فصلنامه چشم انداز مدیریت صنعتی
سال سیزدهم شماره 4 (پیاپی 52، زمستان 1402)

The purpose of this research is to identify and investigate the relationship between the barriers to the implementation of the circular economy and Industry 4.0 in the supply chain in one of the polluting industries of the country (Yazd Glass Complex). In order to identify the barriers and categorize the barriers, the Meta-Synthesis approach was used, and in the quantitative part, in order to present the pattern of relationships between these barriers and their prioritization, the combined method of Fuzzy DEMATEL- ANP (FDANP) was used. The findings of the qualitative section included the identification of 24 obstacles in 5 dimensions. In the quantitative part, the findings showed that economic, infrastructural and technical-legal barriers are considered as causal dimensions that affect human resources and organizational-management (Effect) dimensions. Also, the obstacles of lack of expertise and knowledge of employees regarding the circular economy and Industry 4.0; Fear of unemployment and resistance to organizational and process changes and lack of funds and financial tools for training and research and development and operations are the most important obstacles in the implementation of the circular economy and Industry 4.0 in the supply chain of polluting industries such as the glass industry.

Over the past decade, growing environmental concerns and social legislations have forced decision makers to design their supply chains with regard to the environmental impacts, responsiveness, and economic benefits. In this research, a mixed integer linear programming (MILP)  model is developed to optimize the objectives of cost, environmental impacts and responsiveness in order to determine appropriate policies about the decisions of carbon tax, technology selection, location, capacity of facilities. In this model, there are simultaneously two types of disruption and operational risk for some parameters such as demand and costs, which is named hybrid uncertainty. To cope with this type of uncertainty, a possibilistic stochastic programming approach using concepts of credibility constraint programming is proposed to design a closed-loop supply chain. A high-performance flexible programming approach is applied to solve the multi-objective model. Results show that the appropriate rate of carbon tax is around 20000 Rial per kilogram of carbon. The results indicate that in order to optimize the level of responsiveness and reduce the environmental impacts and costs, it is better to use the green technologies in the plants and recycling centers and to establish the plant and distribution centers near the customer zones.

One of the characteristics of the fourth industrial revolution is the creation of production intelligence through real-time data to make accurate and timely decisions. Therefore, data-driven statistical process control is expected to significantly contribute to the advancement of intelligent manufacturing. For this reason, statistical process control has become one of the most widely used tools to maintain an acceptable level of quality characteristics in the era of Industry 4.0. In this article, organizational readiness factors for the establishment of intelligent statistical process control in the age of the fourth industrial revolution in the gas industry were investigated. For this purpose, after identifying the control structure of the intelligent statistical process of applying the literature review, 12 factors of organizational readiness in order to achieve this goal were presented in the form of a framework. Then the relationship and sequence of these factors were determined by fuzzy interpretive structural modeling. Next, the obtained model was verified using the structured equation modeling approach. The presented model can be a guide for the gas industry in implementing an intelligent statistical process control system to provide better services and less defects.

The main purpose of the research is to achieve a smart supply chain management model for FMCG industry in Iran. Since this research is of an exploratory mixture type, in qualitative section after reviewing the existing literature and collecting the relevant literature of the researchs, related articles identified and the obtained results analyzed by Multi-Grounded theory. After performing the meta-synthesis method, the selected articles extracted. Then after interviewing with 15 experts, the categories were coded. The result led to the extraction of the model. In the quantitative part, in order to refine the attributes, the DCSM used. Next in qualitative part, for model configuration, FsQCA method implemented. Quantitative data collected from 20 supply chain managers and experts in companies active in FMCG industry in Tehran. Based on the results obtained, the model has 6 main categories, 21 sub-categories and 113 attributes. Also, based on the results of the quantitative section, 92 attributes determined as the most appropriate attributes and then the most suitable combination of the attributes related to the paradigm model determined. The results of the research presented a conceptual model for smart supply chain management in FMCG industry in Iran, which can be used by industrialists and researchers.

Investment in industrial equipment is a necessity for every industrial unit. In this study, a mathematical model for intelligent monitoring of the performance of industrial equipment of wind turbines using the Internet of Things and unmanned aerial vehicles (UAVs) with an edge computing approach is investigated. In this model, the performance of the UAV for intelligent monitoring of wind turbines is investigated in three stages: detection, computational offloading, and local computations. Considering the bi-objective nature of the final model, the model was solved by a genetic algorithm with non-dominated sorting and the epsilon constraint method using random numbers. According to the findings of the study, the epsilon constraint method loses its efficiency with increasing model dimensions and is not able to find the Pareto frontier in problems with large dimensions. For this purpose, the model was solved using the second version of the genetic metaheuristic algorithm with non-dominated sorting and a new structure for representing chromosomes. This algorithm was able to solve problems in large dimensions that the epsilon constraint method was unable to solve. According to the results, the epsilon constraint method and the genetic algorithm with non-dominated sorting differ only in the time to solve, and have similar performance in other criteria; as a result, the genetic algorithm with non-dominated sorting is proposed as the superior method.

