Structural Rearrangement for Supply Chain Efficiency: A DEA-Based Game-Theoretic Approach

Accepted by: Ali Emrouznejad The environmental efficiency of industries plays an important role in economic development of countries. Accordingly, dividing the internal network structure of industries into two sub-processes, including green and operational stages, enables decision-makers to assess both of the efficiencies simultaneously. Such assessment can be implemented using a non-parametric methodology termed data envelopment analysis (DEA). Standard DEA models consider the whole system of decision-making units (DMUs) as a single process (i.e. black-box). The black-box approach ignores modelling of the internal network structure of the assessed DMUs. This issue tackled by network DEA models since it considers the internal network structure of DMUs. In the network DEA, the efficiency evaluation of system stages is essential to identify its overall efficiency, resulting to a multi-objective optimization problem. Therefore, the network DEA is a widely welcomed methodology proposed for solving multi-objective problems. This paper assesses the operational and environmental efficiencies of a network structure system by converting the multi-objective optimization problem into a linear single objective function. In this investigation, a technique of tri-objective function problem is proposed. The proposed technique transforms into a single objective function by keeping one objective function and shifting the other two objective functions into the model’s constraints. The applicability and usefulness of the proposed technique have been tested using a data set of 20 industries. The developed approach provides valuable evaluations to decision-makers to rank DMUs by considering their green and operational efficiency simultaneously.

Purpose – In investigating the performance of multidivisional organizations, ability to account for each division's importance and contribution enhances the deftness of resource allocation and targeting desired outcomes. With this motivation, the author aims to introduce network data envelopment analysis (NDEA) from operations research in this conceptual article and discuss how two articles from this journal can be extended using this approach. Design/methodology/approach – NDEA was first developed to deliver a more in-depth understanding of underlying sources of operational inefficiency. Thus, NDEA can be viewed as a peer benchmarking method useful in comparing performance of organizations and identifying divisional inefficiencies that may detract from overall performance. NDEA's ability to capture interactions among multiple variables in an objective manner based on actual observed data rather than sample averages is one of its key advantages. Findings – The author discusses how NDEA can be applied in organisational analysis by examining two articles from this journal. Briefly commenting on one of the cases here, the author shows that a network can be defined as the interacting divisions of cultural norms and structural forms. The potential improvements (i.e. horizontal re-alignment) indicated by NDEA can guide management on the extent organisational alignment that could be changed in reaching strategic aims. The author's theoretical model is conducive to assessing the amount and direction of change from the proposed alignment model in a multi-criteria framework – characteristics embraced by NDEA. Practical implications – Given the hierarchical nature of organizations where employees are nested in work groups or teams, groups nested in departments or divisions, and divisions nested in organizations, application of NDEA at various levels of analysis is feasible. Originality/value – NDEA's ability to account for each division's importance or assign desired weights in what-if analyses adds to flexibility in managerial decision-making regarding allocation of resources, or re-alignment of processes and targeting of desired outcomes. Such a method that does not assume independence among multiple performance measures provides additional assurance to those concerned about shortcomings of additive scales in complex organizations.

This paper proposes that data envelopment analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics in performance evaluation and benchmarking. While computational algorithms have been developed to deal with large volumes of data (decision making units, inputs, and outputs) under the conventional DEA, valuable information hidden in big data that are represented by network structures should be extracted by DEA. These network structures, e.g., transportation and logistics systems, encompass a broader range of inter-linked metrics that cannot be modelled by conventional DEA. It is proposed that network DEA is related to the value dimension of big data. It is shown that network DEA is different from standard DEA, although it bears the name of DEA and some similarity with conventional DEA. Network DEA is big data enabled analytics (big DEA) when multiple (performance) metrics or attributes are linked through network structures. These network structures are too large or complex to be dealt with by conventional DEA. Unlike conventional DEA that are solved via linear programming, general network DEA corresponds to nonconvex optimization problems. This represents opportunities for developing techniques for solving non-linear network DEA models. Areas such as transportation and logistics system as well as supply chains have a great potential to use network DEA in big data modeling.

