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A novel network data envelopment analysis model for evaluating green supply chain management
Abstract
Green supply chain management (GSCM) has become a method to improve environmental performance. Under stakeholder pressures, forces and regulations, companies need to improve the GSCM practice, which are effected by practices such as green purchasing, green design, product recovery, and collaboration with patrons and suppliers. As companies promote the GSCM, their economic performance and environmental performance will be enhanced. Hence, GSCM evaluation is very important for any company. One of the techniques that can be used for evaluating GSCM is data envelopment analysis (DEA). Traditional models of data envelopment analysis (DEA) are based upon thinking about production as a “black box”. One of the drawbacks of these models is to omit linking activities. The objective of this paper is to propose a novel network DEA model for evaluating the GSCM in the presence of dual-role factors, undesirable outputs, and fuzzy data. A case study demonstrates the application of the proposed model. A case study demonstrates the applicability of the proposed model.
... Data envelopment analysis (DEA) is a nonparametric mathematical tool for assessing the relative efficiency of homogeneous decision making units (DMUs) (Mirhedayatian et al., 2014). The first DEA model under the assumption of constant returns to scale (CRS), known as the Charnes, Cooper and Rhoades (CCR) model, was developed by Charnes et al. (1978). ...
... Decision-makers aim for minimal emissions, hence it is generally considered as an undesirable output. Mirhedayatian et al. (2014) treated the CO2 emission and Parts per million (PPM) as the undesirable outputs. However, existing DEA methods rarely consider impact of dual-role factors and undesirable outputs while assessing production efficiency, which affects the accuracy of efficiency assessment. ...
... Slack based measure is widely utilised in DEA. For example, Chen et al. (2019) and Mirhedayatian et al. (2014) employed this method to deal with undesirable outputs. In production, decisionmakers always prefer fewer undesirable outputs. ...
... For instance, Chen et al. [28] employed a RAM model with undesirable outputs to measure the performances of the truck restriction policy, and the undesirable outputs were coped with the SBM. Mirhedayatian et al. [29] presented a network SBM model in the presence of fuzzy data and undesirable outputs. Chen et al. [30] introduced a unified BAM considering undesirable outputs, which was handled by the SBM method. ...
The acts of assessing the efficiency of agricultural and pastoral systems and improving their production levels have profound implications for the sustainable development of the agricultural economy. Agricultural and pastoral systems are composed of agricultural sub-systems and pastoral sub-systems, which encompass both the production stage and the sales stage. These two sub-systems include shared factors and undesirable outputs, the latter of which refer to by-products such as CO2 emissions, among others. These factors create significant challenges in assessing the efficiency of agricultural and pastoral systems. To address this issue, this study first proposes divisional system network bounded adjusted measure (BAM) models that consider shared factors and undesirable outputs for assessing the efficiency of agricultural sub-systems and pastoral sub-systems. Subsequently, an overall efficiency model for evaluating the efficiency of agricultural and pastoral systems is developed. The new method is applied to evaluate the efficiency of agricultural and pastoral systems across 30 provinces and cities in China. To explore the impact of undesirable outputs, the efficiency that ignores undesirable outputs is compared with our method. The results indicate that efficiency may be misestimated when ignoring undesirable outputs. Additionally, efficiency under different conditions of intermediate products is also computed, revealing that efficiency under the fixed link of intermediate products tends to be overestimated compared to the free link method we used.
... On the other hand, traditional DEA models consider the DMUs as black boxes (Mirhedayatian et al., 2014). However, many production and service systems (DMUs) deal with network structures. ...
In recent years, we have witnessed many unforeseen disasters. During disruptive events, governments and business organizations call for more effective and efficient humanitarian operations. Humanitarian supply chains (HSCs) play a vital role in helping businesses operate smoothly in such challenging situations. Simultaneously, the performance measurement of HSCs plays a strategic and key role in responding to unpredicted events. Despite the importance of measuring performance in HSCs, there are significant challenges with existing approaches in the literature. The objective of this paper is to develop a novel network data envelopment analysis (NDEA) model with unique features. The proposed model can calculate division and overall efficiency scores with high discriminatory power. Furthermore, the proposed NDEA model simultaneously takes into account integer data, ratio data, undesirable outputs, and fuzzy data. The model also addresses interval undesirable outputs. The proposed model demonstrates the capability to accurately rank HSCs. Additionally, a case study of Iranian natural disasters is provided to showcase the capabilities of the proposed model. Finally, the results of the numerical analysis are utilized to derive managerial implications for HSCs.
... Lozano and Moreno (2014) adapted a variety of fuzzy DEA methods to the network DEA framework for managing fuzzy data where the DMUs are organized by a network framework. For assessing organizations in green supply chain management (GSCM), a novel network DEA approach that includes dual-role elements, fuzzy data, and undesirable outputs was presented by Mirhedayatian et al. (2014). Furthermore, for supply chain assessment investigation, a fuzzy supply chain DEA by integrating fuzzy intermediate measures within a triangular fuzzy membership function was developed by Xia et al. (2014). ...
Uncertainty is a significant context to investigate when assessing entities. In the presence of imprecise and vague data, this study presents a novel method for evaluating the decision-making units (DMUs) performance utilizing a network structure consisting of two stages. Thus, for presenting the fuzzy network data envelopment analysis (FNDEA) model, two-stage data envelopment analysis (TSDEA), chance constraint programming (CCP), and possibilistic programming are utilized. Furthermore, the possibilistic network data envelopment analysis (PNDEA) method could be utilized under various returns to scale (RTS) presumptions. For measuring the investment firms’ (IFs) performance using a two-stage structure containing portfolio and operational management procedures, the developed fuzzy network DEA model is implemented. In addition, IFs like mutual funds (MFs) and investment organizations are extremely significant organizations to make investments in capital markets. Consequently, assessing the related performance in determining effective investment firms and proposing an appropriate solution for inefficient IFs is important. Finally, a real-world case study of the Tehran Stock Exchange is used, and findings indicate that the developed model is effective.
... Another strand of the literature has focused on the performance evaluation of sustainable supply chains (Chen & Yan, 2011;Gupta et al., 2021;Sun et al., 2017Sun et al., , 2020aYou & Jie, 2016). A plethora of studies belonging to this category have used data envelopment analysis (DEA) to measure supply chain efficiency (Cook et al., 2017;Hahn et al., 2021;Mirhedayatian et al., 2014;Soheilirad et al., 2017;Song et al., 2018;Wu & Olson, 2008;Xu et al., 2009). In addition to evaluating the management of supply chains, these papers study possible trajectories for their improvement. ...
China’s electric utility industry is among the country’s main polluters. Increasing the efficiency of China’s power supply chain is therefore essential to promote more environmentally sustainable power generation. This paper assesses the performance of China’s regional power supply chains that include electric utilities and power grid companies. We rely on the notion of sustainable supply chains to propose a multi-stage model that accounts for negative externalities such as harmful emissions, power transmission line loss, and increased mortality caused by pollution. Different from the existing studies of power supply chains, our approach is based on the exponential directional distance function defined with respect to a multiplicative production technology that can help mitigate the impact of data outliers. We use a panel of 30 Chinese provinces covering the period 2008–2018 to measure the efficiency improvement potential within regional supply chains and assess the efficiency-enhancing role of digital innovation. Our results provide evidence of considerable regional disparities and suggest that China’s eastern provinces outperform other regions due to the relatively advanced state of their technology. Furthermore, we demonstrate that the efficiency improvement potential from better management or technological innovation remains significant across the majority of provinces. Finally, our results provide evidence of a significant role of digitalization in promoting the efficiency of power supply chains. Our findings offer important perspectives on the strategies policymakers can use to promote sustainable performance of electric utilities and power grid companies.
... NDEA, besides, has been a widely applied tool for assessing the performance of SCs. To evaluate green SCs, a network DEA model was proposed by Mirhedayatian et al. (2014). Tavassoli et al. (2015) proposed a network DEA technique to measure the efficiency of SCs, considering zero data. ...
The concept of the circular economy (CE) proposes eco-friendly principles for dealing with circularity problems in production systems and supply chains (SCs). To achieve circularity goals in SCs, CE principles assist decision-makers with reusing, remanufacturing, and recycling initiatives in the production process. However, previous meso- and macro-level circularity investigations reveal shortcomings in practical closed-loop solutions. This study aims to develop a closed-loop framework for assessing the circularity of sustainable SCs using network data envelopment analysis (NDEA). The closed-loop framework is created utilizing real-life indices of sustainable SCs, such as "recyclable undesirable outputs". To make the appraisal more realistic and enhance the validity and reliability of the results, the circularity results of SCs are assessed under data uncertainty. This allows SCs to be ranked based on their real circularity level, identifying sustainable and unsustainable SCs. The proposed framework offers decision-makers a practical evaluation tool to identify the highly circular SCs and establish circularity benchmarks for inefficient SCs. The proposed approach is scalable and applicable to real-world circularity evaluations in multistage SCs and production systems. The study concludes with a two-stage case study to show the practicality of the new framework.
... Tavana et al. (6), introducing a network model founded on the Network Epsilon-Based Measure (NEBM), have investigated supply chain performance, simultaneously analyzing changes in inputs and outputs, both radially and non-radially within the network. In the realm of Green Supply Chain Management, Mirhedayatian et al. (7) have positioned it as a method to enhance environmental performance, asserting that companies, influenced by stakeholders, pressures, and regulations, must enhance the performance of Green Supply Chain Management (GSCM). Shafiee et al. (8), after an extensive examination of various tools for evaluating supply chain performance, have proposed a novel approach based on network DEA and the Balanced Score card (BSC) method. ...