In this research, multi-period stock portfolio selection was modeled and solved under uncertainty and considering transaction costs. A possibilistic mean-semivariance-entropy model for multi-period portfolio selection by taking into account four criteria viz., return, risk, diversification degree of portfolio and transaction cost was introduced. In this model, the return level by the possibilistic mean value of return, the risk level by the lower possibilistic semivariance of return, and the diversification degree of portfolio was quantified by the possibilistic entropy. We used fuzzy theory in order to consider uncertainty in proposed model and considered asset returns as trapezoidal fuzzy numbers. MOPSO algorithm was used to solve the model. In order to evaluate the proposed models performance, a similar model including proportional entropy was modeled and solved and its results were compared with the possibilistic entropy model. The results of this comparison showed that the possibilistic entropy model is better than the proportional entropy model because it provides better efficiency frontier. Regarding the optimized portfolios in one-time implementation of the algorithm on the possibilistic entropy model in third-time period, the highest percentage of stocks selected in the optimal portfolio of risk seeker, risk averse and risk neutral investor is respectively kagol,hakeshti and shekhark.

In the present unstable business environment, supply chains are considerably impacted by disruptions, necessitating the implementation of resilience strategies. These strategies, however, pose significant financial challenges for companies. Therefore, financing is essential in developing resilient supply chain networks. In addition to utilizing existing capital, options such as bank loans and trade credit can be employed to alleviate the financial burden and enhance working capital.  The present scholarship has failed to address the issue of financial strain resulting from the adoption of resilience strategies. Additionally, the significance of trade credit and repayment scheduling in all levels of the supply chain network also left under-researched. To fill this research gap, this paper proposes a three-tiered supply chain network consisting of main/support suppliers, factories, and distribution centers under uncertain demand conditions. The network is developed to effectively handle demand uncertainty and achieve optimal net present value and demand estimation. To solve the bi-objective model of the study, a preemptive fuzzy ideal programming approach accompanied by the implementation of the CPLEX solver is utilized. The findings lend support to the importance of securing financial support for support suppliers and establishing effective trade credit agreements across all levels of the supply chain.

The objective of this research is to predict and monitor the ton-kilometers and waybill on the Iran's roads to find abnormal Behavior. In this study, data was collected from monthly observations over a period of 6 years (1395 to 1400) by the Iran Road Maintenance and Transportation Organization, categorized by province. Different machine learning techniques, deep learning, and time series methods were employed to predict ton-kilometers, and the results were monitored for abnormal behavior following an increase in interest rates and taxes. For model implementation, a dataset of 72 records of ton-kilometers and 72 records of issued waybill, collected from 32 provinces over six years of road transportation, was utilized. Initially, four different prediction methods, including random forest, LSTM neural network, ARIMA, and ETS, were extensively examined. The empirical results indicate that the random forest outperforms the other models. this study employs the statistical quality control tool, the z-score, to detect outliers and abnormal behavior in the data. The empirical findings reveal that out of the 32 provinces, three provinces exhibit abnormal behavior, and one of them is attributed to factors other than an increase in interest rates and transportation taxes.

This paper has investigated the inventory and financial flows in supply chains. Its purpose is to provide a method to optimize these two flows for chain members, where Return on Capital (ROC) is defined as the dependent variable, and cash conversion cycle (CCC) equation components, which show financial and physical flows, are formulated as independent variables. The data of chain members from six selected industries, including auto & parts, pharmacy, food, petrochemical, metal, and mining, have been extracted. Two scenarios, 1) revision of independent variables without a change in the cash conversion cycle of the entire supply chain, and 2) reducing the days of independent variables along with reducing the cash conversion cycle, have been defined. The problem is simulated using Agent-Based Modeling and NetLego software. Results of the first scenario indicate that if Days Inventory Outstanding (DIO) is reduced in downstream and transferred to upstream of the chain, and Days Payment Outstanding (DPO) in the upstream is shortened, ROC is improved for the entire chain. Also, the results of the second scenario show that, in proportion to the reduction of the cash conversion cycle through productivity under collaboration of chain members, the performance improvement of ROC is remarkable.

Increasing concerns about environmental issues, resources constraint and social issues of employees have moved organizations to review production strategies and facility layout to provide an arrangement that takes into account all dimensions of sustainability (economic, social, and environmental). Therefore, in this research, a multi-objective mathematical model has been developed to achieve a balance between reducing layout costs, reducing electrical energy consumption, and improving the safety of the production environment for the operators. One prominent feature of the proposed model is the consideration of the strategy of scaling the processing speed of operations in machines alongside process routing. This is combined with concepts such as machine reliability, workload balancing, and operator allocation. In order to validate and assess the usability of the proposed model, the LP-metric method and examples derived from the relevant literature have been utilized. Considering the complexity of the model and the limitations of the GAMS software in providing timely solutions for large-scale problems, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been employed. Finally, the proposed approach was used as a real case study in a gas stove and industrial oven production workshop. The results show a 62% savings in production costs from applying the proposed method.