Dual-role Factors Research Articles

An improved game cross-efficiency approach with dual-role factors for measuring the efficiency of Chinese “985 project” universities

Evaluating the efficiency of universities is essential to gain sustained competitive advantage for regional/national development, especially from the long-run perspective. In this paper, we develop an improved game cross-efficiency (GCE) approach to assess Chinese “985 Project” universities considering dual-role factors. First, building on the concept of “joint technology” and the GCE approach, we provide a new approach for efficiency analysis with dual-role factors. In addition, we identify a potential inconsistency problem while searching the Nash equilibrium point from a computational perspective. Importantly, we propose an improved searching process that is consistent with the original GCE paradigm. Finally, we illustrate the proposed approach by applying it to “985 Project” universities in China. The findings validate the importance of adopting the proposed improved approach.

Evaluating Supply Chains Based on the Novel Network Data Envelopment Analysis Model

In today’s competitive world, organizations are required to respond to customers and their stakeholders whose goals are profit, cost reduction, quality improvement, and customer satisfaction. Therefore, it is vital to realize the ways for continuous quality improvement, customer satisfaction and organizational performance management. A performance measurement system should be designed to consider the chain specifications of the network and its interrelations. Data envelopment analysis (DEA) is broadly used to evaluate the relative performance of a set of manufacturing processes because their models do not require considering the precise production functions. In this paper, a new approach based on network DEA is developed to evaluate the performance of an organization’s supply chain. The proposed model considers different constraints simultaneously. The purpose of this research is to provide a new approach by effective network DEA in order to evaluate the supply chains with different assumptions. This model is input–output-oriented and has a target setting, considering dual-role factors and desirable/undesirable outputs as well as flexible intermediate measures to evaluate the efficiency in a more realistic situation. Finally, a case study in drink industry is reanalyzed relying on the proposed model, and the results are presented and discussed. The results show that the approach presented in this research by considering the targets for inputs and outputs, in addition to improving efficiency, can also calculate and report the distance between DMUs and their targets. Also, the proposed model by considering the dual-role factors, desirable and undesirable outputs, has provided a comprehensive method that with all of these assumptions can evaluate the network DMUs under more realistic conditions. So, through this model with the considered assumptions, not only the efficiency scores of the units can be calculated but also an analysis of how to improve the units is also obtained according to the decision makers’ opinion.

Sustainable Supply Chain Evaluation with Shared Resources and Shared Feedbacks: A Common Set of Weights Slack Based Model

Data envelopment analysis (DEA) has been recently employed for performance and efficiency evaluation of decision-making units (DMUs) with multiple inputs and outputs. Complex internal relations within a DMU required more accurate DEA models than existing classic DEA. In real world applications, shared resources, among stages of a supply chain, is of great interest for managers and decision makers. Unfair allotment of shared factors can render the assessment invalid. In this study, an innovative DEA model is formulated for efficiency evaluation of a supply chain with shared factors. In the first step, a linear DEA model is presented in the multiplier form, based on the slack-based measure for efficiency assessment, in order to obtain the optimal proportion of the shared resources and shared feedbacks which are dual-role factors are presented. Then, the aggregate efficiency is decomposed into its stage efficiencies. Next, the presented model is extended for deriving the Common Set of Weights (CSW) for efficiency evaluation of the entire chain and correspondence stages. The case of 20 sustainable supply chains in the oil industry is considered with the developed DEA approach.

A Modified-Range Directional Measure for Assessing the Sustainability of Suppliers by DEA/UTASTAR

Sustainability in the supply chain means considering environmental, social, and economic practices. Conventional data envelopment analysis (DEA) models deal with desirable, discretionary, and nonnegative data. However, there might be undesirable outputs, nondiscretionary factors, and negative data. On the other hand, some criteria can be considered as outputs and inputs. These factors are named as the dual-role criteria. The objective of this paper is to develop a non-radial DEA model for dealing with negative data in the presence of undesirable, non-discretionary, and dual-role factors in weight restrictions context. The ordinal regression method, UTASTAR, is performed to define priorities in terms of criteria. The capabilities of the proposed method are compared with other methods. A case study is presented in which the best sustainable suppliers of SAPCO are selected. To check the importance of dual-role variables, two extra cases are considered.

