نشریه مدیریت راهبردی در سیستم های صنعتی
پیاپی 51 (بهار 1399)

Today,, the process of designing, producing products, and providing services are very fast. However,, despite the mentioned development,, there are some sorts of challenges for societies in terms of social health and coping with corona. Also, these challenges make some concerns with service design and industrial design disciplines. Therefore, in order to solve the recent problem in this case-descriptive study was done. In this regards, 200 questionnaires which filled out by participants as volunteer samples, in which 171 valid and related questionnaires were analyzed. Also, the registered documents for the prevalence of corona in the country based on the Iran Ministry of Health report was considered. According to the author's experience and the mentioned gathered data, the author recommended a new paradigm entitled post-service design to cope with Corona (Covid-19) related problems The proposed paradigm is an integrated model based on three fields of macro-ergonomics, service design and persuasive design.

Bullwhip effect that occurs in the chain, leads to inefficiencies such as excess inventory and overdue orders during the chain. These problems can be reduced by appropriate predictions. Forecasting must be done in all levels of a supply chain. This paper addresses the problem of optimal combination of forecasting to reduce the bullwhip effect in the four-level supply chain. For this purpose, a four-level supply chain is considered. One of the methods such as moving average, exponential smoothing, linear regression and multilayer perceptron artificial neural network can be considered for predicting in each level. First, the desired supply chain is simulated for this means. The different combinations of aforementioned forecasting methods are calculated. Then a combination of forecasting methods according to minimized bullwhip effects is selected. Finally, the results are analyzed by variance analysis model. Two combinations have the lowest bullwhip effects. Moving average, neural networks, exponential smoothing and linear regression for levels: retailer, wholesaler, manufacturer and supplier respectively as an answer and the other is: moving averages, exponential smoothing, neural network and linear regression in the same mentioned levels and other combinations have less utility.

Maintenance systems are critical to achieving the goals of the organization. The main purpose of these systems is to optimize machinery's ability to maximize production and reduce its erosion and malfunction. Accordingly, one of the main concerns of managers in companies is selecting the appropriate strategy for maintenance of equipment and machinery. This research presents novel method for selecting optimal maintenance strategy based on Fuzzy Analytical Network Process and Fuzzy Multi-Choice Goal Programming. The method of calculating the fuzzy weights of elements is explained by FANP method and then by de-fuzzy obtained weights and writing the ideals and the objective function and applying the FMCGP approach maintenance strategies will be prioritized. Finally, in the form of a case study, the optimal maintenance strategy among Preventive Maintenance, Reliability base maintenance, Condition base maintenance, Time base maintenance, corrective maintenance and Design-out maintenance for the four selected machines in Mashhad Milk Powder Company is considered. In this regard, by literature review six criteria and thirty sub-criteria has been taken and paired comparison questionnaire was distributed among the ten experts and their views were aggregated and geometric mean was taken from them, and by the FANP approach, the weight of the elements are calculated. Since the weight of the criteria is obtained from FANP method, then the general score of the problem is obtained by solving FMCGP. The present research can provide a comprehensive view of decision makers, especially maintenance managers, and assist them in selecting optimal maintenance strategy.

The purpose of this study was to present a model for analyzing and evaluating suppliers in the Imam Petrochemical Procurement Department based on hierarchical analysis with non-linear (gray) data. The statistical population of this study consisted of an expert group of 20 managers and senior staff of Bandar Imam Petrochemical Procurement Department. To select these individuals, we tried to provide the most knowledgeable people in the field after preparing a complete list, through non-random "judgmental" sampling. Research topic selected and involved in the research process. The main data collection tool in this study was questionnaires designed for different purposes and distributed to the target population after obtaining the approval of the respected supervisor. After distributing and collecting the data, the necessary analyzes were performed through SPSS, Expert Chase and Excel software. In this process, analyzes such as t-test and hierarchical gray analysis were performed. Finally, the research results identified 4 main criteria (supplier criterion, product performance criterion, service performance criterion, cost criterion) and 27 factors were identified and their ranking was determined. It should be noted that this research is of practical purpose, in terms of survey approach and exploratory studies.

The importance of petrochemicals as the largest private sector in the country in terms of value creation for subsidiaries and the use of a smart model is of paramount importance. The purpose of this study was to present and validate the intelligence model of the petrochemical firm's parent firm and provide related solutions. The research is of mixed type which is interviewed by managers in petrochemical industry through snowball sampling and the interviews continued until a theoretical saturation of 23 interviews was reached. Research propositions were analyzed using the data base method and MAXQDA software. The statistical population includes 462 managers of the Persian Gulf Petrochemical Company. In the quantitative part, a researcher-made questionnaire was distributed among 90 managers of the subsidiaries at different levels. Finally, 82 questionnaires were identified for proper analysis. Factor analysis was performed using SPSS and WarpPLS software to analyze quantitative data. The intelligence model of the petrochemical firm specializes in 550 open source code divided into 95 common propositions. The propositions at this level are divided into 13 main categories and 4 concepts that include concepts: strategic decisions (with a beta coefficient of 0.979), tasks, services and resources (0.987), creator / creators of capital and structures (0.974), systems and processes (0.980). The beta coefficients obtained from the data analysis in the quantitative section indicate the high importance of each of the concepts and quantities of fit and factor analysis to confirm the model.