... Data Envelopment Analysis (DEA) is a method that is widely used to calculate the efficiency score of the Decision-Making Unit (DMU) (Kahi et al., 2017;Khodakarami et al., 2015;Shabani and Saen, 2015;Dania et al., 2019). One of the main goals of the DEA is to provide benchmarks for inefficient DMUs, with the implication that these benchmarks serve as targets to be achieved by DMUs (Mirhedayatian et al., 2014). In the standard DEA model, the inefficient DMU benchmark uses only historical data, so it does not consider future planning . ...
Supply chain benchmarking of agroindustry can be done by emphasizing the perspective of sustainability. This paper aimed to analyze the efficiency of Sustainable Supply Chain Management (SSCM) in Micro, Small, and Medium Enterprises (MSMEs) and provided a prospective benchmark with the potato chips industry as a study case. Program Evaluation and Review Technique (PERT) estimated future input and output values to obtain prospective benchmarks and be added to the DEA formula later. Analytical Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) methods were used to measure SSCM performance. The results showed that 17 of 20 Decision-Making Units (DMUs) were efficient (score 1). The other 3 DMUs were classified and got an efficiency score of 0.965, 0.955, and 0.995. In future estimates calculation, the efficiency score of one of the inefficient DMUs has changed to 1, while the other two got the scores of 0.996 and 0.980. The limitation of this research mainly comes from the scope of assessment, which is limited to the supply chain’s downstream sector and assesses only a limited number of MSMEs in a particular region. SSCM efficiency measurement variables were adjusted to be assessed and applied to MSMEs. PERT was also beneficial to gain future estimates of the assessment scope.
... Based on different studies by Zhou et al. (2018), Liu et al. (2013) and Liu et al. (2016) and in addition to the traditional DEA model (Mousavi-Avval et al., 2011;Moutinho et al., 2017;Cheng et al., 2018;Gong & Chen, 2017;Kang & Lee, 2016), many other DEA methods can be applied to sustainability research. These methods include the SBM and Dynamic DEA models (Tsai et al., 1230;Chang et al., 2015;Wang & Feng, 2015;Song & Zheng, 2016;Chen & Jia, 2017;Iftikhar et al., 2016;Woo et al., 2015;Xie et al., 2014;Geng et al., 2021), Extending models (Wey, 2015;Kumar et al., 2014;Mirhedayatian et al., 2014;Lee & Saen, 2012;Li & Lin, 2015), Two-stage contextual factor evaluation framework (Assaf et al., 2012;Chen et al., 2014;Gadanakis et al., 2015;Picazo-Tadeo et al., 2011), two-stage Network DEA (Yan et al., 2017;Wu et al., 2017;Bostian et al., 2016;Sarkhosh-Sara et al., 2020;Qorri et al., 2018;Ramezankhani et al., 2018;Iftikhar et al., 2018), as well as Models handling particular types of data, such as fuzzy (Azadi et al., 2015;Song et al., 2021), ordinal (Chen & Delmas, 2011), qualitative (Zeydan et al., 2011), negative data and so on (DiMaria, 2014). Accordingly, Traditional DEA models (e.g., CCR and BCC models) are the most popular ones (Zhou et al., 2018). ...
Several countries have focused on achieving optimal economic growth in the past decades, which has caused diverse environmental concerns among policymakers and managers. The present study aims to introduce new metric using mathematical modeling to examine strategies to promote environmental performance in OECD countries. Thus, we propose a target-oriented Distance Friction Minimization (TO-DFM) model to design a practical improvement strategy for low-efficiency DMUs. In this regard, considering the efficient and inefficient frontiers, we present two models, IDMU and ADMU, respectively, based on TO-DFM and DFM models. Then, the distance scores yielded by the two models are combined to evaluate the efficiency of each DMU using the hybrid index of relative closeness (RC). The results reveal that the proposed model has a better distinctive potential compared to the standard TO-DFM model. Overall, implementing the proposed model on a set of sustainability data, for OECD countries, resulted in a more logical performance in classifying countries regarding their distance from the efficient and inefficient frontiers. Therefore, less developed countries should pay considerable attention to implementing environmental policies to improve their performance and control environmental pollution with a reliable and healthy economy. For instance, the implementation of such policies in countries such as Turkey, Estonia, and Hungary, with lower efficiency scores, can affect the sustainability of their development performance.
... Green et al.'s (2012) research focuses on, it was discovered that GSCM and corporate performance have a positive and significant relationship. Additionally, a number of earlier research (Younis & Sundarakani, 2019) demonstrated that improving business performance through the application of GSCM (Dubey et al., 2017 andMirhedayatian et al., 2014). H1: GSCM has a positive effect on corporate performance. ...
This research aims to determine if employee satisfaction mitigates the detrimental effects that organizational culture, leadership, and GSCM have on corporate performance. For this purposeful convenience sampling, the research population is an industrial and commercial firm in Indonesia. The primary source of the data used is from surveys that were gathered using Google Forms. The sample for the study consisted of 102 people. The statistical analysis in this study used structural equation modeling using partial least squares. This study expands the GSCM's set of indicators. The study's findings demonstrate that GSCM, leadership, and organizational culture all have an impact on business performance. But only the impact of organizational culture—not the impact of GSCM or leadership—on performance is tempered by employee satisfaction. The study includes ramifications for manufacturing and trading companies, such as the requirement for a strong organizational culture, strong leadership, and the application of a GSCM strategy. The research tools or GSCM indicators are what set this study apart from others. The amount of respondents to the survey is one of this study's shortcomings, hence it is recommended that future research use more samples. Keywords: Organizational culture, leadership, green supply chain management, and employee satisfaction
... They combined the linguistic preferences using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate an overall performance score for each vendor. Mirhedayatian et al. (2014) developed an NDEA for assessing green SCs. They integrated dual-role factors, bad outputs, and fuzzy sets. ...
... The amount of energy resources consumed "1" (highest) "2" (medium) "3" (lowest) Mirhedayatian et al. (2014) ...
Product deletion is a strategic organizational decision. Using multiple literature streams, we identify antecedents and relationships of product deletion across four functional areas—marketing, supply chain, finance, and sustainability. Using a field data sample from eight organizations, we apply Bayesian analysis to identify various relationships between product deletion across various functional performance measures. We complete additional simulation analyses using two dependent variable distribution methods (balanced and unbalanced) and validate using parametric logistic regression methodology for robustness checks. The results show predictive relationships exist amongst the functional performance measures and product deletion. High performance across organizational functions results in decreased odds of product deletion; but not as large a margin as expected. If improved product performance is identified across all functions especially cost dimensions, the likelihood of product deletion decreases. Amongst these functions, surprisingly supply chain performance is a better indicator of a product's candidacy for deletion. The integrative findings are exploratory but provide insights for further research development and practical implementation.
... decomposition are influencing firms to implement green strategies such as GSCM (Omar et al., 2019;Abbass et al., 2022). Green supply chain practices are not optional; it is necessary for firms to gain a competitive advantage in the business world (Mirhedayatian et al., 2014). According to a recent Mckinsey report, firms supply chain operations are causing 90% environmental deterioration (Herrmann et al., 2021). ...
Purpose
This paper aims to investigate the impact of green human capital (GHC) on green supply chain management (GSCM) practices (environmental education and internal environmental management) and sustainable supply chain performance.
Design/methodology/approach
This study used a survey method to collect data from 350 randomly selected manufacturing firms in China, including supply chain and human resource professionals from the period of December 2021 to June 2022.
Findings
The partial least squares-structural equation modeling version 4 is applied to assess the data and test the hypotheses. Under the notion of resource-based view theory, the findings demonstrate that GHC has a positive and significant relationship with GSCM practices (environmental education and internal environmental management) and that GSCM practices are positively associated with the sustainable supply chain performance.
Practical implications
This study offers implications for Chinese manufacturing firms to use GHC on dimensions of GSCM implementations for achieving environmental, financial and social performance.
Originality/value
This study finds that GHC as a critical enabler for implementing GSCM practices, resulting in more robust and better sustainable supply chain performance (environmental, financial and social performance).
... However, since CCUS technology has not realized full application, CO 2 emission reduction is still highly related to controlling carbon-generating input use like coal. Considering this, several studies have proved that it is essential to divide the production system into a production stage with carbon emission and a pollution abatement stage with end-ofpipe treatment [50][51][52][53][54][55][56][57][58][59][60][61]. Previous studies related to CFPPs would treat an undesirable output like SO 2 or CO 2 emissions or an actual energy input as a link variable between production and abatement stages [50][51][52], and assumed that they satisfied a common weak disposability feature, meaning that if the undesired output decreases, the other output will necessarily decrease by some amount [62]. ...
Marginal abatement cost (MAC) plays an essential role in pricing pollutants and guiding environmental policies. Considering the heavy polluting nature of China’s coal power industry, this paper aims at providing companies and policymakers with more comprehensive information on the cost of abatement by estimating the MACs of CO2 and SO2 for coal-fired power plants (CFPPs) in China. This study contributes to the literature by considering an interconnected two-stage production system to investigate. The estimation framework is advanced in combining the electricity production and pollution abatement process of CFPPs into a convex quantile regression (CQR) model. The results show that the averages of MAC for CO2 and SO2 are estimated to be 367.56 Yuan/ton and 662.30 Yuan/ton, respectively, indicating that the reduction of such emissions is still costly. The heterogeneous analysis then indicates that large CFPPs, central-government-owned power plants (CGOPPs), and low-regulated CFPPs tend to possess lower MACs for CO2. Regarding SO2, large and medium-sized power plants show significantly larger MACs than small plants. In addition, the MACs of SO2 for CGOPPs and high-regulated CFPPs are more concentrated at high levels. In the second part, the Tobit regression analysis was used to discuss the determinants of MACs for CO2 and SO2. Factors like carbon emission intensity, load, and operating hours can notably decrease MACs for CO2, while MACs for SO2 tend to be positively affected by the total abatement cost and the abatement rate of the FGD equipment. In addition, the MACs for the large CFPPs, CGOPPs, and high-regulated CFPPs are more likely to be affected by the selected influence factors. Based on these results, we conclude with some policy recommendations.
