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

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

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

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

The use of supply chain DEA models in operations management: A survey

Standard Data Envelopment Analysis (DEA) approach is used to evaluate the efficiency of DMUs and treats its internal structures as a “black box”. The aim of this paper is twofold. The first task is to survey and classify supply chain DEA models which investigate these internal structures. The second aim is to point out the significance of these models for the decision maker of a supply chain. We analyze the simple case of these models which is the two-stage models and a few more general models such as network DEA models. Furthermore, we study some variations of these models such as models with only intermediate measures between first and second stage and models with exogenous inputs in the second stage. We define four categories: typical, relational, network and game theoretic DEA models. We present each category along with its mathematical formulations, main applications and possible connections with other categories. Finally, we present some concluding remarks and opportunities for future research.Supply chain; Data envelopment analysis; Two-stage structures; Network structures

Assessing the Efficiency of Mass Transit Systems in the United States

Frustrated with increased parking problems, unstable gasoline prices, and stifling traffic congestion, a growing number of metropolitan city dwellers consider utilizing the mass transit system. Reflecting this sentiment, a ridership of the mass transit system across the United States has been on the rise for the past several years. A growing demand for the mass transit system, however, necessitates the expansion of service offerings, the improvement of basic infrastructure/routes, and the additional employment of mass transit workers, including drivers and maintenance crews. Such a need requires the optimal allocation of financial and human resources to the mass transit system in times of shrinking budgets and government downsizing. Thus, the public transit authority is faced with the dilemma of “doing more with less.” That is to say, the public transit authority needs to develop a “lean” strategy which can maximize transit services with the minimum expenses. To help the public transit authority develop such a lean strategy, this report identifies the best-in-class practices in the U.S. transit service sector and proposes transit policy guidelines that can best exploit lean principles built upon best-in-class practices

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling

Sustainability of small and medium sized enterprises (SMEs) is significant as SMEs contribute to GDP substantially in every economy. This research develops an innovative sustainable supply chain performance measurement model for SMEs. Prior researches predominantly use balanced score card (BSC) approach that presume causal relationship of criteria and Data Envelopment Analysis (DEA), which derive efficiency of units from a few input and output criteria. While DEA is effective for policymakers, BSC is more suitable for individual SME. The proposed method that uses structural equation modeling (SEM) approach to derive the relationship of criteria and criteria weights formulates regression-type models for a specific region as well as for specific SME. The SEM-based supply chain sustainability performance measurement model is beneficial to policymakers as they can determine means for improvement at a regional level. The proposed method could also facilitate managers/owners of individual SMEs with measures for improving their supply chain sustainability performance. The method has been applied to three varied geographical locations in the UK, France and India in order to demonstrate its effectiveness

DEA-based benchmarking models in supply chain management: an application-oriented literature review

Data Envelopment Analysis (DEA) is a mathematical methodology for benchmarking a group of entities in a group. The inputs of a DEA model are the resources that the entity consumes, and the outputs of the outputs are the desired outcomes generated by the entity, by using the inputs. DEA returns important benchmarking metrics, including efficiency score, reference set, and projections. While DEA has been extensively applied in supply chain management (SCM) as well as a diverse range of other fields, it is not clear what has been done in the literature in the past, especially given the domain, the model details, and the country of application. Also, it is not clear what would be an acceptable number of DMUs in comparison to existing research. This paper follows a recipe-based approach, listing the main characteristics of the DEA models for supply chain management. This way, practitioners in the field can build their own models without having to perform detailed literature search. Further guidelines are also provided in the paper for practitioners, regarding the application of DEA in SCM benchmarking

Performance Management of Supply Chain Sustainability in Small and Medium-sized Enterprises Using a Combined Structural Equation Modelling and Data Envelopment Analysis

Although the contribution of small and medium-sized enterprises (SMEs) to economic growth is beyond doubt, they collectively affect the environment and society negatively. As SMEs have to perform in a very competitive environment, they often find it difficult to achieve their environmental and social targets. Therefore, making SMEs sustainable is one of the most daunting tasks for both policy makers and SME owners/managers alike. Prior research argues that through measuring SMEs’ supply chain sustainability performance and deriving means of improvement one can make SMEs’ business more viable, not only from an economic perspective, but also from the environmental and social point of view. Prior studies apply data envelopment analysis (DEA) for measuring the performance of groups of SMEs using multiple criteria (inputs and outputs) by segregating efficient and inefficient SMEs and suggesting improvement measures for each inefficient SME through benchmarking it against the most successful one. However, DEA is limited to recommending means of improvement solely for inefficient SMEs. To bridge this gap, the use of structural equation modelling (SEM) enables developing relationships between the criteria and sub-criteria for sustainability performance measurement that facilitates to identify improvement measures for every SME within a region through a statistical modelling approach. As SEM suggests improvements not from the perspective of individual SMEs but for the totality of SMEs involved, this tool is more suitable for policy makers than for individual company owners/managers. However, a performance measurement heuristic that combines DEA and SEM could make use of the best of each technique, and thereby could be the most appropriate tool for both policy makers and individual SME owners/managers. Additionally, SEM results can be utilized by DEA as inputs and outputs for more effective and robust results since the latter are based on more objective measurements. Although DEA and SEM have been applied separately to study the sustainability of organisations, according to the authors’ knowledge, there is no published research that has combined both the methods for sustainable supply chain performance measurement. The framework proposed in the present study has been applied in two different geographical locations—Normandy in France and Midlands in the UK—to demonstrate the effectiveness of sustainable supply chain performance measurement using the combined DEA and SEM approach. Additionally, the state of the companies’ sustainability in both regions is revealed with a number of comparative analyses

Supply Chain Efficiency Analysis: A Theoretical Approach

We divide the supply chain into a central control system (CCS) case and a decentralized control system (DCS) case, and compare the supply chain efficiency with both the CCS case and the DCS case.  We find that the supply chain efficiency of a capital-intensive industry is better under the CCS case than under the DCS case.  Under the CCS case, the maximized supply chain efficiency can be reached by choosing either the lowest price that the upstream firm is willing to receive or the highest price that the downstream firm is willing to pay for intermediate goods