In recent years, there has been a significant increase in the number of organizations that use the project management approach to advance their goals. In spite of improving the effectiveness, this approach has led to some problems. For this, during the years in which project management knowledge has been formed, various standards and institutions have been formed to improve effectiveness in project management. Due to challenges in the field of project management, especially monitoring and steering R & D projects, the study sought to design an optimum model to solve challenges and improve index of the success in R&D projects. Comprehensive review of literature reveals that one of the solutions for these challenges is establishment of an Organizational Unit called Project Management Office (PMO). For this purpose, the concept of the project management office, models, structures and functions has been studied and parameters for effective monitoring and steering research and development projects are identified. In order to design an optimum model for organizations with the nature of such activities, a questionnaire was prepared and after approval and completion by experts of a related organization as a case study and also analyzing the results, a project management office model for covering the above needs is presented

with increase in greenhouse gases, and pollutants and ever-increasing attention to environmental issues, the managers and researchers tried to design and put into operation some networks which in addition to economic optimization has a specific focus on environmental factors and reducing pollutants. , Supply Chain Management has become changed into one of the basic issues of the economic firms in such a way that it has left an impression on all activities of the organizations for producing the products, improving the quality, decreasing the prices and presenting the required services for the customers. The first goal is to increase the profit of the entire supply chain and the second goal is reducing the pollutions of the environment.

This study in terms of purpose is practical and in terms of the method is descriptive-research. In this study, a dual-objective mathematical model is presented for the closed-loop supply chain network design. The mathematical model based on some standards such as the number of products, number of reused products and number of defined parts. Also, one solution was presented based on fuzzy-planning and a dual-objective fuzzy model was considered. In the next step with the available data, the model is tested by using GAMS software.

increasing the amount of the first purpose function has a complete direct relationship with increasing the multiplier. Whereas changing the demand has an indirect relationship with the second purpose function. increasing demand leads to an intensive increase in environmental pollutions.

Today, ensuring the sustainable development of any organization depends on the Optimal conservation and utilization of limited resources and irreplaceable for sustainable Social and Economic Development in that country. For this reason, sustainable supply chain management has become an interesting topic for researchers and industry managers. The purpose of this study is to determine the relationships and levels of sustainable supply chain management incentives. The statistical population of the study is Persian Gulf petrochemical industry. The research method was descriptive-analytical. From the research literature, 12 instances of sustainable supply chain management incentives were identified. Then, by collecting data and determining the relationship between drivers, using interpretive structural modeling and MicMac are analyzed. The results of this study showed that third level metrics including information technology empowerment, institutional pressures, social values and ethics, and commitment and strategy of the company were the influencing factors on other criteria. Also green inventory variables, strategic supplier partnerships, environmental conservation, continuous improvement, internal pressures, economic stability and green product design are affected by other variables and in themselves have no effect on other factors. Logistic optimization criterion is also influenced by level 3 variables and affects level 1 variables. in the MicMac analysis, the results also indicate that the variables of all three levels are considered to be link interface variables. And that means they are highly influential, and every small change to these variables causes a major change in the system.

There are multiple problems of social, economic and environmental have led to organizations towards the use of sustainable supply chain management and on the one hand, globalization has made successful implementation of sustainable supply chain management always a concern for managers, especially in the automobile industry. As a result, due to the need for the use of sustainable supply chain management in organizations, it is essential to achieve a successful and efficient sustainable supply chain management. In this regard, this study aims to detect critical factors that are effective in the successful implementation of sustainable supply chain management in the automobile industry by review the literature and using experts' viewpoints and Fuzzy Delphi method, as well as by using DANP, it determines the importance and ranking of these factors in the implementation of sustainable supply chain management. Research results determined 25 critical factors for the successful implementation of sustainable supply chain management in the automobile industry that of these, the top management commitment and support, strategic planning and long-term vision and environmental auditing for suppliers, were identified as the most important factors, respectively and cross-function integration as the least important.

Content marketing is a strategic approach to marketing that focused on producing and disseminating valuable content. A business can be introduced to them at a low cost by sharing useful information about the products and the business services through this method, but challenges such as lack of knowledge as well as a suitable content marketing model caused this method doesn't have its real place. In this article, an attempt has been made to provide a framework to review content marketing topics. A topic that few official sources have addressed so far. The main purpose of this article is to provide a reasonable and valid framework based on the Foundation's data theory for content marketing with the aim of studying the Fars tourism industry. At first, the necessary information about content marketing is obtained through interviews. After producing open, central and selective coding, the content marketing process pattern is presented. Then, in order to check the accuracy of the model obtained from the Foundation's data theory, the proposed model is examined by using the main criteria for evaluating the Foundation's data model, including: adaptation, comprehensibility, generalizability, control level, theory evaluation and research process evaluation. Based on the obtained results, the proposed model has been able to meet expectations in all criteria. According to the obtained results, the proposed model can also be extended to other manufacturing and service businesses.

The bank is an economic entity that carries out the tasks of equipping and distributing credit, financial operations, currency trading, documentary and dividend claims, stock maintenance and securities. The National Bank of Iran has a staff of over 45,000 people. The National Bank has 5 branches operating in Iran, 2 operating branches and 2 subsidiaries abroad. Branches and Evaluate Branch Productivity during 96 and 97 Years Using Data Envelopment Analysis (DEA) Model. For this purpose, the effective factors are first evaluated and then the DEA model is used to evaluate the performance of the branches in order to measure their efficiency and inefficiency. We then rank them using the AP model and finally measure the productivity of a branch relative to 96 and 97 using the Malmquist Productivity Coefficient model. In this paper, using the data analysis approach, the performance of the West Bank Branches of Tehran National Bank in two years has been evaluated. The efficient and inefficient units of this branch are then identified by the CCR model and finally the efficient branches are ranked with the Anderson Peterson (AP) model and finally the Malmquist efficiency model is measured.