نشریه مهندسی مدیریت نوین
سال نهم شماره 3 (پاییز 1402)

In this research, a framework for optimizing the oil condensate supply chain is designed using mathematical programming and simulation. Based on this framework, investment and operating costs and greenhouse gas emissions for oil and gas transmission lines can be minimized to meet pressure and transmission network needs. We can also minimize the production of pollutants in the related parts of the chain. By applying a real case study, all possible decisions are taken into account to consider the environmental aspects of the supply chain. Therefore, the structure and decisions of the supply chain are generally based on two objective functions, including the reduction of transportation and maintenance costs and pollution in treatment plants and distribution centers. The results showed that using the proposed model reduces costs by 31% and greenhouse gas emissions by 51%. There will also be an 8% increase in the capacity of fields and refineries and a 65% increase in exports. Using the results obtained from solving the model, it is possible to determine the share of each oil product in the total price and each part of the chain in the production of greenhouse gases. According to the results, oil is the most expensive and oils are the least expensive. In addition, refineries have the most impact and storage tanks have the least impact on environmental pollution.

Gas turbines are complex and expensive machines that the cost of repairing unexpected failures is very high. There are many sensors installed in each gas turbine that record and collect large amounts of data. With the data mining of such big data, failure prediction is possible before the occurrence. The data set for the present study is the recorded quantities of sensors mounted on a 9-frame gas turbine in one of the country's power plants. The one column of data matrix rows was first labeled to identify healthy and defective row in each data sample. Then, by using the Principal Component Analysis method, the dimensions of the data matrix were reduced from seven to four dimensions and the main features were extracted. Following this, a model was developed by applying Artificial Neural Network method that was able to identify fault rows in the data matrix and identify the class of the data samples as healthy or defective. Accuracy, precision, and convergence of the model for two-to-six-dimensional model reductions were studied after machine learning was performed on 80% of the data. After matrix dimensionality reduction, and feature extraction by using "Principal Component Analysis" method, our well-designed model was also able to identify and classify the fault by using "Artificial Neural Network" method. In this thesis, it was found that our mode l by combining "Principal Component Analysis" method with "Artificial Neural Network" was able to show more than 90% precision with good accuracy and maximum degree of data matrix convergence. Moreover, it was able to specify the gas turbine fault class.

Choosing the right suppliers is the basis of a successful supply chain and a key factor for improving competitiveness. Therefore, the purpose of the present study is to investigate the quality and failure rate of the parts that provided by the suppliers. In doing so,  the evaluation approach based on the failure rate was used and the reliability of the suppliers was determined and their ranking was done. As a result, the research method used is applied in nature and its method is contextual-case. Moreover, for the purpose of a case study, the suppliers of seven separate supply chains of an electrical appliance manufacturing company in Shiraz were examined that each of the chains included eight suppliers. Besides, in order to evaluate and establish the supplier's reliability model based on the failure rate, a parallel system was used. Finally, by calculating the reliability, the failure rate of the supplier's manufactured product was obtained. The results showed that this failure rate is a suitable criterion for comparing the output quality or the products of suppliers. Thus, it is possible to rate the suppliers based on this obtained model.

The purpose of the present study is to explore the dominant knowledge of the demographic characteristics of customers in choosing banks through  using associative rules in data mining. Effective decision-making and learning in a growing and complex world with with the help of  thinkers and executives is a necessary which also need employing some mechanisms to understand the structures of complex systems and mass data acquisition as well as  knowledge generation to make decisions. Most businesses identify their key customers through a variety of demographic characteristics. Businesses also target their consumers by promoting similar marketing features. Targeting consumers with similar demographic characteristics is useful for maximizing sales and profitability of the business. Banks are no exception to this rule  because they are essential elements of the economy of a country. Data mining solves this problem through  providing methods and software for automating analytics and discovering large and complex data sets. This research was conducted according to CRISP-DM standard and data were collected by questionnaire. Then, the results were converted into a database of ninety sources and after that they were extracted by using SPSS modeler software association rules for each bank. Extraction rules show how changing variables have an effect on other factors and ultimately on achieving goals.

The current research has been conducted with the aim of investigating the role of information technology in creating flexible supply chains with the mediating role of trust and industry competitiveness. This study is applied and descriptive in terms of purpose and was conducted with a survey method. The statistical sample in this study is 183 managers and employees of Ghadir Niriz Steel Company, who were selected by convenience sampling. In order to analyze the data, partial least squares method and Smart PLS-2 software were used. Convergent validity was used to measure validity, and Cronbach's alpha was used to measure reliability, and its coefficient is greater than 0.7 for all research constructs. . The obtained results indicate that information technology has a positive and significant effect on flexible supply chains respectively with the path coefficient (0.411), trust (0.582) and industry competitiveness (0.667). On the other hand, the confidence and competitiveness of the industry also has a positive and significant effect on flexible supply chains. Finally, it can be said that both trust variable and industry competitiveness play a mediating role in the relationship between information technology and flexible supply chain. Therefore, it can be said that paying attention to information technology is necessary in order to create flexibility and trust in the supply chain and competitiveness in the steel industry.

Paying attention to professional human resources in the framework of administrative competencies based on sustainable development requires continuous training based on the needs and requirements that were considered within the framework of duties and performance aimed at the development goals for the organization, one of the important priorities of organizations commercial, especially in developing countries. Therefore, the present research was conducted cross-section and with the qualitative content analysis method, with the aim of investigating the requirements of environmental care in human resources training, emphasizing the sustainable consumption policy in the Ministry of Commerce of Iraq. The participants in the research up to the theoretical saturation of the data were 15 subject matter experts in the field of environmental sustainability who were selected in a purposeful method dependent on the criteria. The data collection tool was a semi-structured interview, and the validity of the data was confirmed by an external audit. The results showed that increasing the knowledge of consumption and its political and executive antecedents is one of the main dimensions of environmental care requirements in human resources education with an emphasis on sustainable consumption through 9 main components including the development of education, consumer self-awareness, supportive policies, and sustainable incentives. , implementation of control policies, recognition of economic models, green management training, technological development and sustainable identification are known in the form of 57 sub-components.