The field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply chain performance (Naimi Sediq et al., 2013; Shafi'i et al., 2018). While every action and performance within the supply chain influences the outcomes of the game, it does not solely determine them. The goal is to balance the parties involved in the supply chain and create satisfaction for the end customer (Metinfer et al., 2018).
Although extensive research has been conducted in supply chain management within the steel industry, the impact of sanctions on Nash equilibria and the application of three approaches (Cournot, Stackelberg, and collusion) to achieve game balance in different scenarios have not been thoroughly investigated. This research aims to fill this gap by addressing the mentioned research question. The current study focuses on determining the optimal price using an intelligent decision-making system based on game theory within the steel industry, considering the presence or absence of the sanctions variable.
Our country currently possesses several relative advantages in terms of steel production conditions, including abundant and affordable energy, iron ore and refractory raw materials, considerable steel production experience, and a skilled and cost-effective workforce. By acquiring new production technology, these advantages enable our country to play a competitive and influential role in the global steel market. However, the steel industry faces significant challenges such as price fluctuations, extreme price disparities across regions, and delayed delivery due to a lack of efficient supply chain management. Therefore, the main research question aims to provide a model that incorporates key variables, such as the supply and demand of final and intermediate products in the steelmaking industry and the future trends in production and quantity changes.
This article introduces a composite model that combines artificial neural networks and game theory to assist stakeholders in the steel industry in determining optimal production levels and price levels. To predict the price of steel, three techniques were employed: Bayesian neural networks, support vectors, and Grassberg anti-diffusion. Additionally, to address the issue of binary identification in the neural network, three different network structures were introduced: feedforward network structure, competitive network structure, and backward associative memory network structure.
The first scenario is the non-cooperative game (Cournot model scenario) where each participant aims to maximize their profit and would not deviate from their strategy as it would lead to a reduction in their profits. The second scenario is the sequential non-cooperative game (Stackelberg model scenario), in which two chains engage in a confrontation of the Stackelberg game type. The leader's goal is to determine the best strategy while considering all rational strategies that follower players can employ to maximize their income. This scenario demonstrates that the rate of price and profit increase is lower compared to sequential and cooperative game modes. The third scenario is the cooperative game (collusion model scenario). In this scenario, the allocation of profits among the cooperating members is crucial to ensure the stability of their cooperation. The Grassberg anti-diffusion method exhibits higher accuracy due to its higher true positive (TP) and true negative (TN) values compared to other algorithms. Additionally, it has fewer false positives (FP) and false negatives (FN) because a higher TP and TN indicate more accurate predictions in the tested dataset, while FP and FN represent incorrect predictions. The inclusion of the sanctions variable as a moderating factor in the steel price forecasting model accounts for the potential reduction in production and increased cost price. Through the model, it was discovered that the Grossberg method yields more accurate steel price forecasting. Consequently, price forecasting in the model is based on the Grossberg method.
The results indicate that transitioning from the Cournot game to the Stackelberg game and from the Stackelberg game to the collusion game in the steel industry's supply chain leads to a $6 increase in price per ton and a product supply ranging from 1500 to 4000 tons. In other words, as collusion in the steel market intensifies, more products are introduced into the market, resulting in an increase in product prices and a decrease in the welfare of steel consumers. According to the findings, increased competition in the industry reduces the profitability and production levels of producers while enhancing consumer welfare. Conversely, higher levels of monopoly exhibit the opposite effect. To maintain a balanced supply chain in the steel industry and prevent potential problems, it is recommended to adopt the Stackelberg game approach, which aligns more closely with reality. It is worth noting that the order in which players enter the game impacts the Nash equilibrium. Therefore, exploring market entry monitoring regulations and rules in this industry becomes crucial since the steel industry involves high entry and exit costs. Policymakers and industry managers should consider monitoring the entry and exit of players, formulate game standards and rules among market participants. Based on the results, the primary recommendation of this research is to increase competition intensity and adopt the Cournot approach in the industry to reduce prices and increase production. Additionally, enhancing international relations and diplomatic efforts will mitigate the impact of sanctions on the industry, leading to cost price improvements and an increase in the level of comparative advantage at the international level.