PurposeConsidering the fact that an efficient bank is able to allocate its resources effectively, reduce its operating costs and maximize profitability, this study investigates the technical, allocative, and cost efficiency (TE, AE, CE) of private commercial banks (PCBs) in Bangladesh from 2017 to 2023 to gain insight into the current condition of Bangladesh’s private banking industry and provide policy recommendations for managers. The paper also determines a few determinants of banking efficiency. Due to the unavailability of data, 39 banks out of 43 private commercial banks were observed.Design/methodology/approachThe study used the data envelopment analysis (DEA) and the modern network DEA (NDEA) as the research tools. The input variables include total deposits, fixed assets, and personnel expenses with their prices, while the output variables consist of total loans and off-balance sheet items for DEA. Total deposits were considered as an intermediary product in NDEA. In addition, a Bayesian Tobit Regression Analysis is conducted to define the determinants of banking efficiency in conjunction with Tobit Censored Regression Analysis. The predictors selected for the analysis are bank size, share of loans, profitability, cost management ability, and credit risk.FindingsThe DEA results suggest that Bangladesh’s PCBs performed well in terms of TE, AE, and CE during the study period, averaging 91%, 94%, and 86%. The NDEA model, on the other hand, shows the average scores of 54%, 73%, and 38%, respectively. The determinants of TE are the bank size, share of loans, and cost management ability, while only the first two act as determinants of CE. There is no evidence that these variables act as determinants of AE. The findings have some limitations due to issues with the model’s goodness of fit.Originality/valueBy comparing the DEA and NDEA scores, this study contributes to the body of research by looking at how efficient Bangladesh’s private commercial banking sector is as a whole while also defining the determinants of efficiency. This study differs from other studies as it applies frequentist and non-frequentist statistical methods for identifying banking efficiency determinants.

Purpose: In investigating the performance of multidivisional organizations, ability to account for each division's importance and contribution enhances the deftness of resource allocation and targeting desired outcomes. With this motivation, we introduce network data envelopment analysis (NDEA) from Operations Research in this conceptual article and discuss how two articles from this journal can be extended using this approach. Design/methodology/approach: NDEA was first developed to deliver a more in-depth understanding of underlying sources of operational inefficiency. Thus, NDEA can be viewed as a peer benchmarking method useful in comparing performance of organizations and identifying divisional inefficiencies that may detract from overall performance. NDEA’s ability to capture interactions among multiple variables in an objective manner based on actual observed data rather than sample averages is one of its key advantages. Findings: We discuss how NDEA can be applied in Organisational Analysis by examining two articles from this journal. Briefly commenting on one of the cases here, we show that a network can be defined as the interacting divisions of cultural norms and structural forms. The potential improvements (i.e., horizontal re-alignment) indicated by NDEA can guide management on the extent organisational alignment could be changed in reaching strategic aims. The author’s theoretical model is conducive to assessing the amount and direction of change from the proposed alignment model in a multi-criteria framework – characteristics embraced by NDEA. Practical implications: Given the hierarchical nature of organizations where employees are nested in work groups or teams, groups nested in departments or divisions, and divisions nested in organizations, application of NDEA at various levels of analysis is feasible. Originality/value: NDEA’s ability to account for each division's importance or assign desired weights in what-if analyses adds to flexibility in managerial decision-making regarding allocation of resources, or re-alignment of processes and targeting of desired outcomes. Such a method that does not assume independence among multiple performance measures provides additional assurance to those concerned about shortcomings of additive scales in complex organizations.