... do not have enough information and knowledge to adopt green practices for eco-sustainability because of less idea about the environmental benefits of implementing a green conceptMirhedayatian et al. (2014);Luthra et al. (2016);Wang et al. (2016);Mangla et al. (2017) Lack of awareness and knowledge about reverse logistics practices (MB52) This sub-barrier shows that the industries do not know about reverse logistic practices, which means the reuse or recycling of products for economic benefits; thus, the industries are unaware of this practice.Govindan et al. (2014); Luthra et al. (2016); Wang et al. (2016); Mangla et al. (2017); Moktadir et al.(2018)Complexity in recollecting used products (MB53) This sub-barrier shows that industries face complexity in recollecting used products; thus, it is not easy for industries to find third parties to recollect used productsGovindan et al. (2014); Luthra et al. (2016); Wang et al. (2016); Mangla et al. (2017) Lack of green system exposure (MB54) The industries lack green system exposure in both quality and quantity to pursue sustainable goals Luthra et al. (2016); Wang et al. (2016); Mangla et al. (2017); Moktadir et al. (2018) Managerial barriers (MB6) Lack of involvement of top management (MB61) This is a very important sub-barrier that indicates the importance and involvement of top-level management in changing existing policies, information systems, and investment procedures to transform the GSCM system. However, moving to green manufacturing practices is disregarded by top management. ...
The industries view green supply chain management (GSCM) as a viable means of achieving sustainable operations by reducing environmental impact and enhancing operational performance. Although conventional supply chains still dominate many industries, integrating eco-friendly practices through green supply chain management (GSCM) is crucial. Nonetheless, there are several barriers that hinder the successful adoption of GSCM practices. Therefore, this study proposes fuzzy-based multi-criteria decision-making approaches comprised of the Analytical Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). The study evaluates and overcomes barriers to the adoption of GSCM practices in the textile manufacturing sector of Pakistan. After the comprehensive literature review, this study identifies 6 barriers, 24 sub-barriers, and 10 strategies. The FAHP method employs to analyze the barriers and sub-barriers. Then, the FTOPSIS method ranks the strategies to overcome various identified barriers. Based on the FAHP results, the most significant barriers to the adoption of GSCM practices are technological (MB4), financial (MB1), and information and knowledge (MB5). Further, the FTOPSIS indicates that increasing the research and development capacity (GS4) is the most vital strategy for implementing GSCM. The study’s findings have important implications for policymakers, organizations, and other stakeholders interested in promoting sustainable development and implementing GSCM practices in Pakistan.
... A DMU is deemed efficient if it lies on the boundary; otherwise, it is deemed inefficient. As Kao & Hwang (2010) discuss, in the last few decades many DEA models have been developed and applied, e.g., business performance measurement (Serrano-Cinca et al., 2005), decision-making performance with group decision support systems (Barkhi & Kao, 2010), evaluating supply chain management (Toloo, 2014;Toloo & Barat, 2015;Mirhedayatian et al., 2014), efficiency evaluation of sustainable suppliers (Azadi et al., 2015), technology selection (Wu et al., 2016a), allocation of emission reduction tasks (Wu et al., 2016b), measuring the performance of humanitarian supply chains (Izadikhah et al., 2019, and measuring the impact of enterprise integration on firm performance (Fazlollahi & Franke, 2018). However, the conventional DEA models only consider inputs and outputs; the operations of the internal components are ignored when measuring efficiency. ...
The rapid growth of advanced technologies such as cloud computing in the Industry 4.0 era has provided numerous advantages. Cloud computing is one of the most significant technologies of Industry 4.0 for sustainable development. Numerous providers have developed various new services, which have become a crucial ingredient of information systems in many organizations. One of the challenges for cloud computing customers is evaluating potential providers. To date, considerable research has been undertaken to solve the problem of evaluating the efficiency of cloud service providers (CSPs). However, no study addresses the efficiency of providers in the context of an entire supply chain, where multiple services interact to achieve a business objective or goal. Data envelopment analysis (DEA) is a powerful method for efficiency measurement problems. However, the current models ignore undesirable outputs, integer-valued, and stochastic data which can lead to inaccurate results. As such, the primary objective of this paper is to design a decision support system that accurately evaluates the efficiency of multiple CSPs in a supply chain. The current study incorporates undesirable outputs, integer-valued, and stochastic data in a network DEA model for the efficiency measurement of service providers. The results from a case study illustrate the applicability of our new system. The results also show how taking undesirable outputs, integer-valued, and stochastic data into account changes the efficiency of service providers. The system is also able to provide the optimal composition of CSPs to suit a customer’s priorities and requirements.
... This raises awareness and motivates consumers to boycott FC (Hajmohammad and Vachon, 2016). Additionally, pressure from public opinion on FC occurs through class associations, professional bodies, industry groups and other partners (Jajja et al., 2019) that encourage FC to become increasingly aware of society's demands, for example, of environmental sustainability (Mirhedayatian et al., 2014;Liu et al., 2017). This pressure can also mobilize public opinion for or against FC (Maestrini et al., 2017;Vanpoucke et al., 2016;Gong et al., 2018). ...
Purpose
To do this, the authors carried out a systematic literature review to answer three questions: (RQ1) Which external pressures affect an FC and its suppliers in an MSC? (RQ2) What influences power relationships between an FC and its suppliers for MSC compliance? and (RQ3) Which governance mechanisms support an FC to achieve compliance for managing its MSC?
Design/methodology/approach
This research aims to identify how external pressures affect chain agents to achieve compliance and implement governance mechanisms and analyzes the influence of the power relationship between FC and their suppliers.
Findings
The results identify how external pressures from different stakeholders act on FC and FT and ST suppliers. A combination of contractual governance mechanisms (auditing, certification, assessment, code of conduct and monitoring) with relational ones (third-party, cooperation) is identified, facilitating compliance between agents. Furthermore, different power relationships (power position, level of resources and institutional distance) that influence the implementation of governance mechanisms are explored.
Research limitations/implications
This article comprised only a systematic literature review and content analysis. Carrying out empirical research, covering the theme of this article, is the next step, which is being completed and will be discussed in due course in another publication.
Practical implications
The results can help professionals of the FC to understand their role in multi-tier supply chain (MSC), the external pressures exerted and the governance mechanisms that can be implemented to achieve compliance.
Originality/value
This article develops three relevant issues constantly addressed in MSC, which have not yet been combined to understand the management of multi-tier suppliers.
... To deal with network structure, Kao and Liu (2011) provided two-stage DEA model where all inputs and outputs are presented by fuzzy members. Mirhedayatian et al., (2014) proposed a fuzzy non-radial NDEA model for appraising the performance of green supply chain management. Tavana and Khalili-Damghani (2014) suggested a two-stage fuzzy DEA model via the α-cut method. ...
In recent years, many companies face economic, environmental, and social problems. Iranian Combined Cycle Power Plants (ICCPPs) consist of three stages with different types of inputs and outputs and complex connections with series and parallel structures. This study proposes a novel non-radial fuzzy network data envelopment analysis (FNDEA) model for assessing the sustainability of ICCPPs’ components in a fuzzy context. The new model is based on the range adjusted measure (RAM) model as it can handle both the desirable and undesirable outputs and has several properties such as translation invariant, unit invariant, and projection. The proposed model can simultaneously evaluate the sustainability of the ICCPPs, operational performance, and environmental performance in a unified model. The results reveal that 46% of ICCPPs have a relatively good performance in terms of operational and environmental performance. Also, the results of the correlation coefficient among different types of efficiency scores reveal that environmental efficiency has the most impact on the overall efficiency of ICCPPs. Finally, we suggest a way to improve the sustainability of ICCPPs.
... Delphi method was used, whose theoretical features are illustrated in Section 4. 239 Three key tasks need to be performed to complete a Delphi study (Linstone and Turoff, 1975 Fuzzy set theory is based on the assumption that "the main factors in human judgement and thought are 296 not numbers, but linguistic terms" (Rostamzadeh et al., 2015) and that words or sentences in a natural or 297 artificial language are more suitable to express complex situations compared to conventional quantification 298 (Mirhedayatian et al., 2014;Zadeh, 1975). Fuzzy theory supports the linguistic assessment of data by 299 decision-makers and concurrently handles the ambiguity associated with such linguistic data by exploiting ...
Circular economy (CE) implementation requires the transition from linear business models (BMs) to circular ones, with related uncertainties and multi-disciplinary risks, which often discourage organisations. However, there is still a lack of understanding of risks associated with this process. This work thus aims to identify, classify and prioritise key risk factors for innovative circular BMs in order to enable the development of appropriate risk management strategies.
A fuzzy Delphi method was tailored to assess the risk factors obtained from the literature and was applied to the industrial case of composite materials. 24 major risk factors for innovative circular BMs were identified and classified into six categories. The probability and impact of the risk factors were evaluated by experts and the risk factors were then ranked by calculating their risk scores. The resultant major risks appeared to be related to the external context in which organisations operate. Among those risks, the greatest were those generated by take-back systems and low customers’ acceptance of CE products.
This research is the first to address risks for circularity in a structured way and contributes to the field of CE by providing an extensive list and classification of risk factors for innovative circular BMs as they are perceived by industry, acting as a reference for academics and practitioners. Furthermore, it provides the first evaluation and prioritisation of risk factors within the CE domain, highlighting critical risks within the specific industrial context of composite materials and suggesting action priorities for the establishment of circular BMs.
... Second, the interaction of different performances has no clearly identified in GSCM practices. Most existing literature focuses on the effect of GSCM practices on environmental performance (Dubey et al., 2015;Mirhedayatian et al., 2014;Tuzkaya et al., 2009) and economic performance (Rao and Holt, 2005;Zhu et al., 2012a). Less literature pays attention to the impact of operational performance on environmental and economic performance. ...
