فصلنامه مدیریت زنجیره تامین
پیاپی 74 (بهار 1401)

Nowadays, companies are trying to meet customer demand due to the economic characteristics of modern global business and complex supply chain operations. Global demand uncertainty is a major challenge that is exacerbated by the occurrence of disruptions. In this study, a multi-product fuzzy stochastic nonlinear mathematical programming model is proposed to design a reliable supply chain network considering a product with modular manufacturing technology at the risk of disruption. Also, a decision support system is designed for modular production, which plays a major role in increasing the reusability of products and reducing waste. The case study, focuses on the production of cryogenic pumps used for the export of LPG, which is a critical equipment in the export of liquid propane and butane. The structure of this supply chain network includes levels of module suppliers, primary production centers, inspection centers, repair centers, reproduction centers, and customers. To deal with the disturbances, a scenario-based stochastic approach has been used and parametric uncertainty has been managed with the possibilistic-robust hybrid programming approach. The model evaluation has been performed with a precise approach in GAMS software with CPLEX solver and the sensitivity analysis has addressed the uncertain parameters. The results show that the current approach while controlling the uncertainty, ensures the optimal flow of facilities.

Taking into account the population growth, significant consumption of natural resources, environmental degradation and the resulting public unrest, the governments’ strategy of circular economy is not sufficient to meet the demands and it is required that the entire supply chain be transformed from the linear model to a circular one. Circular supply chain management is a good solution to reduce problems such as pollution, production patterns, consumption, resource scarcity and climate change. The goal of supply chain management is to optimize the resource consumption throughout the product life cycle by recycling. Despite the many advantages of using a circular supply chain, its implementation in some countries, including Iran, is associated with many obstacles. The present study has been designed and implemented with the aim of identifying and extracting these obstacles using the stakeholder analysis approach in the steel industry. In the first stage, the focus group method was used to identify the stakeholders of the steel industry and determine the position of each. In order to evaluate the validity and reliability of the research, the members of the focus group consisting of ten experts were selected based on their experience (at least five years) with the steel industry supply chain projects by purposive sampling. Data were categorized and prioritized through the interest-power matrix. The obstacles to the acceptance of circular supply chain were identified and extracted from the perspective of stakeholders through interviews. The steel industry policymakers can implement the circular economy approach in supply chains by taking measures to modify the factors identified in this study.

The present study investigates the role of strategic cost management as an intermediary variable on the relationship between the supply chain activities, and the financial performance improvement. The improvement of financial performance is selected as the dependent variable, the supply chain activities and the strategic cost management are considered as the main independent variables whilst the financial structure and the size of company are considered as control variables. The research is an applied purpose research, based on a data-survey retrospective research design and uses a descriptive-inductive inference method. From a list of selected companies of Tehran Stock Exchange, 165 companies were picked by the simple random method and top management survey was used to measure the supply chain activities and strategic cost management. Applying the structural equation model after diagnostic tests shows that strategic cost management has a positive and significant effect on the relationship between the supply chain activities in different dimensions and on improving the financial performance with different metrics.

In the real world, remanufacturing depreciated products for the purpose of cost reduction in supply chains, is recognized as a competitive advantage. In this paper, an appropriate approach for collecting and remanufacturing depreciated products in a closed-loop supply chain is considered with the aim of reducing costs using the Stackelberg game theory. To this end, the considered chain members include the manufacturer, the retailer, and the remanufacture contractor (as the third party to the contract). In this research, the four following scenarios are considered and the results are analyzed: 1) Payment by the manufacturer to collect and reproduce the depreciated product; 2) Joint payment of an amount by the retailer and the manufacturer to collect and reproduce the depreciated product; 3) Joint payment of an amount by the manufacturer and the third party to the contract to collect and reproduce the depreciated product; 4) Payment by the manufacturer for collection and non-reproduction of the product. The computational results show that the chain profit will reach its maximum amount with the optimal reproduction rate considering the third scenario, and the chain members will gain more benefits than other scenarios. The findings of this research indicate that when the reproduction process does not take place, the lowest possible profit is made in the chain. The sensitivity analysis performed by increasing the effect of the final product price on customer demand in different periods, shows a decreasing trend in the three decision variables of final product price, selling price to the retailer, and the amount paid to the customer for each consumed product.

Today due to the significance of environmental pollutants and the increase of global standards for the environment, more attention has been paid to the design of closed-loop supply chain networks with green considerations. On the other hand, the direct and inverse chains affect each other in terms of the efficiency rate. As a result, the performance of each chain has its own effect on other chains and on the entire supply chain. In this research, we design a mathematical model for the green closed-loop heavy-duty supply chain network, which considers products under conditions of uncertainty with the concept of economic pricing. Economic pricing in this issue increases economic profitability. The designed mathematical model has a fuzzy basis and pursues two objectives. The first goal is to minimize costs and the second goal is to minimize environmental pollutants. The decisions made in this model include determining the optimal location of each center based on potential locations, the optimal amounts of production, distribution, collection and recycling, and also the reproduction of products. Furthermore, a two-sample independent t-test is used to validate the results of the definite and indefinite models. To solve the two-objective function model, the ε-constraint method is used so that the problem can guarantee strong Pareto optimal solutions and prevent weak Pareto solutions. Finally, to evaluate the efficiency of the proposed method, a case in the field of heavy tires is studied, solved and implemented to produce and present valuable management results.

Most organizations use different channels to offer products and services in order to increase their market share and attract more customers. In a supply chain with more than one sales channel, product pricing is a critical issue in the decision-making process. Proper pricing can be very effective in reducing the conflict between channels and increasing the profitability of existing channels. In this paper, a pricing strategy between the dual retail and chain channels is presented to maximize profits by considering a discount in the chain channel and a revenue sharing contract between the manufacturer and the chain stores. The input parameters of the problem are adjusted using the Taguchi method and solved by the particle swarm metaheuristic algorithm in order to validate the model. Regarding the short solution time of the proposed algorithm, it has a good quality and efficiency in comparison to GAMS software. In addition, the gap of the objective value obtained by the proposed algorithm is less than 1% compared to the GAMS output. The results also show that the potential demand of the retail channel has a higher impact on the profit of the supply chain, in comparison to other parameters.