Weight restrictions for the DEA model in the presence of dual-role factors: An application to the Iranian banking sector

A production process transforming multiple inputs to different outputs is considered in conventional data envelopment analysis (DEA) models. In various settings, however, there are factors that simultaneously play the roles of both input and output called dual-role factors. In some situations, additional information is available to impose on a DEA model with dual-role factors, or the decision maker is forced to impose some restrictions regarding the importance of dual-role factors on the model. Toward this end, the current research employs two different weighting methods to introduce various weighted DEA models in the presence of dual-role factors. To strengthen the accuracy of the new models, their properties are discussed. Then, each new model is illustrated in details by a numerical example. Moreover, to show that the new models are applicable, they are applied to the Iranian banking sector. To do this, 20 bank branches which have dual-role factors are assessed. At last, to show the outcome of weight restrictions, the results obtained by each new model are compared with those from Cook and Zhu’s model [Eur. J. Oper. Res. 180 (2007) 692–699].

A DEA model with dual-role factors and fuzzy data for selecting third-party reverse logistics provider, case study: Hospital waste collection

Today, most institutions and companies delegate reverse logistics services, organizational communication, and outsourcing company to a third-party logistics reverse provider. To select an appropriate third-party logistics provider, this study proposes a decision-making procedure considering dual role data and fuzzy states based on data envelopment analysis models. After determining evaluation indices, this study uses the α -cut approach to transform the proposed model under uncertainty to a deterministic model. Finally, a case study (collection and burial of hospital waste) is presented to show the application of this research. Results of selecting the best third-party logistics provider and more analyses are provided after coding in GAMS software.

An epsilon-based data envelopment analysis approach for solving performance measurement problems with interval and ordinal dual-role factors

An epsilon-based data envelopment analysis approach for solving performance measurement problems with interval and ordinal dual-role factors

On performance evaluation with a dual-role factor

On performance evaluation with a dual-role factor

An interval efficiency analysis with dual-role factors

Data envelopment analysis (DEA) is a data-driven and benchmarking tool for evaluating the relative efficiency of production units with multiple outputs and inputs. Conventional DEA models are based on a production system by converting inputs to outputs using input-transformation-output processes. However, in some situations, it is inescapable to think of some assessment factors, referred to as dual-role factors, which can play simultaneously input and output roles in DEA. The observed data are often assumed to be precise although it needs to consider uncertainty as an inherent part of most real-world applications. Dealing with imprecise data is a perpetual challenge in DEA that can be treated by presenting the interval data. This paper develops an imprecise DEA approach with dual-role factors based on revised production possibility sets. The resulting models are a pair of mixed binary linear programming problems that yield the possible relative efficiencies in the form of intervals. In addition, a procedure is presented to assign the optimal designation to a dual-role factor and specify whether the dual-role factor is a nondiscretionary input or output. Given the interval efficiencies, the production units are categorized into the efficient and inefficient sets. Beyond the dichotomized classification, a practical ranking approach is also adopted to achieve incremental discrimination through evaluation analysis. Finally, an application to third-party reverse logistics providers is studied to illustrate the efficacy and applicability of the proposed approach.

Efficiency measurement to identify the best efficient unit in the presence of dual-role factors

Efficiency measurement is crucial to management for performance evaluation of several decision-making units (DMUs) in order to find the most efficient unit. Data envelopment analysis (DEA) is a practical and popular method that has been used widely for efficiency measurement in many applications. Thus, in the literature of DEA, several mixed integer DEA models are developed to efficiency measure and find the best efficient unit. In some situations, some factors named dual-role factors may be simultaneously considered as both inputs and outputs. However, a little attention has been paid in the literature of DEA to these types of factors. The current paper develops a new mixed integer linear DEA model to identify the best efficient DMU in the presence of dual-role factors. The basic DEA models consider different roles for each dual-role factor, and also solve at least one model for each DMU to determine the best unit. However, the developed approach in this paper considers a unique role for each dual-role factor, and also determines the best unit through by solving just one mixed integer DEA model. Two numerical examples are used to illustrate the profits and applicability of the developed approach.

A DEA model to sustainability improvement of the electricity supply chain in presence dual-role factors and undesirable outputs: A case on the power industry

The energy and industrial sectors are the most attractive investment regions for enhancing efficiency in production processes. The power industry is one of the important investment targets for enhancing corporate sustainability. One of the most fundamental problems in the power industry is the control of wasted energy in oil and gas fields and power plant sectors and the power losses management in transmission and distribution lines. The investment to new technology innovation and environmental protection from pollution gases emission in energy and power plant sectors and the power losses management in transmission and distribution lines play an important role in the implementation progress of the power industry. The purpose of this study is to examine the effects of investment to flare gas and greenhouses gases reduction in energy and power plant sections and power losses control by equipping sections to improved engineering systems in transmission and distribution networks of the electricity supply chain. Indeed, the supply chain management needs information related to investment effect to activity level control as handling flare gas in energy sections and reducing harmful substance emissions and greenhouses gases in power plant sectors and harnessing power losses in transmission and distribution networks. The proposed approach evaluates the sustainability and efficiency of an electricity supply chain by a radial model in the presence of two categories of inputs under natural and managerial disposability, dual-role factors and undesirable produces. A real case on the Iran power industry is presented to demonstrate the applicability and practicability of the proposed method. Moreover, to demonstrate the capability of the proposed approach a supply chain identified by oil and gas companies, power plants, transmissions companies, dispatching companies and final consumers in the Iran power industry. One empirical implication has obtained from model performance in the electricity supply chain. The results indicate approximately, the oil and gas fields, the power plants and the distribution lines and the public divisions of power consumers have earned 100%, 90% and 90% efficiency of the total in supply chains, respectively. Particularly, this study recommends that transmission and distribution companies must have adequate decisional capacities regarding investment for transmitting power to industrial, agriculture divisions in the power industry.