<p>Recently Internet of things (IoT) and its applications are emerging as a momentous trend in industry. Numerous hardware and software providers have been entering the intense market competition in IoT related industries. Correspondingly, the attention on evaluation of IoT industry is growing. However, it is a main theme as how to consider the multiple dimensions and dependencies among the criteria in IoT supply chains simultaneously. By considering internal processes in DMUs as well as their interactions, this study designs the evaluation methods with network data envelopment analysis (NDEA) and multi-objective programming (MOP) techniques. This work intends to estimate the efficiency of IoT businesses from the perspectives of R&D, manufacturing, sales and finance, and the overall performance. The proposed models are implemented with empirical case studies in IoT supply chains in Taiwan. The results show the usefulness and validity of the proposed methods in evaluating IoT related business.</p> <p> </p>

PurposeThe purpose of this paper is to determine the level of efficiency in the Mexico electricity industry during the 2008-2015 period.Design/methodology/approachA data envelopment analysis (DEA) network model is proposed, where technical efficiency is calculated. A factorial analysis using the principal components method was carried out first. Later, latent dimensions were calculated through the variance criterion and sedimentation graph, where four components were presented. After performing factor rotation, the nodes were grouped: generation, transmission, distribution and sales. It proceeded later to structure a DEA network model.FindingsFrom the calculations made, the most efficient node was the transmission, while the North Gulf and East Center divisions were the only efficient.Research limitations/implicationsThe limitations presented in this study were data collection.Practical implicationsThe implications that were observed were that through the results obtained, proposals can be made to the Mexican electricity sector to improve each of the nodes, and have a better operation and reduce energy losses.Social implicationsThe social impact of this type of study is that based on the results obtained, they present the basis for improving energy policy and users can have a better service that has better quality and coverage.Originality/valueThe originality of this study consists in the use of two methodologies, factor analysis methodology and DEA network model.

In view of the defect that network DEA (data envelopment analysis) can not reflect the network structure when it comes to dynamic evaluation, we proposed a two-stage evaluation method of dynamic network DEA. Time parameter was introduced to network DEA and dynamic network DEA model was established. In order to evaluate the efficiency of dynamic network DEA in several time spans, we built a two-stage evaluation method. In the first stage, dynamic network DEA efficiency matrix was formulated. In the second one, a new input-output DEA unit was set up to evaluate the synthetical efficiency of dynamic network DEA. The two-stage method can manifest the real dynamic property in network DEA, as well as consider the network structure which involves intermediate products by dynamic measure. A numerical example indicated that the two-stage evaluation method can solve dynamic network DEA problem efficiently, it can also provide improved information between inefficient DMU and optimum values by slacks. The new measure can be a good tool of systems analysis.

Single case studies are at the origin of both theory development and research in the field of psychoanalysis and psychotherapy. While clinical case studies are the hallmark of psychoanalytic theory and practice, their scientific value has been strongly criticized. To address problems with the subjective bias of retrospective therapist reports and uncontrollability of clinical case studies, systematic approaches to investigate psychotherapy process and outcome at the level of the single case have been developed. Such empirical case studies are also able to bridge the famous gap between academic research and clinical practice as they provide clinically relevant insights into how psychotherapy works. This study presents a review of psychoanalytic empirical case studies published in ISI-ranked journals and maps the characteristics of the study, therapist, patient en therapies that are investigated. Empirical case studies increased in quantity and quality (amount of information and systematization) over time. While future studies could pay more attention to providing contextual information on therapist characteristics and informed consent considerations, the available literature provides a basis to conduct meta-studies of single cases and as such contribute to knowledge aggregation.

