## 5 Numerical Example
At steady state, the inventory levels at each node in the sub-chain meet the desired inventory control objectives, contributing to improved economic efficiency. Additionally, the system demonstrates the ability to swiftly return to normal operation following disruptions, such as node updates or replacements, minimizing the impact and improving system stability. Therefore, it can be concluded that the supply chain model proposed in this paper effectively mitigates the bullwhip effect caused by uncertain demand. By ensuring system stability, it enhances supply chain performance, reduces economic losses, and minimizes the adverse effects of uncertainties. To verify the superiority of the proposed model, we compared the method presented in this paper with existing supply chain inventory control strategies. Figure 6 illustrates the inventory state changes of the supply chain system under a closed-loop switching control strategy with Markov jump parameters [35]. Although both control strategies stabilize the system, the method proposed in this paper outperforms it in terms of control tracking accuracy, time required to reach stabilization, and the ability to mitigate the effects of disturbances. Furthermore, when compared to certain fuzzy-system-based methods [36; 37], the T-S fuzzy switching control design based on the trust mechanism proposed here demonstrates enhanced capability in handling node failures, node update iterations, and other perturbations during system operation. To verify the generalizability of the model and comprehensively evaluate its performance, we proceed to analyze its application across various industries with differing characteristics. Specifically, we assess the model's impact on mitigating the bullwhip effect,  
Figure 6: Changes in inventory under traditional control methods: (**a**) inventory changes at the supplier nodes of subchains i, j, m; (**b**) inventory changes at the retailer nodes of subchains i, j, m; (**c**) inventory changes at the four nodes of subchain i; and (**d**) changes in consistency error among all nodes. enhancing cost-effectiveness, and improving lead-time indicators within the supply chain, using actual data for validation. Take the fresh food and television factory industries as examples. The supply chain of the fresh food industry has a higher inventory loss rate, more stringent conditions required for storage, and needs to spend more on inventory costs, so it needs to keep the inventory in a lower state. Due to the short time period of goods storage and high real-time requirements, it is necessary to set a lower upper bound of interaction latency. At the same time, it should pay more attention to the production and sales of its own nodes to set the control scheme, so the scaling gain of the consistency controller should be reduced. TV industry products at all stages in the storage process are not easy to wear and tear, and the storage cost is lower, but when subjected to unforeseen events by the impact will be greater (such as the replacement of chip manufacturers, etc.). The node will spend a longer period of time in a broken state, so you need to ensure a higher inventory to withstand market turbulence. Due to the characteristics of larger reserves, longer production cycles, and greater impact by perturbations, the TV industry is more likely to have uneven distribution of goods between warehouses, so it is necessary to give the consistency of the protocol controller greater gain. Taking the actual data in the supply chain operation of a domestic TV set manufacturer as an example, Figure 7 shows the impact on the cost-effectiveness and lead time of the supply chain under different control strategies. It can be seen that the method proposed in this paper has a better effect on reducing supply chain cost and improving supply chain efficiency.