A two-stage model for monitoring the green supplier performance considering dual-role and undesirable factors

PurposeThe purpose of this paper is to monitor the environmental efficiency of suppliers in the presence of undesirable output and dual-role factors with static and dynamic aspects. Meanwhile, it also aims to explain the main reason for the low efficiency of suppliers.Design/methodology/approachThe authors propose a modified data model considering undesirable output and dual-role factors. The study integrates the modified data envelopment analysis model into the distance function of the Malmquist–Luenberger index. Moreover, this study uses the global benchmark technology to formulate a two-stage model. To verify the validity of this model, a model application is conducted on an automotive spare components company in China.FindingsThe results identify the unique status of dual-role factors based on the global optimality of the model and then categorize inefficient suppliers in an individual evaluation cycle. In addition, each supplier is projected on a frontier curve after obtaining the improved data. Furthermore, through the status plot of M-L and its components, this paper concludes that efficiency scale change is the main reason for the gap in ecological performance between different suppliers.Research limitations/implicationsThe proposed model considers both undesirable output and dual-role factors; however, variables with different features, such as imprecise, fuzzy and qualitative characteristics, can be embedded into the presented two-stage model.Originality/valueEvaluating green suppliers through multiple consecutive evaluation cycles will aid a company in effectively managing its key suppliers. Furthermore, the evaluation provides policy guidance for further improvement of suppliers.

Efficiency measurement to identify the best efficient unit in the presence of dual-role factors

Efficiency measurement is crucial to management for performance evaluation of several decision-making units (DMUs) in order to find the most efficient unit. Data envelopment analysis (DEA) is a practical and popular method that has been used widely for efficiency measurement in many applications. Thus, in the literature of DEA, several mixed integer DEA models are developed to efficiency measure and find the best efficient unit. In some situations, some factors named dual-role factors may be simultaneously considered as both inputs and outputs. However, a little attention has been paid in the literature of DEA to these types of factors. The current paper develops a new mixed integer linear DEA model to identify the best efficient DMU in the presence of dual-role factors. The basic DEA models consider different roles for each dual-role factor, and also solve at least one model for each DMU to determine the best unit. However, the developed approach in this paper considers a unique role for each dual-role factor, and also determines the best unit through by solving just one mixed integer DEA model. Two numerical examples are used to illustrate the profits and applicability of the developed approach.

Evaluation of the allocation performance in a fashion retail chain using data envelopment analysis

Efficiency is one of the most important criteria of performance evaluation in any supply chain management, especially in the fashion retail industry. In the fashion industry, products are characterized by short life cycles and demand uncertainty. Most fast-fashion companies have employed the allocation practice that includes initial allocation and multireplenishment to capture the latest market information. Previous studies focus more on optimizing allocation policies, but overlook the efficiency issue, and the models always tend to be complex and are difficult to understand or apply. In this study, we model the allocation process as a multi-stage system with multiple inputs and outputs. A time-based dynamic network Data Envelopment Analysis (DEA) model, called multi-stage efficiency model (MEM), is developed to evaluate the allocation performance. The MEM considers undesirable outputs, dual-role factors (inventory as an output at the end of previous stage and the input at the beginning of the next stage) and the inconsistent attributes of the dual-role factors among the multi-stages. Meanwhile, the traditional DEA model is introduced to demonstrate the MEM is essential to capture the allocation performance. Based on the MEM results, the more appropriate initial allocation strategy is identified and the factors affecting allocation performance are discussed. The model is applied to a major Canadian fast-fashion company to validate the effectiveness. This article not only provides valuable managerial insights of supply chain management for fashion companies, but also makes contributions to the industrial application of theoretical OR (operation research) models.

Sustainability evaluation of the supply chain with undesired outputs and dual-role factors based on double frontier network DEA

The performance analysis of supply chain is a very complicated problem. The network DEA can be used to calculate efficiency of decision-making units with multiple stages. In this paper, a new network DEA model is proposed. This model includes undesired outputs and dual-role factors. It can calculate optimistic and pessimistic efficiency. The supply chain can be ranked in terms of the overall efficiency scores. A case study is presented to demonstrate the applicability of our model.

