Industrial Management Studies

Abstract 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

Abstract 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.

Abstract 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.

Abstract 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

Abstract In this study, first by study different sources, effective factors of successful
implementation 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.

Abstract 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.

Abstract Abstract
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.