Although the significance of green supply chain management activities on corporation sustainable development has been highlighted in the literature, the potential inter-dependencies between these activities and corporation performance have not been investigated by considering cooperation moderation. The purpose of this research is to examine internal and external Green Supply Chain Management (GSCM) activity on automobile performance: Environmental performance, operational performance, positive economic performance, and negative economic performance. Using survey data from foreign-owned company A with 117 manufacturing firms and domestic company B with 94 manufacturing firms, a significant relationship between GSCM and corporation performance has been found. For foreign-owned automobile companies, internal organizational activity has the greatest influence on operational performance, and promotes corporate value and corporation with outside. However, for domestic automobile companies with the advanced green concept, Eco-design exerts the biggest effect on environmental performance, and also brought a negative economic impact on corporate performance. Moreover, although both corporate social responsibility (CSR) and creating shared value (CSV) promotion could influence the corporation with consumers and suppliers, CSV promotion has a more positive influence on cooperation with consumers than that CSR. These findings have important implications for designing GSCM strategic plans for the automotive industry in developing countries.
... On the other hand, traditional DEA models deal with black box DMUs and ignore the internal components of DMUs. It is argued that ignoring the internal structure of DMUs may lead to misleading results (Mirhedayatian et al., 2014). For the first time, Färe and Grosskopf (1996) developed the network DEA. ...
The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes.
... In this section, an example of soft drinks companies is provided to explain the proposed approaches and to show their applicability. Data are partially derived from (Mirhedayatian et al. 2014). We consider each process as a network with two components, supplier and producer. ...
... The idea here is to first detect outliers in the dataset and then handle them in the estimation process since the outliers leads to a biased results. Let us look at an example that represents 11 years of input data: 2, 10, 45,49,52,56,78,83,86,90,148. We must first establish the quartiles (Q1, Q2, Q3, interquartile range (IQR=Q3-Q1), lower fence (Q1-1.5*IQR), ...
In the world of chaos, nothing is certain. In such an unpredictable world, measuring the efficiency of any individual is inevitable. In a conventional data envelopment analysis (DEA) model, exact input and output quantity data are needed to measure the relative efficiencies of homogeneous decision-making units (DMUs). However, in many real-world applications, the exact knowledge of data might not be available. The rough set theory allows for handling this type of situation. This paper tries to construct a rough DEA model by combining conventional DEA and rough set theory using optimistic and pessimistic \begin{document}\end{document} confidence values of rough variables, all of which help provide a way to quantify uncertainty. In the proposed method, the same set of constraints (production possibility sets) is employed to build a unified production frontier for all DMUs that can be used to properly assess each DMU's performance in the presence of rough input and output data. Besides, a ranking system is presented based on the approaches that have been proposed. In the presence of uncertain conditions, this article investigates the efficiency of the Indian fertilizer supply chain for over a decade. The results of the proposed models are compared to the existing DEA models, demonstrating how decision-makers can increase the supply chain performance of Indian fertilizer industries.
... The combination of the DEMATEL approach with fuzzy set theory overcomes existing limitations in dealing with the subjective judgment of experts (Zadeh, 1965). A fuzzy number is an extension of a regular number, and instead of referring to a single value it refers to a contiguous set of potential values, whereby each value has its own weight between 0 and 1 (Mirhedayatian et al., 2014), which is called membership function. Therefore, a fuzzy number is a specific type of a convex fuzzy set. ...
The paradigm of circular economy and the transfer of its principles to supply chain management has recently received much attention from researchers and practitioners. Limited natural resources, governmental legislation, and social responsibility for environmental preservation are the main factors for the development of circular supply chains. Especially in the automotive industry, critical materials are used to produce electric vehicles, reinforcing the importance of circularity for the industry. As the first stage of the supply chain, suppliers have a considerable influence on creating self-sufficient production systems, and in the automotive sector, a low level of vertical integration is important. Therefore, selecting suppliers is an enabler for circular supply chains, but this decision is a complex process due to multiple, partly conflicting criteria. To contribute to the knowledge in this research area, this study applies a fuzzy decision-making trial and evaluation laboratory approach for supplier selection in a circular supply chain in a case study of electric vehicles. First, a set of criteria for supplier selection in circular supply chains was identified based on a literature review and was systematically categorized based on the natural resource-based view. Afterward, the criteria were assessed and refined by experts with a strong procurement background in the automotive industry. Second, the experts were interviewed for value collection, and finally, the mutual influence of the criteria was determined. The results show that the most important criteria for circular supplier selection in electric vehicle supply chains are environmental standards, environmental-related certifications, resource consumption, and waste generation. Regarding the natural resource-based view, short-term focused criteria addressing the capability of pollution prevention have a higher importance compared to long-term criteria that drive sustainable development. In addition, this study classifies the criteria into the categories of causes and effects, discusses the prominence of the criteria, and presents a strategic map showing the mutual influences of the criteria. The results contribute to the theoretical and practical discussion on circular supply chains by identifying the key criteria for circular supplier selection and providing decision-making support for procurement managers.
... There were also considerations for potential game theory applications in SCM [97]. Furthermore, the keyword "performance" referred to both economic performance and environmental performance that could be enhanced by green supply chain management [98,99], and inventory management also had been cited as one of the keywords of supply chain management, as Belien [100] presented a review of the literature on inventory and supply chain management of blood products. ...
Supply chain management (SCM), which generally refers to horizontal integration management, has steadily become the core competitiveness of company rivalry and an essential approach to developing national comprehensive and national strength since the end of the 20th century due to the numerous needs arising from a competitive international economy. Manufacturers develop a community of interest by forming long-term strategic partnerships with suppliers and vendors throughout the supply chain. This paper defines supply chain management by reviewing the existing literature and discusses the current state of supply chain management research, as well as prospective research directions. Specifically, we conducted a bibliometric analysis of the influential studies of SCM in terms of various aspects, such as research areas, journals, countries/regions, institutions, authors and corresponding authors, most cited publications, and author keywords, based on the 8998 reviews and articles collected from the SCI and SSCI database of the Web of Science (WoS) between 2010 and 2020. The results show that the major research areas were Management (3071, 34.13%), Operations Research & Management Science (2680, 29.78%), and Engineering, Industrial (1854, 20.60%) with TP and TPR%. The most productive journal and institution were J. Clean Prod and Hong Kong Polytech Univ with a TP of 554 and 238, respectively. China, USA, and UK were the top three contributing countries. Furthermore, “sustainability”, “green supply chain (management)”, and “sustainable supply chain (management)” were the most popular author keywords in recent three years and since 2010, apart from the author keywords of SCM. When combined with the most cited articles in recent years, the application of block chain and Industry 4.0 in supply chain management increased rapidly and generated great attention.
... is approach consider DMUs as a system composed of some stages characterized by their own inputs and outputs and related by intermediate measures [7]. ...
The two-stage data envelopment analysis models are among the widely used mathematical programming approaches to evaluate the performance of two-stage structures. In this paper, a two-stage structure with shared inputs and feedback is studied. To reduce undesirable outputs, an additive slack-based measure model is proposed to evaluate the stages and overall efficiencies, while undesirable outputs are weakly disposable. As it does not require determining the weights for combining stages’ efficiencies, all Pareto optimal stages’ efficiencies can be gained. In addition, the proposed approach can identify desirable outputs from undesirable outputs, thereby avoiding the need for weighting. This advantage from the aspect of multiobjective programming helps internal evaluation of the network model to match priorities of managers. The proposed nonlinear model is reformulated as a second-order cone program, which is a convex optimization problem that can be solved to global optimality. This is a computational improvement over the parametric models in the literature. Furthermore, the proposed model is applied for country-wise and area-wise performance evaluations of a real industrial application dataset in mainland China. Results show that the efficiency of the overall system relies between the efficiencies of the two stages and for all DMUs, the first stage’s efficiency scores are always higher than the second stage ones in both evaluations. Also, the Pearson correlation coefficient test results show that the overall efficiency is more correlated with the waste disposal stage. Finally, to show the effect of the decision maker’s preference, a detailed sensitivity analysis is performed.
The purpose of this study is to propose a new dynamic network-based data envelopment analysis approach to appraise the efficiency of Iran’s electricity supply chains in terms of sustainability and resilience paradigms over consecutive periods. The proposed dynamic network-based data envelopment analysis model is developed in a Rang Adjusted Measure framework as it can well handle both the desirable and undesirable outputs and has several properties such as translation invariant, unit invariant, and projection. Then to deal with qualitative and imprecise data, the proposed model is extended in a fuzzy context. The network-based and dynamic features of the suggested model make it possible to appraise the efficiency of the whole electricity supply chains and its components simultaneously at multi-period planning. The dynamic feature allows the suggested model to recognize changes in the efficiency of the Iran’s electricity supply chains much better than static data envelopment analysis models. The suggested model comprehensively measures the efficiency of Iran’s electricity supply chains by taking into account sustainability and resilience paradigms using real data. Given the research findings, the privatization of companies has had a significant impact on the performance of Iran’s electricity supply chains in 2020. Also, the results of this study show that the average efficiency of the transmission sector from 2017 to 2020 is higher than the two other generation and distribution sectors. Finally, this study suggests a way to improve the sustainability and resilience of Iran’s electricity supply chains. The proposed methodology can be applied to electricity supply chains of other countries as well.This study introduces a new fuzzy dynamic network data envelopment analysis model and apply it to appraise the sustainability and resilience of the Iran’s electricity supply chains during 2017–2020 years.
One of the challenges for suppliers is to increase their market share due to the limited target market. In other
words, in the supply chain, the demand for suppliers’ products is limited. Therefore, suppliers produce a
determined share of the required amount by producers. However, when it comes to the share in the amount of
output among suppliers, the output of suppliers in this indicator is interdependent. In the classical data envelopment
analysis (DEA) models, there are no models that assess the suppliers according to the dependence of at
least one output on each other. In this paper, a model is presented that can assess sustainable suppliers in the
presence of interdependent output among suppliers by using DEA models and their extension. It can also
determine the total benchmarks of decision making units (DMUs) in such a way that satisfies the interdependent
output constraint. In other words, benchmarks are not determined independently. Ultimately, an approach is
presented to determine efficient projections for inefficient DMUs by considering the concept of interdependent
output. To represent the applicability of our proposed model, a dataset for two consecutive years including 32
sustainable suppliers with consideration of interdependent output has been implemented using the model presented
in this paper. The resulting sustainability has been compared with a classical model.
