This paper presents an integration of balanced score card (BSE) with two-stage data envelopment analysis (DEA). The proposed model of this paper uses different financial and non-financial perspectives to evaluate the performance of decision making units in different BSC stages. At each stage, a two-stage DEA method is implemented to measure the relative efficiency of decision making units and the results are monitored using the cause and effect relationships. An empirical study for a banking sector is also performed using the method developed in this paper and the results are briefly analyzed.
Data Envelopment Analysis (DEA) has been widely used as an effective tool for measuring the relative efficiency of similar units by considering various input/output parameters. This paper examines DEA models for the estimation and improvement of organizational inputs and outputs in order to enhance management and decision making processes. We propose an empirical DEA analysis on banking sector by considering several financial and non-financial inputs and outputs. The relative efficiencies of various branches of banks are analyzed in different scenarios. The preliminary results indicate that there are some non-financial items that could significantly change the overall performance of a unit along with other financial items.
During the past few decades, there have been many evidences to believe that the stock markets around the world follow cyclical trends. In this paper, we study the cyclical trends using wavelet function based on various time windows on some major stock market indices. We use two methods of Daubechies and reverse bi-orthogonal wavelet methods and determine the optimal values of both methods. The results are used for Tehran stock exchange using the most recent ten years daily information as an empirical study. The details of our analysis on TEDPIX index for the last decade indicate that there are, at least, four trends of weekly, monthly, quarterly and yearly and the cycles would be expected to be repeated in future.
With globalization, sweeping technological development, and increasing competition, customers are placing greater demands on manufacturers to increase quality, flexibility, on time delivery of product and less cost. Therefore, manufacturers must develop and maintain a high degree of coherence among competitive priorities, order winning criteria and improvement activities. Thus, the production managers are making an attempt to transform their organization by adopting familiar and beneficial management philosophies like cellular manufacturing (CM), lean manufacturing (LM), green manufacturing (GM), total quality management (TQM), agile manufacturing (AM), and just in time manufacturing (JIT). The main objective of this paper is to propose an optimal assembly method for an engine manufacturer’s assembly line in India. Currently, the Indian manufacturer is following traditional assembly method where the raw materials for assembly are kept along the sideways of conveyor line. It consumes more floor space, more work in process inventory, more operator's walking time and more operator's walking distance per day. In order to reduce the above mentioned wastes, lean kitting assembly is suggested by some managers. Another group of managers suggest JIT assembly as it consumes very less inventory cost compared to other types of assembly processes. Hence, a Multi-attribute decision making model namely analytical hierarchy process (AHP) is applied to analyse the alternative assembly methods based on various important factors.
Designing distribution centers is normally formulated in a form of set covering where is primary objective is to minimize the number of connected facilities. However, there are other issues affecting our decision on selecting suitable distribution centers such as weather conditions, temperature, infrastructure facilities, etc. In this paper, we propose a multi-objective set covering techniques where different objectives are considered in an integrated model. The proposed model of this paper is implemented for a real-world case study of truck-industry and the results are analyzed.
During the past three decades there have been tremendous efforts on building steel factories on economic scales. The primary question is to find an economic scale for such plants which could also meet domestic demand. In this paper, we perform an empirical survey to find out whether building small steel factories are more suitable or setting up giant steel industries to meet regional demands. The results indicate that in many countries, building small steel plants based on the recent advances of technologies not only reduces the total cost of steel production but also it could significantly reduce the unnecessary transportation cost, providing cheaper labor, etc. This would lead to better competition which would increase the productivity.