA) model with a slack-based measure (SBM) to analyze the eco-efficiency of 284 Chinese cities, enabling us to find a way to open the “black box” in conventional DEA models and introduce social well-being factors into the model, and depict the role of local government in providing public service and improving social well-beings. We set up a framework of urban development by dividing the process of into two steps. The first stage is a production system translating inputs and natural resources into GDP and waste production, which will be inputs to the second stage for distribution and consumption to realize social welfare and environmental protection. The results show eco-efficiency of Chinese cities experienced a significant decrease from 2005 to 2016, which should be mainly attributed to the distribution and consumption processes. Structural differences are described by regions, administrative level and clusters. These results are compared with an existing urban sustainability index system developed by McKinsey and an ANOVA approach is conducted to reveal differences between cities across regions and clusters. This article sheds new light on the understanding of urban sustainable construction and development in China regarding the service performance of local government. View Full-Tex

The governance-production nexus of eco-efficiency in Chinese resource-based cities:A two-stage network DEA approach

For decades, resource-based cities in China have significantly contributed to China's socio-economic development. The heavy resource dependence of resource-based cities inevitably leads to a series of environmental problems. Mitigating environmental impacts in an unthinking manner might be disruptive for economic development. Improving eco-efficiency has been a crucial solution for protecting the environment while mitigating its negative economic impact. However, the method commonly used to evaluate the eco-efficiency – that is, the black-box data envelopment analysis (DEA) – cannot examine the inefficiencies of the internal structure, and as a result, the underlying management defects are unclear. To open the black box, this study presents a two-stage network DEA framework incorporating government and industrial sectors and measures the eco-efficiency of 84 resource-based cities during the post-financial crisis period (2007–2015). The results indicate that the average eco-efficiency of China's resource-based cities shows a promising increase, and there is a positive relationship between governance efficiency and production efficiency. The decreasing trend of governance efficiency in the Central, Western, and Northeast regions after 2014 shows the low quality of the government sector in the usage of fiscal income. Proactive disclosure of how the government sector conducts public business and spends taxpayers' money should be made to increase transparency, attract more entrepreneurial resources to carry out production activities, and further improve sustainability. The two-stage network DEA framework helps obtain more insights into the internal management defects of the government and industrial sectors and enhance their cooperation to improve the eco-efficiency precisely

Operational research:methods and applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Assessing Dynamic Efficiency of Machine-made Carpet Industry by Network DEA Technique

The results of dynamic efficiency evaluation not only help managers to realize their business' position in competitive market, but also enable them to compare current company’s performance with previous periods and do strategic planning properly. For doing so, network data envelopment analysis is a logical approach. Hence, the main objective of this illustration is to measure dynamic efficiency by means of network data envelopment analysis technique. Although different approaches in network DEA are introduced recently, the need for a comprehensive methodology in this area is remained because of the defects of previous methodologies. Consequently, a novel approach based on multi-objective optimization is introduced in this paper in order to measure the efficiency of a network structure. Finally, the case of Machine Made Carpet Industry (MMCI) is used and the dynamic performance of MMCI's companies in the period of four years is measured. Efficiency results of case data showed that the methodology proposed in this paper is able to eliminate defects of previous approaches and evaluate both total and annual efficiency simultaneously Introduction: Dynamic efficiency assessment is so crucial for managers to watch out their business performance by passing the time. In a competitive market, understanding whether the company is performing in an efficient manner or not, in comparison to their rivals, is so important for managers. In this regard, assessing dynamic efficiency is the objective of this research and Machine-made Carpet Industry (MMCI) is taken into account as the case study. So, the companies producing machine made carpets are considered as the Decision Making Units (DMUs). Therefore, the main purpose of this research is to assess the dynamic efficiency of MMCI’s companies during a four-year period. The methodology used for assessing dynamic efficiency is network data envelopment analysis.   Materials and Methods: The main purpose of this research is to assess the dynamic efficiency of MMCI’s companies during a four-year period by means of network data envelopment analysis technique. For doing so, five different approaches are used; while, four of this approaches include ‘Standard DEA approach, Separation approach, Average approach and Relational analysis approach’ are in the literature and the last approach, named as ‘Max-min approach’ is developed for the first time in this paper. All the first four approaches are used for assessing the efficiency of this research’s network structure and the disadvantages of all four approaches were highlighted by details. Finally, this paper introduces a multi-objective optimization method named as max-min approach for assessing total and partial efficiency of the network structure simultaneously. This new approach is able to eliminate the defeats of the previous ones and bring a comprehensive methodology for assessing the dynamic efficiency of DMUs. Results and Discussion: In this article, firstly, the weaknesses of the available methodologies in the literature for assessing the dynamic efficiency of a network structure by means of network data envelopment analysis are illustrated. Then, a new approach based on multi-objective optimization technique is proposed in order to assess dynamic efficiency of a four-stage network structure with extra inputs and outputs. In more details, this new approach has the ability to eliminate the defeats of the methodologies available in the literature which can briefly be named as the disability in measuring total and partial efficiency simultaneously, being biased in giving importance to some sub-processes, lack of discrimination and disability in assessing unique efficiency scores for sub-processes. This paper’s novel approach is named as max-min optimization approach and is able to assess the unique and unbiased efficiency scores in a network structure for both total and partial efficiency simultaneously. To be more accurate, the efficiency assessment which are obtained by the methodology of this paper is unique. In addition, decision makers’ point of view plays no role in giving the priority to any sub-process and all the stages have the same importance in measuring the efficiency of a network structure. Last but not the least is that, since these sub-processes are connected, efficiency assessment should be done in a manner that takes into account the role of intermediate parameters and this consideration is done appropriately in this paper.   Conclusion: In this paper, dynamic efficiency assessment of MMCI’s companies is measured by means of network data envelopment analysis. Since the approaches presented in literature have some weaknesses, this paper aims to develop a comprehensive network data envelopment analysis approach which is able to measure dynamic efficiency of DMUs in an appropriate manner. To do so, this research develops a novel methodology based on network data envelopment analysis. This approach is a multi-objective programming technique that measure total and partial efficiency of a network structure simultaneously in a unique and unbiased manner and is named as max-min approach. Finally, the max-min approach presented in this investigation is a proper methodology in assessing dynamic efficiency of a network structure in a period of time.   References Cook, W. D., Zhu, J., Bi, G., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122-1129. KAO, C. (2016). Efficiency decomposition and aggregation in network data envelopment analysis. European Journal of Operational Research, 255, 778-786. TONE, K. & TSUTSUI, M. )2014(. Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42, 124-131.