In this paper, we present a comprehensive approach for evaluating efficiency in complex networks by integrating network data envelopment analysis (NDEA) with the Malmquist productivity index. The proposed method addresses the inherent challenge of accommodating negative data within the network efficiency evaluation framework, which is a common occurrence in real-world network operations. Through the introduction of a two-stage structure, the model not only effectively manages the presence of negative values, but also provides a robust and insightful assessment of network efficiency. A case study from banking sector is employed to demonstrate the efficacy of the proposed approach, showcasing its capacity to offer valuable and actionable insights for decision-making in complex network environments. The results highlight the practical applicability and importance of the extended approach in addressing the complexities associated with evaluating efficiency in diverse network settings.
This paper estimates the Most Productive Scale Size (MPSS) in the NDEA model to appraise the sustainability and resilience of the supply chains. As the corresponding input and output criteria are not always accurately measurable, we also introduce the fuzzy version of our proposed NDEA model and apply the proposed model in a case study involving 10 Iranian supply chains of Companies Producing Soft Drinks (CPSDs). The considered-three-echelon supply chains include suppliers, manufacturers, and distributors. Mathematical analysis proves that the MPSS of the considered supply chain can be decomposed as the sum of the MPSS values of the individual stages. Thus, the supply chain is overall MPSS if and only if it is MPSS in every three stages. The results of this study reveal that the Behnoush supply chain is overall MPSS in all three stages, including supplier, manufacturer, and distributor, for any α ∈ {0.1,0.5, 1}. A sensitivity analysis has been performed to measure the impact of each criterion on the entire supply chain performance. The sensitivity analysis results indicate that the social and resilience criteria significantly impact the performance and ranking of supply chains. Finally, we discuss how to improve the sustainability and resilience of non-MPSS supply chains.
This chapter investigates data envelopment analysis (DEA) in the context of supply chain management. DEA, a non-parametric method for assessing productive efficiency with many inputs and outputs, has considerably impacted research and practical implementation. It enables performance analysis across organizations with complicated input-output interactions. DEA provides user-friendly and customizable criterion weighting, simplifies analysis by eliminating the need for production function calculation, and delivers comprehensive efficiency measurements. This chapter examines existing research on the use of DEA in supply chains to assess present practices, recent breakthroughs, and techniques critically. This chapter addresses the central research question, “What are the latest advancements and methodologies in applying DEA to the supply chain?” The findings of this study add to the understanding of current practices at the confluence of DEA and supply chain management, which is critical in today's complex corporate context.
In recent decades, Group Decision-Making (GDM) has emerged as a potent strategy for addressing pivotal decisions within organizations. A fundamental step in GDM methodologies, such as the Analytical Hierarchy Process (AHP), involves deriving priorities from Pairwise Comparison Matrices (PCMs). The Eigenvector Method (EM) has conventionally served as the prevailing means for weight determination in AHP. Nonetheless, inherent limitations mar its effectiveness, primarily stemming from its non-linearity and susceptibility to inconsistency-related issues. To redress these shortcomings, this paper advances a novel approach by first introducing a Linear Programming (LP) methodology grounded in EM principles for priority derivation. Subsequently, the paper introduces three distinctive LP models, which utilize an enhanced set of constraints derived from revised EM constraints, to ascertain both weights and priorities within the PCM. Notably, these models yield accurate weights for Perfectly Consistent PCM (PCPCM) and effectively determine optimal local priorities for inconsistent PCMs, closely aligned with EM-derived priority vectors. Comparative analysis between the proposed models and existing counterparts underscores the superiority of the former, particularly in weight determination. The proposed models, showcased through a comprehensive case study, exhibit significant advantages in enhancing GDM through the AHP technique, thereby substantiating their practical applicability. Key contributions of this paper include the novel proposition of a LP approach grounded in EM for priority derivation, and the introduction of three innovative models for weights and priority determination. These models are subsequently adapted for GDM within the AHP framework. Moreover, the proposed models stand resilient against the issue of rank reversal, even with the addition or removal of unrelated choices. Additionally, their adaptability extends to the group AHP (GAHP) method, encompassing interval and fuzzy weights. In summation, this paper underscores the evolution of GDM methodologies, propelling the field towards enhanced precision and applicability. The introduced models not only address existing limitations but also lay the foundation for novel avenues of research and practice in multi-criteria decision-making paradigms.
The goal of green procurement, also called green purchasing, is to reduce waste and improve operational efficiencies to enhance sustainability. Although this practice has gained importance in recent years and garnered significant scholarly attention, there is a lack of bibliometric studies evaluating the green procurement field. To close this gap, we leverage bibliometrics to comprehensively summarize the literature and identify existing research hotspots and trends. Specifically, we employ bibliometric tools to analyze keywords, identify influential authors, universities, and research areas and reveal the most important publications in terms of citations. The analysis shows that sustainable development, sustainability, green supply chain management, and green public procurement are core topics related to green procurement. The co-citation analysis further reveals five important research clusters in the literature, namely green public procurement, green supply chain management, green supplier selection and evaluation of green performance, networked sustainable procurement, and green procurement in the construction sector. This study makes a contribution to the green procurement literature by summarizing this quickly growing field and providing timely guidance as to future research directions.
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.
The preceding two chapters discussed systems with two divisions connected in series. Intuitively, the two-stage system should be able to be extended to multiple stages to suit more general cases. As a matter of fact, many real-world systems have a multi-stage structure, with assembly lines as typical examples, where raw materials go through a number of workstations to become the final products. The meaning of multi-stage system in this context is rather vague, because a stage may have several divisions connected in different structures. What it refers to in the conventional network DEA is a system composed of a number of divisions connected in series, with only one division in each stage. In this regard, the term series system may be more appropriate, and in this chapter these two terms will be used interchangeably when there is no ambiguity.
Purpose
This study aims to benchmark the operational efficiency of fifty-eight public hospitals across Mexico between 2015 and 2018 and identifies the most critical inputs affecting their efficiency. In doing so, the study analyzes the impact of policy changes in the Mexican healthcare system introduced in recent years.
Design/methodology/approach
To measure the operational efficiency of Mexican public hospitals, data envelopment analysis (DEA) window analysis variable returns to scale (VRS) methodology using longitudinal data collected from the National Institute for Transparency and Access to Information (IFAI). Hospital groups are developed and compared using a categorization approach according to their average and most recent efficiency.
Findings
Results show that most of the hospitals in the study fall in the moving ahead category. The hospitals in the losing momentum or falling behind categories are mostly large units. Hospitals with initially low efficiency scores have either increased their efficiency or at least maintained a steady improvement. Finally, the findings indicate that most hospitals classified as moving ahead focused on a single care area (cancer, orthopedic care, child care and trauma).
Research limitations/implications
This study examined the technical efficiency of the Mexican healthcare system over a four-year period. Contrary to conventional belief, results indicate that most public Mexican hospitals are managed efficiently. However, recent changes in public and economic policies that came into effect in the current administration (2018) will likely have long-lasting effects on the hospitals' operational efficiency, which could impact the results of this study.
Originality/value
To the best of authors’ knowledge, this is the first study that examines the efficiency of the complex Mexican healthcare system using longitudinal data.
The transport industry is one of the main contributors to environmental pollutions. Sus-tainability evaluation of the transport industry helps companies to increase their awarenessand leads to the right decisions. This study addresses the subject of sustainability for thetransportation supply chain. Data envelopment analysis (DEA is a popular approach forefficiency evaluation. This work develops a common set of weights (CSW) model usingtwo-stage network DEA and Shannon’s entropy. The proposed CSW model evaluates thesustainability of transportation supply chains in DEA context. The objective of this paperis to propose an integrated slack-based two-stage network DEA model with zero inputsand CSW analysis using Shannon’s entropy technique. To calculate the optimal weights,the Shannon entropy technique is used. To the best of the authors’ knowledge, there is notwo-stage network DEA model based on Shannon’s entropy for evaluating the sustainabil-ity of transport companies when there are zero inputs. The proposed model can fully rankDMUs. In this study, optimal scores by different weights are obtained and can be appliedin real-world problems. To demonstrate the applicability of the proposed approach, the sus-tainability of transportation supply chains is assessed.
(PDF) Sustainability evaluation of transportation supply chains by common set of weights-network DEA and Shannon's entropy in the presence of zero inputs of weights ·
The transport industry is one of the main contributors to environmental pollutions. Sustainability evaluation of the transport industry helps companies to increase their awareness and leads to the right decisions. This study addresses the subject of sustainability for the transportation supply chain. Data envelopment analysis (DEA is a popular approach for efficiency evaluation. This work develops a common set of weights (CSW) model using two-stage network DEA and Shannon's entropy. The proposed CSW model evaluates the sustainability of transportation supply chains in DEA context. The objective of this paper is to propose an integrated slack-based two-stage network DEA model with zero inputs and CSW analysis using Shannon's entropy technique. To calculate the optimal weights, the Shannon entropy technique is used. To the best of the authors' knowledge, there is no two-stage network DEA model based on Shannon's entropy for evaluating the sustainability of transport companies when there are zero inputs. The proposed model can fully rank DMUs. In this study, optimal scores by different weights are obtained and can be applied in real-world problems. To demonstrate the applicability of the proposed approach, the sustainability of transportation supply chains is assessed.
