نشریه مدیریت راهبردی در سیستم های صنعتی
پیاپی 65 (پاییز 1402)

In this research, a combined statistical process control model is presented to identify factors affecting efficiency and quality in manufacturing and component manufacturing industries, and then controlling and optimizing these processes is considered. Manufacturing and component industries are considered as the main body of the country's industries for case study and implementation. Clustering techniques are used to discover factors affecting efficiency. And then using decision tree algorithms to predict efficiency and quality in these industries, and in the final stage, control charts of dispersion and average variables are used to draw control charts. The comparison table of the parameters is prepared by the output of the Clementine software, and RapidMiner software is used in the neural network section. The results obtained from the identification of influencing and forecasting factors are close to the target values from a technical point of view, and the control charts are consistent with the technical control limits of the characteristics and are useful for optimizing the target value, which is efficiency and quality.

The main sources of income and assets are important for any organization. With this view, companies have started to do more to maintain health. Since in many companies the cost of acquiring a new customer is much higher than actual customer satisfaction, customer churn has become the main area of evaluation for these companies. Client-facing companies, including those active in the technology industry, are facing a major challenge due to customer attrition. With the rapid development of the telecommunications industry, dropout prediction becomes one of the main activities in gaining a competitive advantage in the market. Predicting customer churn allows operators a period of time to remediate and implement a series of preventative measures before customers migrate to other operators. In this research, a decision support system for predicting and estimating the churn of customers of Kurdistan Telecommunication Company (with 52,900 subscribers) with different data-mining and machine methods (including simple linear regression (SLR), multiple linear regression (MLR). Polynomial regression. (PR), logistic regression, artificial neural networks, Adabust and random forest) are presented. The results of the evaluations carried out on the data set of the Kurdistan Province Telecommunication Company, the high performance of artificial neural network methods with 99.9% accuracy, Adabust with 99.9% accuracy, 100% accuracy and random forest It shows 100% with accuracy.

he technology roadmap is one of the most effective approaches to align the organization's strategies with planning in the field of technology, from a strategic point of view and its integration with the new technologies of the fourth industrial revolution, the technology roadmap of the fourth industrial revolution leads organizations to new smart and digital technologies in order to achieve the organization's goals. And it leads to increase in productivity in production. This research aims to analyze the effective smart and digital factors of the fourth industrial revolution with a strategic management approach in developing a technology roadmap for power equipment manufacturing and energy supply industries.This research aims to analyze the effective factors of the fourth industrial revolution on the development of a technology roadmap for power plant equipment manufacturing and energy supply industries.In this regard, first by using the literature and interviews with a number of relevant experts in the power plant equipment manufacturing and energy supply industries, several factors were extracted, and then the factors were finalized using the fuzzy Delphi method and ranked using the TOPSIS technique. The results show that the criterion of intelligent design and production has the highest rank, and the component of defining and balancing the research and development portfolio has the first rank, the technology evaluation component has the second rank, and the market changes evaluation component has the third rank.

Pollution due to biomass burial, the possibility of producing clean energy from biomass and the high demand for ‎energy have made the optimization of the biomass supply chain network important and necessary. The purpose of ‎this article is to optimize biomass supply chain network at four levels in order to reduce economic and ‎environmental costs. The most important gap in research, resolved in this article, is the determination of the ‎desirable and undesirable outputs of the masses in the centers. Separating and considering the multi-period, multi-‎product mode with heterogeneous transport means. The research model is a two-objective linear programming of a ‎correct number mixed with uncertainty and disturbance, four scenarios were designed for this purpose. The model ‎was solved with genetic algorithm and MOPSO method and with Python software. Validation of the model was ‎investigated in a real case study in Fars province has been The proposed model has been able to implement ‎sustainability and resilience at the same time, which has reduced costs, reduced carbon emissions, and increased the ‎commercialization of energy production from biomass, thus increasing the willingness of investors to invest in this ‎network. It is supplied from the supply chain. The proposed model makes the amount of energy production 2.1% ‎lower than when the favorable and unfavorable outputs are not considered, which means it is much closer to reality. ‎By performing sensitivity analysis on real data, the efficiency of the model was proved

In this research, the intelligent model of preventive maintenance and repairs based on artificial neural network methodology - fuzzy logic with the help of artificial intelligence environment of MATLAB software based on the structure of Falcon's five-layer model of artificial neural networks is presented, the research method is based on systems thinking. After determining the most important factors affecting preventive maintenance and repairs with the help of a questionnaire and based on a dataset of 2,000 samples of data and reports of the Director General of Textile and Clothing Industries of the Ministry of Safety during the years 1396 to 1401 (in the form of six and a half years) and validity Data evaluation by the maintenance and repair experts of 240 industrial units, a smart model was designed, which after the implementation of the model in Borujerd textile factories as the place of implementation of the plan can be claimed if (If); Five "technology" factors have values of 0.9129; Good condition (upper bound of good membership function), "Employees" has values of 0.9239; good condition (upper bound of good membership function), "working environment" has values of 0.8859; relatively good (lower limit of the membership function), "quality" has values of 0.9999; Perfect condition (highest function), "strategy" has values of 0.9999; good status (upper limit) in preventive maintenance and repairs, then: the status of the output variable of the research, i.e."Optimization of preventive maintenance and repairs performance (Y)" will be at its fifth level, i.e. very good, equal to 0.882.

In recent years, the growing industrial and economic competition, environmental concerns, and governmental pressures on manufacturers regarding waste management have underscored the significance of designing a reverse supply chain and closed-loop network. Simultaneously, the potential for profit arising from product recycling has further emphasized the importance of these systems. This research focuses on developing a four-stage closed-loop network model for the supply chain, taking into account the uncertainty of product recycling rates. The primary objective of this study is to provide an integer linear programming model aimed at minimizing supply chain costs and customer service time under uncertain conditions. The supply chain model is designed by integrating queuing theory and product recycling system optimization. A critical aspect of this research involves modeling the uncertainty in the return rate of consumer products in the closed-loop supply chain, with the aim of developing a robust approach to address this issue. Additionally, the performance of the proposed model in the paper industry is evaluated, and a sensitivity analysis is conducted with respect to the decision variables using two metaheuristic algorithms: the Multiple Objective Harmony Search and the Non-dominated Sorting Genetic Algorithm.