Data envelopment analysis (DEA) is a non-parametric approach for measuring the efficiency of decision making units (DMUs) that use multiple inputs in order to produce multiple outputs. In most real applications, DMUs have a two-stage network process which can be used for management of organizations such as hospitals, insurance companies, banks, and etc. The data are crisp in the standard DEA mo...

This article presents results from a Delphi study on the future impact of Enterprise Resource Planning (ERP) systems on Supply Chain Management (SCM). The Delphi study was conducted with 23 Dutch supply chain executives of European multinationals. Findings from this exploratory study were threefold. First, the implications of increasing industry clockspeeds on supply chain management, as laid o...

Supply chains are ubiquitous in the manufacturing of many complex products. Traditionally, supply chains have been created through the interactions of human representatives of the companies involved, but advances in autonomous agent technologies have sparked an interest in automating the process. The Trading Agent Competition Supply Chain Management (TAC SCM) scenario provides a unique testbed ...

Data envelopment analysis technique which is developed based on the mathematical programming, evaluates the relative efficiency of a set of homogeneous decision making units. This paper shows the method of Discriminant Analysis (DA), on Imprecise Data by Data Envelopment 724 F. Hosseinzadeh Lotfi et al Analysis (DEA). DEA-Discriminant Analysis (DEA-DA) is designed to identify the existence or n...

data envelopment analysis (dea) is a technique used to compare efficiency in various sectors such as hospitals, chain stores, and dealerships. it represents a set of linear programming techniques and uses deter-ministic data (inputs and outputs), in stable conditions. the dea technique cannot be used when there is data with indeterministic nature, or when there is an environment with dynamic co...

In original data envelopment analysis (DEA) models, the data for all inputs and outputs are known exactly. When some inputs and outputs are unknown decision variables, such as interval data, ordinal data, and ratio bounded data, the DEA model is called imprecise DEA (IDEA). In this paper, We develop an alternative approach based upon slacksbased measure of efficiency (SBM) for dealing with inte...

Supply chain management (SCM) has rapidly emerged as the central field of competition in many industries and has captivated the top executives of companies in the recent past. Electronic commerce (EC) technologies have the potential to revolutionize how firms coordinate and manage their supply chain activities. This paper focuses on the emerging paradigm of Internetenabled supply chain manageme...

evaluation of corporate performance has been one of the main issues in managing decision making. nowadays, evaluation of the suppliers is a completely strategic process because organizations rarely change their suppliers. thesuppliers also do their best to remain stable in the loop of supply chain. hence, it is important to evaluate the suppliers and not something to be done once at a time. it ...

data envelopment analysis (dea) has been widely studied in the literature since its inception with charnes, cooper and rhodes work in 1978. the methodology behind the classical dea method is to determine how much improvements in the outputs (inputs) dimensions is necessary in order to render them efficient. one of the underlying assumptions of this methodology is that the units consume and prod...