Management Research in Iran

Abstract Outsourcing has been the most common method of cooperation. Today, issues such as increased competition, limited resources, technological complications, uncertainty about the future and increased costs have caused organizations to reconsider their management model and turn to new strategies. In such a situation, strategic alliances can replace outsourcing. The purpose of this article is to design a model to evaluate the current state of an outsourcing and to investigate the possibility of developing it into a strategic alliance. For this purpose, in this research, by studying the literature on the subject, examining examples of strategic alliances and also interviewing experts, a list of effective criteria on the formation of a strategic alliance was prepared and categorized into 6 dimensions. Then, using the content validity ratio method, unnecessary criteria were identified in this list. After that, using fuzzy data envelopment analysis without explicit input method, the best combination of weights for essential criteria was obtained. In the next step, by specifying the minimum values for the formation of a strategic alliance for each of the criteria and dimensions of the model, it is possible to compare the current state of the contractor with the minimum value necessary for the formation of a strategic alliance. Results indicates that through this comparison, organizations can identify their weak points in order to become a strategic ally and plan to improve these criteria and, as a result, succeed in developing strategic allies.

Abstract Nowadays, simultaneous consideration of cardinal and ordinal data in supplier selection process is highlighted more than ever as production philosophies such as just-in-time production are being widely used. Many supplier selection optimization models traditionally presume that the average price of respective expenditures is constant. However, the reality is far from this. As a matter of fact, the suppliers often offer volume discounts in order to encourage buyers to place more orders. This paper presents an innovative approach for selecting the best suppliers in volume discount environments and in the presence of both cardinal and ordinal data. The efficiencies of the suppliers under evaluation are, at first, obtained using optimistic and pessimistic views. The optimistic view evaluates each supplier via a set of most favorable weights. The efficiencies measured by the optimistic view are called “optimistic efficiencies”. The pessimistic view evaluates each supplier via a set of most unfavorable weights. The efficiencies measured by this view are called “pessimistic efficiencies”. It is then demonstrated that these two evaluation results contradict one another, and are undoubtedly biased, non-realistic, and unconvincing. To overcome this problem, a new general performance measure is proposed, which is utilized for integrating the measures obtained from the optimistic and pessimistic views. It will be used for identifying the supplier with the best performance. A numerical example will show the application of the proposed method.

Abstract The necessity of restructuring university budgeting from program-based to performance-based budgeting (PBB) leads many studies to be conducted. Reviewing the literature, we observed that there is no mathematical model including Dual PBB Structure in University. The purpose of this paper is to construct a PBB model for university so that the budget is allocated not only to plans based on their priorities, but also to faculties based on approved student per Capita by the Ministry of Sciences, Researches and Technology. Considering the various criteria in the university system and because of fuzzy and stochastic uncertainties in the problem parameters, two scenarios were analyzed. 1) Crisp Lower Bound Robust Fuzzy PBB (CLB-RFPPB) model 2) Fuzzy Lower Bound Robust Fuzzy PBB (FLB-RFPPB) model. A significant point in Both of them, is to use performance Indicators--calculated based on data envelopment analysis approach (DEA) regarding a basic model of input-oriented CCR— determined the importance coefficient for each educational groups in order to allocateing budget. In addition, the weights of Goals and the priority of each plan were determined using some paired comparison questionnaires completed by experts. The PBBGP model has 5 goals, 1142 constraints and 994 decision variables. The results of models solution- that are presented on two macro and Operational Levels- and simulation of Crisp and two RFPBB models, Demonstrate high level capabilities of RFPBB models in response to the uncertainty in the parameters of problem and also managing the level of decision risk

Abstract Abstract Selecting a portfolio has always been a significant issue in Financial Management. The models presented for selecting the best portfolio have some deficiencies and after some time, their deficiencies would be revealed and they will be replaced by some other models. One of the problems with those models is neglecting the multifaceted indices and dimensions for final evaluation of portfolio, and these efficiencies will bring the validity of the evaluation results under question. In order to remove these efficiencies, one can use DEA (Data Envelopment Analysis) technique which is one of the MCDM (Multi-Criteria Decision Making) techniques. In this paper, two models have been presented; one finds the most efficient portfolio and the other one finds the most inefficient. In this paper, 95 companies now present in Tehran Stock Market have been investigated. The results demonstrate that out of those 95 companies, seven companies are efficient and 8 companies are utterly inefficient. Keywords: Data Envelopment Analysis, Multi-Criteria Decision Making (MCDM), Portfolio

Abstract With the increasing pace of science and knowledge in today, s world, the company in order to comply with the condition environment variable to use communication technology and capital investment in this technology is growing rapidly. Performance evaluation is one of the main concerns for monitoring this agreement. Recently applied the theory of data envelopment analysis (DEA) and grey theory for performance evaluating has been highly regarded. Considering the importance of telecommunications as a leader in the field of communication technology and necessary to evaluate the performance of these two techniques have been used in this study. For this mining first set of criteria in evaluating the financial performance of telecommunication and the importance (weight) each was determined using the number of grey. Then, technique of using DEA model for evaluating and ranking the telecommunications companies were presented. The result indicates that the major telecommunication companies province in Markazi, Tehran and Khuzestan have the best financial performance.