With increasing awareness about society and the environment, industries are urged to develop and implement sustainable supply chain (SSC) processes. However, the risk of non‐compliance against these SSC processes to manage overall business risks, namely, avoiding reputational damage and managing financial losses, is increasingly receiving senior management attention. Given these shortcomings, the objective of this research is twofold, namely, (i) to identify and evaluate barriers adopting sustainable supply chain risk management (SSCRM) processes and (ii) to prioritize SSCRM strategies to overcome these barriers in an emerging economy, namely, Bangladesh. To achieve the objectives, this study develops a framework by integrating the technique for order of preference by similarity to ideal solution (TOPSIS) and VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR). The results show that the “information‐related barriers” are most prevalent among the categories of barriers, and “lack of coordination and collaboration” has been identified as the most significant barrier. Evaluating the strategies, “top management commitment” is the best strategy. These findings can help managers develop strategies to overcome the most significant barriers to adopting SSCRM. The proposed framework, which integrates quantitative and qualitative approaches, can be used by decision‐makers to make accurate, prompt, and systematic decisions compliant with SSCRM business processes.
In the present era of the fourth industrial revolution, small and medium enterprises (SMEs) are adopting smart, green, resilient, and lean (SGRL) practices to enhance their performance and achieve sustainability. For SMEs to perform well in their supply chains and satisfy customers, the impact of the combined effects of SGRL manufacturing on SMEs’ performance needs to be studied. Although SGRL manufacturing has been studied independently in order to understand its impact on SMEs’ performance, there is still a need for significant research on its combined effect. The objective of the present work is to evaluate the performance of SMEs and to understand the combined effect of SGRL manufacturing on SMEs’ performance. This research applied the data envelopment analysis (DEA) methodology to evaluate 30 SMEs identified in the northern region of India. A DEA model was developed that considers environmental, operational, and social performances as output criteria while considering SGRL practices as input criteria. Sixteen decision-making units (DMUs) were identified as inefficient using the DEA approach and one of them was considered for a case study for comparison with efficient SMEs. The case study employed a Strength, Weakness, Opportunity, and Threat (SWOT) analysis to provide remedial action to one of the selected underperforming SMEs, i.e.,SME11. The strengths, weaknesses, opportunities, and threats of SME11 were identified and strategies were suggested by benchmarking SME11 with one of the efficient SMEs, i.e., SME23. The findings of this research work will help policymakers, owners, and managers of SMEs take necessary actions and enhance their performance by adopting the proposed DEA model using SGRL manufacturing practices.
This study aims to examine the relationship between green supply chain management (GSCM) practices and environmental performance (ENP) in Bangladesh's manufacturing industry. Data were gathered from 220 managers using survey methods. The results show that GSCM techniques have a beneficial impact on ENP. The findings also show a strong correlation between green marketing and ENP. Additionally, this study will support the implementation of GSCM methods by supply chain managers, policy makers, and practitioners in order to enhance ENP. The implications of GSCM are examined, along with suggestions for further study.
Iran has subsidised trout egg imports due to the lower feed conversion ratio of most imported trout eggs. However, the rainbow trout industry is divided on whether to allocate or abolish imported trout subsidies. The elimination of such support would lead to a three‐fold rise in the price of imported trout eggs (which are already more expensive than domestic trout eggs). Furthermore, the economic performance of the domestic trout supply chain over the imported trout supply chain remains yet to be clarified. The present study seeks to evaluate whether imported trout egg subsidies should be continued. Economic efficiency and network data envelopment analysis are used to evaluate the performance and role of subsidies in trout supply chains in Mazandaran, one of the largest trout farming provinces as a new case study in the aquaculture sector, in 2018. Farms that used imported trout eggs were found to have lower economic efficiency and higher dependence on import subsidies. Feed costs were identified to be the most important explanation for poor economic efficiency of trout production. Finally, the results reveal that the development of domestic trout propagation supply chains with integrated forward vertical structures could improve efficiency in the system. The re‐distribution of imported trout egg subsidies to fund feed cost reduction technologies could provide effective solutions to enhance economic efficiency, production sustainability and food security.
Nowadays, most supply chains are starting to go green in their business with pay more attention to environmental protection as competitive advantage. Therefore, Designing a two-stage green supply chain for optimum assignment a green supplier to a green producer based on maximum efficiency and attention to intermediate products and processes is essential. Because, economic performance and environmental performance of the supply chain will increases. One of the methods used to evaluating efficiency in the green supply chain management, is data envelopment analysis (DEA). The traditional DEA methods for evaluating efficiency of supply chain processes and multi-stage systems not working properly, Because, each decision making units is assumed as a black box and ignore its internal processes. In order to overcome this deficiency, a novel two-stage network DEA will be presented based on the concepts of Electrical Engineering that ability to consider all inputs, Intermediate products, desirable and undesirable outputs between supplier and producer in the green supply chain for optimum assignment a supplier to producer based on maximum efficiency. The proposed model has been described by an application example and Its reliability has been confirmed.
Purpose
This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.
Design/methodology/approach
The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.
Findings
The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.
Research limitations/implications
This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.
Practical implications
The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.
Originality/value
This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.
Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, And DEA-Solver Software, 2nd Edition is designed to provide a systematic introduction to DEA and its uses as a multifaceted tool for evaluating problems in a variety of contexts. Each chapter accompanies its developments with simple numerical examples and discussions of actual applications. Emphasis is placed on the use as well as an understanding of DEA and the topics in this book have been selected and treated accordingly. The first nine chapters cover the basic principles of DEA and the final seven chapters are more advanced treatment of DEA. These final chapters were completely revised into new chapters, reflecting recent developments that greatly extend the power and scope of DEA and lead to new directions for research and DEA uses. Together with the first ten chapters of the basic principles, they will provide students and researchers with a solid understanding of the methodology, its uses and its potential.
Multilateral productivity comparisons of firms producing multiple outputs, some of which are undesirable, are obtained by making two modifications to the standard Farrell approach to efficiency measurement. The restriction that production technology satisfy strong disposability of outputs is relaxed to allow for the fact that undesirable outputs may be freely disposable, and the efficiency measures are modified to allow for an asymmetric treatment of desirable and undesirable outputs. Performance measures that satisfy these requirements are calculated as solutions to programming problems. The methodology is applied to a sample of mills producing paper and pollutants.
Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.
We show how to use DEA to model DMUs that produce in two stages, with output from the first stage becoming input to the second stage. Our model allows for any orientation or scale assumption. We apply the model to Major League Baseball, demonstrating its advantages over a standard DEA model. Our model detects inefficiencies that standard DEA models miss, and it can allow for resource consumption that the standard DEA model counts towards inefficiency. Additionally, our model distinguishes inefficiency in the first stage from that in the second stage, allowing managers to target inefficient stages of the production process.
DEA models treat the DMU as a “black box.” Inputs enter and outputs exit, with no consideration of the intervening steps. Consequently, it is difficult, if not impossible, to provide individual DMU managers with specific information regarding the sources of inefficiency within their DMUs. We show how to use DEA to look inside the DMU, allowing greater insight as to the sources of organizational inefficiency. Our model applies to DMUs that consist of a network of Sub-DMUs, some of which consume resources produced by other Sub-DMUs and some of which produce resources consumed by other Sub-DMUs. Our Network DEA Model allows for either an input orientation or an output orientation, any of the four standard assumptions regarding returns to scale in any Sub-DMU, and adjustments for site characteristics in each Sub-DMU. We demonstrate how to incorporate reverse quantities as inputs, intermediate products, or outputs. Thus, we can apply the Network DEA Model presented here in many managerial contexts. We also prove some theoretical properties of the Network DEA Model.By applying the Network DEA Model to Major League Baseball, we demonstrate the advantages of the Network DEA Model over the standard DEA Model. Specifically, the Network DEA Model can detect inefficiencies that the standard DEA Model misses. Perhaps of greatest significance, the Network DEA Model allows individual DMU managers to focus efficiency-enhancing strategies on the individual stages of the production process.
Traditional DEA models deal with measurements of relative efficiency of DMUs regarding multiple-inputs vs. multiple-outputs. One of the drawbacks of these models is the neglect of intermediate products or linking activities. After pointing out needs for inclusion of them to DEA models, we propose a slacks-based network DEA model, called Network SBM, that can deal with intermediate products formally. Using this model we can evaluate divisional efficiencies along with the overall efficiency of decision making units (DMUs).
Efficiency measurement in Data Envelopment Analysis (DEA) is usually based on the assumption that inputs have to be minimized and outputs have to be maximized. In some situations, undesirable (bad) inputs and outputs may be presented in the production process. In order to improve the performance of an inefficient Decision Making Unit (DMU) the undesirable outputs and desirable (good) inputs should be decreased while the desirable outputs and undesirable inputs should be increased. In this paper, we present a method to deal with such inputs and outputs in non-radial DEA models.
In this paper we consider the problem of determining teaching and research efficiencies for university departments concerned with the same discipline. Considering this problem highlights the issue of how to determine efficiencies when resources are shared between different activities, and a non-linear approach to this issue based upon data envelopment analysis is presented. Computational results are given for chemistry and physics departments in the United Kingdom.
A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.
The decision of supplier selection is strategic and crucial to the success of corporations. For selecting suppliers, data envelopment analysis (DEA), as a multiple criteria decision-making tool, has been applied for several times. However, there may exist some particular situations in supplier selection problem; i.e. (1) the presence of dual-role factor which may be classified either an input or an output. The quantity of such a factor may influence the relative efficiency of the suppliers, (2) the presence of non-discretionary or exogenously fixed inputs; in any realistic situation, there may exist non-discretionary criteria that are beyond the control of a management and (3) the presence of weight restrictions; sometimes it is needed to incorporate the preferences of managers and decision-makers into DEA model which is used to select suppliers. The objective of this paper is to propose a DEA model for selecting suppliers in the presence of dual-role factors, non-discretionary inputs and weight restrictions. A numerical example demonstrates the application of the proposed model.
