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The competition increscent urgency in the global economy causes many organizations focus more on novel management ideas in constructing, delivery services and their productions. In current conditions, supply chain and the importance of a suitable performance evaluating system in order to understand the current condition and program for its improvement, has a very special importance in creating surplus value for clients. In this research, the performance of 7 active supply chains in tile industry which have similar supply chain construction, including providers, producers, distributors and clients, by evaluating producers who perform a key role in chains and by using data envelopment analysis method are considered. According to effectiveness of inputs on outputs of organizations, outputs are considered as functions of inputs and finally the relative performance amounts of decision making units, the key companies of chain supply are computed.

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Strategic alliance promotes enterprise resources sharing and enhances the competitiveness of the marketplace. Therefore, finding a mutually beneficial partner to make a strategic alliance is an important issue for various industries. The aim of this paper is to propose a suitable method based on Grey theory and Data Envelopment Analysis (DEA). A method predicts future business and measure operation efficiency, by the use of critical input and output variables. From this, firms can find out their appropriate candidates. This research was implemented with realistic public data from four consecutive financial years (2009-2012) of twenty Auto Manufactures. The study tries to help target firm find the right alliance partners. The results show the most priori candidates in recent years. The study will be of interest for managers of Auto Manufacture in utilizing alliance strategy.

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Material selection is a challenging issue in manufacturing processes while the inappropriate selected material may lead to fail the manufacturing process or end user experience especially in high-tech industries such as aircraft and shipping. Every material has different quantitative and qualitative criteria which should be considered simultaneously when assessing and selecting the right material. A weighted linear optimization method (WLOM) in the class of data envelopment analysis which exists in literature is adopted to address material selection problem while accounting for both qualitative and quantitative criteria. However, it is demonstrated the adopted WLOM method is not able to produce a full ranking vector for the material selection problems borrowed from the literature. Thus, an augmented common weight data envelopment analysis model (ACWDEA) is developed in this paper with the aim of eliminating deficiencies of WLOM model. The proposed ACWDEA is able to produce full ranking vector in decision making problems with less computational complexities in superior to the WLOM. Two material selection problems are solved and results are compared with WLOM and previous methods. Finally, the robustness and effectiveness of the proposed ACWDEA method are evaluated through Spearman’s correlation tests.

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A fundamental problem is the use of DEA in multistep or multilevel processes such as supply chain, lack of attention to processes’ internal communications in a way that the recent studies on DEA in the context of serial processes have focused on closed systems that the outputs of one level become the inputs of the next level and none of the inputs enter the mediator process. The present study aimed to examine the general dimensions of an open multilevel process. Here, some of the data such as inputs and outputs are supposed to leave the system while other outputs turn into the inputs of the next level. The new inputs can enter the next level as well. We expand this mode for network structures. The overall performance of such a structure is considered as a weighted average of sectors’ performance or distinct steps. Therefore, this suggested model in this study, not only provides the possibility to evaluate the performance of the entire network, but creates the performance analysis for each of the sub-processes. On the other hand, considering the data with undesirable structure leads to more correct performance estimation. In the real world, all productive processes do not comprise desirable factors. Therefore, presenting a structure that is capable of taking into account the undesirable structure is of crucial importance. In this study, a new model in the DEA by network structure is offered that can analyze the performance considering undesirable factors.