Stochastic cost efficiency evaluation of a supply chain

The main goal of the paper is a consideration of cost efficiency evaluation models related to some supply chain when dealing with imprecise data. Data envelopment analysis (DEA) method is a non-parametric mathematical programming approach to assess the performance. This method is proposed for deterministic data and it can be generalized to inaccurate data, while considering real world applications. Here we consider data as random variables and after reviewing and introducing new models to evaluate cost efficiencies related to the special circumstances of the supply chain using DEA, these models are developed to probabilistic form. Also, deterministic and linear equivalents are proposed using the symmetric error structure of normal distributions. At final, by a numerical example, the proposed models are examined to show relationships of results.Publisher's Versio

Performance Evaluation of Supply Chain under Decentralized Organization Mechanism

Abstract Nowadays among many evaluation methods, data envelopment analysis has widely used to evaluate the relative performance of a set of Decision Making Units (DMUs). Data Envelopment Analysis (DEA(is a mathematical tool for evaluating the relative efficiency of a set Decision Making Units (DMUs), with multiple inputs and outputs. Traditional DEA models treat with each DMU as a “black box" thus, the performance measurement may be not effective. So, there are necessities for network DEA models. The objective of this paper is to propose a new network DEA model for measuring the efficiency of two- supplier and one manufacturer chains under the decentralized organization mechanism. In this mechanism, each section of supply chain is controlled under unique decision maker with his/her own interest. We proposed that, in comparison with CCR model, for the supply chain under decentralized organization mechanism, it is not appropriate to ignore the internal structure and treat as a “black box”, while there is more than one decision maker with different interests. Furthermore, the relation between the supply chain efficiency and division efficiency is investigated. Numerical example demonstrates the application of the proposed model

Decarbonising supply chain operations

The United Nations (UN) developed sustainable development goals (SDGs) in 2015 to end poverty as a global agenda for the future to protect the planet, create peace and prosperity for its population. The UN emphasises the development should be balancing environmental, economic, and social sustainability. Also, nowadays governments, customers, and stakeholders’ pressure to remark environmental and social footprints have been increased. Decarbonising and sustainability of the supply chain is one of such novel ideas involving all the business value-adding operations. This includes purchasing, upstream, and downstream supply chains, distribution and collaboration with suppliers and patrons in a way that has the least negative environmental and social effects. To minimise energy consumption and carbon emissions in the supply chain operations we need to integrate carbon efficiency in suppliers, transportation, plants, distribution centers/warehouses all the way to the market. The main objective of this study is to investigate measuring eco-efficiency of suppliers in the supply chain with data envelopment analysis (DEA). It has the potential to minimise carbon footprints in the supply chain and to address the UN sustainability goals relating to creating a sustainable supply chain in measuring technical (operational), environmental and eco-efficiency of suppliers. In this paper, we model the necessity of simultaneous application of worst and best practice DEA in measuring eco-efficiency of suppliers to minimise carbon footprint in the supply chain. This model would help organisations to balance environmental, economic, and social sustainability in the supply chain in response to the UN sustainable development goals. It is found that this proposed model can provide a more reliable evaluation and selection of right suppliers considering their environmental and other traditional criteria. We also develop an integrated approach through DEA models for measuring technical (operational), environmental and ecoefficiency of suppliers. The proposed models are applied to evaluate the eco-efficiency of a manufacturing company in an automotive industry.N/

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

Data envelopment analysis (DEA) is a well-known non-parametric technique primarily used to estimate radial efficiency under a set of mild assumptions regarding the production possibility set and the production function. The technical efficiency measure can be complemented with a consistent radial metrics for cost, revenue and profit efficiency in DEA, but only for the setting with known input and output prices. In many real applications of performance measurement, such as the evaluation of utilities, banks and supply chain operations, the input and/or output data are often stochastic and linked to exogenous random variables. It is known from standard results in stochastic programming that rankings of stochastic functions are biased if expected values are used for key parameters. In this paper, we propose economic efficiency measures for stochastic data with known input and output prices. We transform the stochastic economic efficiency models into a deterministic equivalent non-linear form that can be simplified to a deterministic programming with quadratic constraints. An application for a cost minimizing planning problem of a state government in the US is presented to illustrate the applicability 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-

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

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

Financial benchmarking of transportation companies in the New York Stock Exchange (NYSE) through data envelopment analysis (DEA) and visualization

In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution

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

A Two-Stage Value Chain Model for Vegetable Marketing Chain Efficiency Evaluation: A Transaction Cost Approach

We applied a two-stage value chain model to investigate the effects of input application and occasional transaction costs on vegetable marketing chain efficiencies with a farm household-level data set. In the first stage, the production efficiencies with the combination of resource endowments, capital and managerial inputs, and production techniques were evaluated; then at the second stage, the marketing technical efficiencies were determined under the marketing value of the vegetables for three typical marketing chains in Nanjing area, P.R. China. The impacts of the transaction costs to the supply chain technical efficiency both at the production and marketing stages were examined by using Tobit model. Study showed that transaction costs significantly impacts on vegetable marketing chain efficiency in research area. Results also revealed that the impacts of transaction costs on marketing chain efficiency differ cross chains. This paper concluded with the reduction of the various types of transaction costs incurred in the vegetable marketing chains as managerial implementations for technical efficiency improvement and farmers' income increasing.Two-stage value chain model, Data envelopment analysis, Technical efficiency, Transaction cost, Vegetable, China, Industrial Organization, D1, D8, Q13, Q18,