Directions for Social Enterprise from an Efficiency Perspective

Social enterprise is recognized as an alternative for sustainable development, as it balances social aspects with economic prosperity. Evaluating social enterprises is very important for both the enterprises themselves and the government, since grants from the government or institutions highly depend on their performance. While relatively significant attention is paid to the social value that these enterprises create, there is a lack of interest in assessing the operational performance directly linked to the sustainable operation of social enterprises. Therefore, this research analyzes the performance of social enterprises from the efficiency perspective, incorporating both operational (economic) and social performance measures. To this end, we apply data envelopment analysis to assess the performance of social enterprises when considering the dual-role factor—the grants. To facilitate clarity for readers, a dataset of Korean social enterprises is used. Through this analysis, we show that the grants can be used for performance evaluation in different ways for each enterprise. Furthermore, an industry-specific analysis provides more realistic and feasible benchmarking information to which inefficient social enterprises should refer. We expect that these findings will complement existing methods of social enterprise evaluation.

How to evaluate sustainability of supply chains? A dynamic network DEA approach

PurposeThe purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score.Design/methodology/approachA dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach.FindingsThis paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately.Research limitations/implicationsIn real world, managers face with big data. Therefore, we need to develop an approach to deal with big data.Practical implicationsThe proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc.Originality/valueFor the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

On Performance Evaluation with a Dual-Role Factor

This paper provides rigorous analytical discussions of the dual-role factor classification problem in Data Envelopment Analysis (DEA). We study two approaches taking opposite directions: one (the conventional and popular approach) clarifies the input/output roles prior to analysis, while the other incorporates the dual roles into the analysis. We show that both approaches are necessary and related. The former requires modification and the latter is a special empirical implication of the former after modifying. Two pitfalls are discussed, i.e., providing underestimated efficiency and a benchmark contradicting the role classification. Finding pitfalls in the form of observing slacks in the empirical implementation applying the standard DEA models with pre-classified roles, we suggest that a Pareto-Koopmans efficiency measure can help avoid unmeaningful outcomes even without considering the interaction among the dual-role factor and other inputs and outputs.

How to Assess Sustainability of Suppliers in the Presence of Dual-Role Factor and Volume Discounts? A Data Envelopment Analysis Approach

Sustainability factors play critical role for long-term achievement of a supply chain management and purchasing process becomes more complicated with social and environmental pressures. Managing supplier selection process is a necessary step for companies seeking to manage their corporate legitimacy and reputations. Data envelopment analysis (DEA) has been widely used for supplier selection problems. In this paper, we propose a new super-efficiency method for evaluating sustainability of suppliers in the presence of dual-role factors and volume discounts. We show that enhanced Russell model (ERM) fails to present a complete ranking of suppliers. Our new model presents a complete ranking and also preserves properties of the ERM. Capabilities of our proposed method are shown using a couple of examples. A case study is presented to illustrate our proposed approach. The proposed method is used to select the best sustainable suppliers.

Dual-role factors for imprecise data envelopment analysis

Efficiency analyses are crucial to managerial competency for evaluating the degree to which resources are consumed in the production process of gaining desired services or products. Among the vast available literature on performance analysis, Data Envelopment Analysis (DEA) has become a popular and practical approach for assessing the relative efficiency of Decision-Making Units (DMUs) which employ multiple inputs to produce multiple outputs. However, in addition to inputs and outputs, some situations might include certain factors to simultaneously play the role of both inputs and outputs. Contrary to conventional DEA models which account for precise values for inputs, outputs and dual-role factors, we develop a methodology for quantitatively handling imprecision and uncertainty where a degree of imprecision is not trivial to be ignored in efficiency analysis. In this regard, we first construct a pair of interval DEA models based on the pessimistic and optimistic standpoints to measure the interval efficiencies where some or all observed inputs, outputs and dual-role factors are assumed to be characterized by interval measures. The optimal multipliers associated with the dual-role factors are then used to determine whether a factor is designated as an output, an input, or is in equilibrium even though the status of the dual-role factors may not be unique based upon the pessimistic and optimistic standpoints. To deal with the problem, we present a new model which integrates both pessimistic and optimistic models. The integrated model enables us to identify a unique status of each imprecise dual-role factor as well as to develop a structure for calculating an optimal reallocation model of each dual-role factor among the DMUs. As another method to investigate the role for dual-role factors, we introduce a fuzzy decision-making model which evaluates all DMUs simultaneously. We finally present an application to a data set of 20 banks to showcase the applicability and efficacy of the proposed procedures and algorithm.