JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES
Year:2013 | Volume:11 | Issue:30|Papers Count:7

In the last few years, supply chain management becomes more important, because of the globalization of business. By increasing complexity, level of uncertainty and risk in the chain goes up. Hence supply chain risk management has become a major issue in the organization. One of the risks existing in the supply chain is risk of suppliers. This research provides model for predicting supplier risk in Iran Alloy Steel Company that is then analyzed using Artificial Neural Networks which are capable to consider non-liner interrelations among criteria. In the model using fuzzy Delphi, seven criteria have been identified. Then by using AHP-VIKOR the risk of supplier calculated and the risk of suppliers were predicted. Finally, we use sensitive analysis for identification effect of every input on output.

Inventory classification is one of important techniques in inventory control context. Managers have to classify inventories because of their variety and high volume. So a stream of research has been to attempt to find methods that increase the management control by determining the number of inventory classes. In this paper the multiple objective particle swarm optimization algorithm has been used. This algorithm has been presented by Chi-Yang Tsai and Szu-Wei Yeh in 2008. Multiple objective particle swarm optimization algorithm is an evolutionary algorithm that enables the management to optimize multiple objectives simultaneously. Minimizing costs of inventory holding and ordering and maximizing inventory turnover ratios are this model’s objectives. We write the software program of this model and then test it on a sample of 100 items. Results show that this algorithm can decrease costs of holding & ordering and also increase the inventory turnover ratios significantly.

To achieve a competitive edge in the rapidly changing business environment, companies must align with suppliers and customers to streamline operations, as well as working together to achieve a level of agility beyond individual companies. Consequently, agile supply chains are the dominant competitive vehicles. Due to the ambiguity of agility assessment, most measures are described subjectively using linguistic terms. In this research, it is identified different dimensions of the agility. It is studied how the PISHRANEH Company accesses the agility in its supply chain as a case study. The innovation of this paper is the simultaneous use of Fuzzy rule-based and the Fuzzy agility index approaches which it is applied for the first time in such articles. Finally, it is represented some suggestions for the progress of agility level of the supply chain of the company and some for the future.

Due to the sharp rise of the information technology (IT), the amount of data stored in databases is dramatically on the rise. Analyzing the stored data and converting it to information and knowledge which is applicable in organizations requires powerful instruments. As with other economic sectors, recognizing and attracting low-risk and profitable customers are of high significance for insurance industry. Car insurance is one of the most important insurance branches which accounts for a great deal of portfolio of insurance industry. Risk segmentation of policyholders on the basis of observable features can help insurance companies to reduce loss, raise the rate of insurance coverage, and prevent them from making an inappropriate choice in the insurance market. In this study, the segmentation of comprehensive car insurance customers on the basis of risk was selected through self-organizing map and K-means. At first, the effective factors on the risk of policyholders are identified. Then, the insurance policyholders are segmented using the proposed SOM and K-means. Customers’ characteristics in every cluster are identified. Finally, the two methods compared with each other. The advantages and disadvantages of them illustrated.

In this study, first by study different sources, effective factors of successful mplementation of knowledge management systems have been identified. Then, by means of statistical analysis and SPSS software status of each factor in the Tamin Company was evaluated in order to determine is the company ready to implement this system, or not. The results show that company is not ready to implement this system so it is necessary for the company to improve its situation before implementing such system. Among those factors, culture, information and communication technology, have respectively the highest and lowest priorities to make correction.

The importance of having information system in any organization is obvious. It is not for sure having software & hardware that made a system profitable. It is when system achieves it predetermined goals and users benefit from it. The success implementation of master information system has been questioned through a questionnaire from all system users. Ask them to answer questions regard before system. Results show promotion in six questioned indicators. In this research, ISACO is a case.

In this paper, the critical parameters of a method of welding with shielding gas arc welding (GMAW) are discussed; this method is an important process in creating high quality metal permanent connections in various industries, including the automobile industry to improve the quality of stem diameter welding parameters. One of the most useful techniques for modeling and solving the problems is Response Surface Method. In this paper, considering five most important factors such as speed welder, torch angle with the work piece, electrode diameter, wire speed, gas consumption, and CO2 levels as input variables, can be controlled independently from the level of response, the relationship between the input variables and the response variables were determined using linear regression. Then optimum value for each factor was calculated using non-linear programming model to evaluate the results obtained along with the comparison of output of the Simulation Annealing Algorithm. In this study, both qualitative and quantitative variables are considered to evaluate and optimize all response variables regarding that these variables are not the same, and then fuzzy set theory and LP metric are used to find answers for multi-objective optimization methods.