Taiwan's banking industry remains highly fragmented and competitive after a series of financial liberalization and restructuring. With the enforcement of these fiscal policies, domestic banking institutions face a more dynamic, increasingly intense and highly competitive environment even as the banking industry's overall efficiency has gradually been enhanced. This structural change has further forced individual banking institutions, especially state-owned banks, to inspect the performance of their branches and identify improvement directions so as to gain further competitive advantages. To conduct a valid, fair and reliable evaluation on Taiwan's bank branches, we integrate a two-stage series performance model and fuzzy multiobjective model. A new scheme that considers the complementation of production and intermediation activities within a branch and overcomes the shortage of the traditional network DEA methodology about DMUs cannot be assessed on a common base. The results indicate that the overall performances of mixed ownership bank branches are superior to those of state-owned bank branches, representing that the advantages of banking privatization have some remedial effects for improving the managerial inefficiency of state-owned banks. In addition, the sensitivity analysis and decision-making matrix herein help bank management to identify branches' efficiency, weakness, and directions for improvement.
Green supply chain management (GSCM) has become a proactive approach to enhance environmental performance. Under stakeholder pressures and regulations, firms need to enhance GSCM practice, which are influenced by practices such as green purchasing, green design, product recovery, and collaboration with customers and suppliers. As proactive firms adopt GSCM, their economic performance and environmental performance will be improved. Hence, this study aims to examine the influential factors among eight criteria of three main GSCM practices, namely practices, performances, and external pressures. To deal with the vagueness of human being’s perceptions, this study utilizes the fuzzy set theory and decision making trial and evaluation laboratory method to form a structural model to find out the cause and effect relationships among criteria. The results and managerial implications are discussed.
In conventional data envelopment analysis it is assumed that the input versus output status of each chosen performance measures is known. In some conditions finding a statue of some variables from the point view of input or output is very difficult; these variables treat as both an input and output and are called flexible measures. This paper proposes a new model based on translog output distance function for classifying inputs and outputs and evaluating the performance of decision-making units by considering flexible measures. Monte Carlo simulation is applied to evaluate the presented model comparing with that of the recent model found in the literature. The result shows that the measure efficiencies of our model are statistically closer to true efficiencies and have higher rank correlation with true efficiencies. Also results obtained from simulated data show that there are high correlation between our model and that of the recent model.
A dynamic theory of production correspondences is formulated, first abstractly as mappings between function spaces and second as an activity analysis model related to a directed graph defining exogenous inputs, intermediate product and net output transfers. By introducing time variable technical coefficients and time lags, the activity analysis model is given specific form with illustrations of computations.
This paper presents a methodology for dealing with performance evaluation settings where factors can simultaneously play both input and output roles. Model structures are developed for classifying Decision-Making Units (DMUs) into three groups according to whether such a factor is behaving like an output, an input, or is in equilibrium, neither wanting to lose or gain any of the factors. We connect these ideas to those involving increasing, decreasing and constant returns to scale. Examples of factors that play this dual-role are: trainees in organizations, such as nurses, medical students, and doctoral students; awards to scholars or university departments; certain revenue—generating transactions in banks, and so on. We apply the model to the analysis of a set of university departments. In some settings, a dual-role factor may be one that can be reallocated, such as would be the case when DMUs are managed by a central authority. We develop the appropriate model structures to permit such a reallocation. We present two such structures, with the first involving reallocation from an existing allocation, and the second, a zero-base allocation.
There has been an increasing interest towards firms' environmental sustainability activities to improve practices in their supply chain. Stringent environmental regulations in Europe and US challenge manufacturers to comply with these without losing their competitiveness. This study illustrates the case of a printed circuit board manufacturer in Taiwan that seeks to implement green supply chain management (GSCM) and selects a green supplier to meet its requirements. Choosing the suitable supplier is a key strategic direction in eliminating environmental impact on supply chain management for manufacturing firms. The firm's criteria and supplier selection need to be unified as a system to improve the firm's performance. This study identified the appropriate environmental and non-environmental GSCM criteria for the case firm and developed the following selection method: (i) evaluate the weights of criteria and alternatives as described both by qualitative and quantitative information; and (ii) rank alternative suppliers using a grey relational analysis. The result shows Alternative 3 ranks first among the four evaluated suppliers and demonstrated strong performance in the top three important criteria, namely, environmental management systems, profitability of supplier and relationship supplier closeness. Additionally, the perception weights on criteria itself are same as the most top five in weighted alternative.
There is a growing need for integrating environmentally sound choices into supply-chain management research and practice. Perusal of the literature shows that a broad frame of reference for green supply-chain management (GrSCM) is not adequately developed. Regulatory bodies that formulate regulations to meet societal and ecological concerns to facilitate growth of business and economy also suffer from its absence. A succinct classification to help academicians, researchers and practitioners in understanding integrated GrSCM from a wider perspective is needed. Further, sufficient literature is available to warrant such classification. This paper takes an integrated and fresh look into the area of GrSCM. The literature on GrSCM is covered exhaustively from its conceptualization, primarily taking a ‘reverse logistics angle’. Using the rich body of available literature, including earlier reviews that had relatively limited perspectives, the literature on GrSCM is classified on the basis of the problem context in supply chain's major influential areas. It is also classified on the basis of methodology and approach adopted. Various mathematical tools/techniques used in literature vis-à-vis the contexts of GrSCM are mapped. A timeline indicating relevant papers is also provided as a ready reference. Finally, the findings and interpretations are summarized, and the main research issues and opportunities are highlighted.
This chapter describes network DEA models, where a network consists of sub-technologies. A DEA model typically describes a
technology to a level of abstraction necessary for the analyst’s purpose, but leaves out a description of the sub-technologies
that make up the internal functions of the technology. These sub-technologies are usually treated as a “black box”, i.e.,
there is no information about what happens inside them. The specification of the sub-technologies enables the explicit examination
of input allocation and intermediate products that together form the production process. The combination of sub-technologies
into networks provides a method of analyzing problems that the traditional DEA models cannot address. We apply network DEA
methods to three examples; a static production technology with intermediate products, a dynamic production technology, and
technology adoption (or embodied technological change). The data and GAMS code for two examples of network DEA models are
listed in appendices.
Sustainable production indicators (SPIs) is a complex and uncertainty concept that is difficult in determining multiple qualitative criteria. Significant efforts have been made related to the measures of SPIs effectiveness. However, a generalized quantitative evaluation model, which considers both the interdependence relation between criteria and the fuzziness of subjective perception concurrently, is lacking. This study evaluates the performance of synthetic SPIs by adopting fuzzy measure and analytical network process (ANP) method in a multi-nation original equipment manufacturing firm. The analytical results indicated that the ANP is a simple, suitable, and effective method for identifying the primary criteria influencing SPIs at case firms, especially when evaluation criteria are interactive and interdependent. The proposed approach is an effective method for assessing the SPIs of a firm and obtains useful information regarding hierarchical framework.
Supply chain management (SCM) practices have flourished since the 1990s. Enterprises realize that a large amount of direct and indirect profits can be obtained from effective and efficient SCM practices. Supplier selection has great impact on integration of the supply chain relationship. Effective and accurate supplier selection decisions are significant components for productions and logistics management in many firms to enhance their organizational performance. This study pioneers in using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method to find influential factors in selecting SCM suppliers. The DEMATEL method evaluates supplier performance to find key factor criteria to improve performance and provides a novel approach of decision-making information in SCM supplier selection. This research designs a fuzzy DEMATEL questionnaire sent to seventeen professional purchasing personnel in the electronic industry. Our research results find that stable delivery of goods is the most influence and the strongest connection to other criteria.
Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple performance factors which are grouped into outputs and inputs. Once the efficient frontier is determined, inefficient DMUs can improve their performance to reach the efficient frontier by either increasing their current output levels or decreasing their current input levels. However, both desirable (good) and undesirable (bad) factors may be present. For example, if inefficiency exists in production processes where final products are manufactured with a production of wastes and pollutants, the outputs of wastes and pollutants are undesirable and should be reduced to improve the performance. Using the classification invariance property, we show that the standard DEA model can be used to improve the performance via increasing the desirable outputs and decreasing the undesirable outputs. The method can also be applied to situations when some inputs need to be increased to improve the performance. The linearity and convexity of DEA are preserved through our proposal.
In conventional DEA analysis, DMUs are generally treated as a black-box in the sense that internal structures are ignored, and the performance of a DMU is assumed to be a function of a set of chosen inputs and outputs. A significant body of work has been directed at problem settings where the DMU is characterized by a multistage process; supply chains and many manufacturing processes take this form. Recent DEA literature on serial processes has tended to concentrate on closed systems, that is, where the outputs from one stage become the inputs to the next stage, and where no other inputs enter the process at any intermediate stage. The current paper examines the more general problem of an open multistage process. Here, some outputs from a given stage may leave the system while others become inputs to the next stage. As well, new inputs can enter at any stage. We then extend the methodology to examine general network structures. We represent the overall efficiency of such a structure as an additive weighted average of the efficiencies of the individual components or stages that make up that structure. The model therefore allows one to evaluate not only the overall performance of the network, but as well represent how that performance decomposes into measures for the individual components of the network. We illustrate the model using two data sets.
Return of used products is becoming an important logistics activity due to government legislation and increasing awareness among the people to protect the environment and reduce waste. For industries, the management of return flow usually requires a specialized infrastructure with special information systems for tracking and dedicated equipment for the processing of returns. Therefore, industries are turning to third-party reverse logistics providers (3PRLPs). In this study, a multi-criteria group decision-making (MCGDM) model in fuzzy environment is developed to guide the selection process of best 3PRLP. The interactions among the criteria are also analyzed before arriving at a decision for the selection of 3PRLP from among 15 alternatives. The analysis is done through Interpretive Structural Modeling (ISM) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). Finally the effectiveness of the model is illustrated using a case study on battery manufacturing industry in India.