Performance measurement deals with ongoing monitoring and evaluation of the operations of the organizations so as to be able to improve their productivity and performance. Thus, the adoption of performance evaluation methods is necessary, which are capable of taking into account all the environmental factors of the organization, identifying the inefficient production processes and suggesting adequate ways to improve them. Such a method is Data Envelopment Analysis (DEA), which is the most popular non-parametric and data driven technique for assessing the efficiency of homogeneous decision making units (DMUs) that use multiple inputs to produce multiple outputs. The DMUs may consist of several sub-processes that interact and perform various operations. DEA has a wide application domain, such as public sector, banks, education, energy systems, transportation, supply chains, countries and so forth. However, the classical DEA models treat the DMU as a “black box”, i.e. a single stage production process that transforms some external inputs to final outputs. In such a setting, the internal structure of the DMU is not taken into consideration. Thus, the conventional DEA models fail to mathematically represent the internal characteristics of the DMUs, as well as they fall short to provide precise results and useful information regarding the sources that cause inefficiency. To consider for the internal structure of the DMUs, recent methodological advancements are developed, which extend the standard DEA and constitute a new field, namely the network DEA. The network DEA methods are capable of reflecting accurately the DMUs’ internal operations as well as to incorporate their relationships and interdependences. In network DEA, the DMU is considered as a network of interconnected sub-units, with the connections indicating the flow of intermediate products. In this chapter, we describe the underlying notions of network DEA methods and their advantages over the classical DEA ones. We also conduct a critical review of the state-of-the art methods in the field and we provide a thorough categorization of a great volume of network DEA literature in a unified manner. We unveil the relations and the differences of the existing network DEA methods. In addition, we report their limitations concerning the returns to scale, the inconsistency between the multiplier and the envelopment models as well as the inadequate information that provide for the calculation of efficient projections. The most important network DEA methods do not secure the uniqueness of the efficiency scores, i.e. the same level of overall efficiency is obtained from different combinations of the efficiencies of the sub-processes. Also, the additive efficiency decomposition method provides biased efficiency assessments. Finally, we discuss about the inability of the existing approaches to be universally applied on every type of network structure.

Conventional data envelopment analysis (DEA) models consider a system as a single-process ‘black box’. There are, however, DEA approaches that consider a system as composed of distinct processes or stages, each one with its own inputs and outputs and with intermediate flows among the stages. In this paper, a network DEA approach to airline efficiency assessment is presented. One conclusion of the study is that the network DEA approach has more discriminative power than the single-process DEA approach and that the computed targets, efficiency scores and rankings are more valid. This is because network DEA allows for a more fine-grained analysis that leads to a more realistic estimation of the overall system production possibility set than the one assumed by conventional DEA. In other words, compared with network DEA the conventional, single-process DEA represents an aggregated analysis that merges all system processes with their inputs and outputs and ignores their internal flows. The main drawbacks are the need for more detailed data (i.e. at the process level) and the greater complexity of the resulting models, especially if there are inputs or outputs that are shared among the processes.

Article history: Received February 9, 2015 Received in revised format: May 12, 2015 Accepted June 2, 2015 Available online June 3 2015 Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). 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. Recently, DEA has been extended to examine the efficiency of network structures, where there are lots of sub-processes that are linked with intermediate parameters. These intermediate parameters can be considered as the outputs of the first stage and simultaneously as the inputs for the second stage. In contrast to the traditional DEA analysis, network DEA analysis aims to measure different sub-processes’ efficiencies in addition to the total efficiency. Lots of network DEA technique has been used recently, but none of them uses Analytic Hierarchy Process (AHP) in network DEA for assessing a network’s efficiency. In this paper, AHP methodology is used for considering the importance of each sub-process and network DEA is used for measuring total and partial efficiencies based on the importance of each department measured from AHP methodology. In this regard, the case of Iranian Handmade Carpet Industry (IHCI) is used. Growing Science Ltd. All rights reserved. 5 © 201

The use of weight restrictions in data envelopment analysis (DEA) is considered an appropriate method to avoid zero inputs and outputs weights. Due to a number of problems associated with the use of weight restrictions, the production trade-off method in DEA is often preferred. An important issue is that in most common DEA models, the internal structure of the production units is ignored, and the units are often considered as black-boxes. The current study aims to estimate the production trade-offs in two-stage network data envelopment analysis (NDEA) to observe its likely impact on efficiency evaluation and discrimination of units and subunits. Also, the probable effect of such the trade-offs are shown on the overall efficiency decomposition to divisional efficiencies and production possibility set (PPS) in two-stage NDEA. Finally, a numerical example is used to explain the results and compare the possible effects of different production trade-offs scenarios in two-stage NDEA with standard DEA models.

Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.

Evaluation of cloud service industry with dynamic and network DEA models