Abstract Supplier selection is a complex but important decision that requires careful review of various performance criteria. Traditionally, models of supplier selection have been based on cardinal data, with less emphasis on ordinal data. However, with the widespread use of production methods, such as the just-in-time method, recently more emphasis is placed on considering imprecise data—i.e., mixtures of interval and ordinal data. This article proposes to use data envelopment analysis (DEA) with double frontiers for supplier selection, a methodology which considers not only the optimistic efficiency but also the pessimistic efficiency of each supplier. Compared with the traditional DEA, the DEA approach with double frontiers can identify the best supplier accurately and easily without having to impose any weight limitations. A numerical example will be examined using the DEA approach with double frontiers to illustrate its simplicity and convenience for supplier selection and justification.

Abstract The Assessment of Financial Distress in Tehran Stock Exchange: A Comparative Study Between Data Envelopment Analysis (DEA) and Logistic Regression (LR) Mohammad Reza Rostami1, Mirfeyz Fallahshams2, Farzaneh Eskandari3 1- Assistant Professor, Department of Management, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran 2- Associate Professor, Department of Management, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran 3- Msc., Department of Management, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran Received: 5 /9/2010 Accept: 13/8/2011 Financial distress evaluation is important because firm failure imposes significant direct and indirect costs on a firm’s stakeholders. Hence, using financial ratios has been considered by bank loan officers, creditors, stockholders, financial analysts, and the general public in order to provide them with timely and accurate assessment. Timely evaluation can help decision makers to find the optimal way and predict bankruptcy. There are different models for financial distress evaluation, which are mainly applied in decision making by financial market players. It has been attempted to improve the accuracy of these models by more developed techniques. The main goal of this research is to examine the capability of the additive model of Data Envelopment Analysis (DEA) model in assessing corporate financial distress by comparing it with logistic regression (LR). The results showed that in within-sample evaluation, LR outperforms DEA (Additive model) in correctly identifying the default firms significantly.

Abstract This research has been done with the aim of identification of the effective factors that influence credit risk and designing a model for the credit rating of the legal clients of Tejarat Bank in 2003-2004 by using Data Envelopment Analysis. For this purpose, the necessary sample data on financial and nonfinancial information of 146 companies (as random simple) was selected. In this research, 27 explanatory variables (including financial and non-financial variables) were identified and examined. Finally, with the application of factor analysis and Delphi method, 8 variables, which had significant effect on credit risk, were selected and entered into the DEA model. Efficiency of the companies was calculated by using these variables. Then the model validity was measured by regression analysis. The DEA credibility scores represented the dependent variables while the 8 ratios used were considered as independent variables. The findings of the research showed that 25 companies stand on the border of efficiency. Also with one exception (owners equity/ total asset), ’all variables had the expected direction α = %5 . Research conclusions confirmed the hypothesis of DEA model’s efficiency on credit rating of the companies who have taken credit facilities from branches of Tejarat Bank in Tehran city.

Abstract Change is the main characteristic of new business era. Confronting this changing environment, Agile Manufacturing System (AMS) as the most advanced production paradigm, is emerging in the production world. Attention to this concept has resulted in developing various methods of assessing organizational agility as an important part of agility literature. These methods have a common deficiency in methodology; the lack of considering the amount of need for agility in assessing organizational agility. All researches in this area measure only the capability of agility. In fact, the intensity of environmental change determines the extent of need for agility. This necessity in turn defines the level of agility capability required for responsiveness. To obviate the cited shortcoming, a method should be developed to consider the capability of agility (output) against the extensity of change (input). This paper presents a modified Data Envelopment Analysis (DEA) model to assess the relative organizational agility. Then, this new model is used for assessing the agility of 20 production organizations from 5 different industries. Finally, agility efficiency and efficiency path for organizations are mapped.

Abstract In this paper, with the priority of measuring and promoting productivity in energy sector, we presented a model for evaluating the technical efficiency of oil refineries, as huge producers of energy and different fuels. The basis of evaluation in this research was Math modeling by using new and advanced operational research techniques such as data envelopment analysis, goal programming, analytical hierarchy process and designing combined models. The formulating process was made under two different scenarios and assumptions: consumption-production and cost-income. The final results ranked the order and sort of each refinery as a decision making unit. In addition to calculation of each unit’s score, we also made a comparison between the units, their weaknesses and the favorite score of each unit in real situation. This research focuses on the optimized utilization of refining processes with less refining cost and ability to make more valuable products considering the present availabilities and capacities

Abstract Total Factor Productivity (TFP) index is the rate of transformation of total input into total output and Total Factor Productivity Growth (TFPG) is used to analyze productivity performance in multiple time periodso, Also, TFPG index is one of the major sources of economic development, thus understanding the factors affecting productivity is very important. The main purpose of productivity measurement during multiple periods is to identify areas of improvement in activities as well as to aid strategic decision making or continuous improvement. Therefore, information about effective factors on productivity changes is useful. This paper presents a general view to the productivity growth indexes and their qualities. Then, a new method for evaluating the productivity growth is proposed. This method will be constructing by the combination of DEA and Tornqvist productivity index to measure TFPG and its decompositions. This paper also evaluates productivity growth and finds out the factors affecting productivity which are critical for the National Iranian Oil Company (NIOC) on the years 1977-2000. The obtained results showed that growth in efficiency is dominant source of change in productivity at NIOC.