This study proposes a combined fuzzy grey relational analysis method based to deal with study objective. This study objective is aimed to present a perception approach to deal with supplier evaluation of environmental knowledge management capacities (EKMC) with uncertainty and lack of information. The ranking of best supplier might be a key strategic direction of other suppliers prior to EKMC. The solving procedure is as follows, (i) the weights of criteria and alternatives are described both in qualitative and quantitative information using fuzzy set theory; (ii) using a grey relational analysis to result the ranking order for all alternatives; (iii) an empirical study of supplier ranking problem in EKMC are used to resolve with this proposed approach and the result indicates that optimal supplier is with higher protection of the environmental knowledge from inappropriate or illegal use or theft (C7) and from the best alternative supplier to study its criteria ranking.
Supply chain management (SCM) has become an important management paradigm. As supply chain members are often separate and independent economic entities, a key issue in SCM is to develop mechanisms that can align their objectives and coordinate their activities so as to optimize system performance. In this paper, we provide a review of coordination mechanisms of supply chain systems in a framework that is based on supply chain decision structure and nature of demand. This framework highlights the behavioral aspects and information need in the coordination of a supply chain. The identification of these issues points out several directions of future research in this area.
In conventional data envelopment analysis it is assumed that the input versus output status of each of the chosen performance measures is known. In some situations, however, certain performance measures can play either input or output roles. We refer to these performance measures as flexible measures. This paper presents a modification of the standard constant returns to scale DEA model to accommodate such flexible measures. Both an individual DMU model and an aggregate model are suggested as methodologies for deriving the most appropriate designations for flexible measures. We illustrate the application of these models in two practical problem settings.
A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input–output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and α-level sets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.
Environmental practice in knowledge management capability (EKMC) is a complex uncertainty concept that is difficult to determine based on a firm's real situation because measuring EKMC requires a set of qualitative and quantitative measurements. A framework is proposed and uses a novel hybrid multi-criteria decision-making (MCDM) model to address the dependence relationships of criteria with the aid of the analytical network process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) in uncertainty. Fuzzy set theory is used to interpret the linguistic information in accordance with the subjective evaluation; ANP is used to analyze the dependence aspects, while DEMATEL is used to determine the intertwined relations among the criteria. The evaluation results obtained through the proposed approach are objective and unbiased for two reasons. First, the results are generated by a group of experts in the presence of motile attributes, and second, the fuzzy linguistic approach reduces the distortion and loss of information. Managers can then judge the need to improve and determine which criteria provide the most effective direction towards improvement.
A recent development in DEA (data envelopment analysis) examines the internal structure of a system so that more information regarding sources that cause inefficiency can be obtained. This paper discusses a network DEA model which distributes the system inefficiency to its component processes. The model is applied to assess the impact of information technology (IT) on firm performance in a banking industry. The results show that the impact of IT on firm performance operates indirectly through fund collection. The impact increases when the IT budget is shared with the profit generation process.
Data envelopment analysis is a mathematical programming technique for identifying efficient frontiers for peer decision making units with multiple inputs and multiple outputs. These performance factors (inputs and outputs) are classified into two groups: desirable and undesirable. Obviously, undesirable factors in production process should be reduced to improve the performance. In the current paper, we present a data envelopment analysis (DEA) model in which can be used to improve the relative performance via increasing undesirable inputs and decreasing undesirable outputs.
In this paper, an incremental quaternion-interpolation algorithm is introduced. With the assumption of a constant interval between a pair of quaternions, the cost of the interpolation algorithm is significantly reduced. Expensive trigonometric calculations in Slerp are replaced with simple linear-combination arithmetic. The round-off errors and drifting behavior accumulated through incremental steps are also analyzed.
This paper introduces a model for dealing with selecting third-party reverse logistics (3PL) providers in the presence of both dual-role factors and imprecise data. The proposed model is based on data envelopment analysis (DEA). A numerical example demonstrates the application of the proposed method.
By a linguistic variable we mean a variable whose values are words or sentences in a natural or artificial language. For example, Age is a linguistic variable if its values are linguistic rather than numerical, i.e.,young, not young, very young, quite young, old, not very old and not very young, etc., rather than 20, 21,22, 23, In more specific terms, a linguistic variable is characterized by a quintuple (L>, T(L), U,G,M) in which L is the name of the variable; T(L) is the term-set of L, that is, the collection of its linguistic values; U is a universe of discourse; G is a syntactic rule which generates the terms in T(L); and M is a semantic rule which associates with each linguistic value X its meaning, M(X), where M(X) denotes a fuzzy subset of U. The meaning of a linguistic value X is characterized by a compatibility function, c: U → [0,1], which associates with each u in U its compatibility with X. Thus, the compatibility of age 27 with young might be 0.7, while that of 35 might be 0.2. The function of the semantic rule is to relate the compatibilities of the so-called primary terms in a composite linguistic value-e.g., young and old in not very young and not very old-to the compatibility of the composite value. To this end, the hedges such as very, quite, extremely, etc., as well as the connectives and and or are treated as nonlinear operators which modify the meaning of their operands in a specified fashion. The concept of a linguistic variable provides a means of approximate characterization of phenomena which are too complex or too ill-defined to be amenable to description in conventional quantitative terms. In particular, treating Truth as a linguistic variable with values such as true, very true, completely true, not very true, untrue, etc., leads to what is called fuzzy logic. By providing a basis for approximate reasoning, that is, a mode of reasoning which is not exact nor very inexact, such logic may offer a more realistic framework for human reasoning than the traditional two-valued logic. It is shown that probabilities, too, can be treated as linguistic variables with values such as likely, very likely, unlikely, etc. Computation with linguistic probabilities requires the solution of nonlinear programs and leads to results which are imprecise to the same degree as the underlying probabilities. The main applications of the linguistic approach lie in the realm of humanistic systems-especially in the fields of artificial intelligence, linguistics, human decision processes, pattern recognition, psychology, law, medical diagnosis, information retrieval, economics and related areas.
One of the fundamental tenets of modern science is that a phenomenon cannot be claimed to be well understood until it can be characterized in quantitative terms.l Viewed in this perspective, much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior.
Although firms have been taking green supply chain management (GSCM) initiatives, it is not known whether they create value for firms. We analyze 104 announcements related to GSCM using an event study, and determine what causes statistically significant gain in stock prices for these firms. Manufacturing firms, firms with high R&D expenses, and early adopters show a strong increase in stock prices on the day of the announcement. At the same time, small firms, firms not well-known for taking green initiatives, as well as firms that are low in growth potential considerably surprise the market when they make such announcements.
Technologies have varied strengths and weaknesses which require careful assessment by the purchasers. One of the uses of data envelopment analysis (DEA) is technology selection. The traditional models of DEA do not consider dual-role factors. The objective of this paper is to use a model for selecting the best technologies in the presence of dual-role factors. The model determines whether in a technology the factors are behaving predominantly like inputs, hence the technology would benefit from having less of the factors, like outputs where more of the factors are desirable, or where they are in equilibrium. A numerical example demonstrates the application of the method. Yes Yes
Supplier selection plays a key role in an organization because the cost of raw material constitutes the main cost of the final product. Selecting an appropriate supplier is now one of the most important decisions of the purchasing department. This decision generally depends on a number of different criteria. The objective of this paper is to propose a data envelopment analysis methodology that considers both undesirable outputs and imprecise data simultaneously. The proposed model is applied in supplier selection problem. A numerical example demonstrates the application of the proposed method. Yes Yes
Cook and Zhu [Cook, W.D., Zhu, J., 2007. Classifying inputs and outputs in data envelopment analysis. European Journal of Operational Research 180, 692-699] introduced a new method to determine whether a measure is an input or an output. In practice, however, their method may produce incorrect efficiency scores due to a computational problem as result of introducing a large positive number to the model. This note introduces a revised model that does not need such a large positive number.
One of the uses of data envelopment analysis (DEA) is supplier selection. Weight restrictions allow for the integration of managerial preferences in terms of relative importance levels of various inputs and outputs. As well, in some situations there is a strong argument for permitting certain factors to simultaneously play the role of both inputs and outputs. The objective of this paper is to propose a method for selecting the best suppliers in the presence of weight restrictions and dual-role factors. This paper depicts the supplier selection process through a DEA model, while allowing for the incorporation of decision maker's preferences and considers multiple factors which simultaneously play both input and output roles. The proposed model does not demand exact weights from the decision maker. This paper presents a robust model to solve the multiple-criteria problem. A numerical example demonstrates the application of the proposed method. Yes Yes
Data envelopment analysis is used to compute a cumulative Malmquist input-based productivity index for coal-burning plants in the U.S. electric generating industry in the 1980s. The authors account for inputs used to control sulfur emissions as well as emissions outputs, and decompose the index into changes in technical efficiency, changes in technology, and changes in scale efficiency. They find that productivity decreased from 1985 to each of their first three target years but grew in the 1985-89 comparison, and that 18.5 percent of their plants, and 27 percent of net generation, lie in the decreasing returns region of the production set. Copyright 1994 by MIT Press.
In the real world there are systems which are composed of independent production units. The conventional data envelopment analysis (DEA) model uses the sum of the respective inputs and outputs of all component units of a system to calculate its efficiency. This paper develops a parallel DEA model which takes the operation of individual components into account in calculating the efficiency of the system. A property owned by this parallel model is that the inefficiency slack of the system can be decomposed into the inefficiency slacks of its component units. This helps the decision maker identify inefficient components and make subsequent improvements. Another property is that the efficiency calculated from this model is smaller than that calculated from the conventional DEA model. Few systems will have perfect efficiency score; consequently, a stronger discrimination power is gained. In addition to theoretical derivations, a case of the national forests of Taiwan is used as an example to illustrate the whole idea.
In this paper we present a quantitative model for comparing university departments concerned with the same discipline. This model is based upon ideas drawn from data envelopment analysis. Computational results are given for chemistry and physics departments in the United Kingdom.