Adaptive Management System of Supply Chain in a Manufacturing Enterprise

This article presents a system for enhancing adaptive management through the integration of fuzzy logic decision-making system in backed by blockchain supply chain smart-contracts of an enterprise. The adaptive management system is based on two main components: risk assessment and supply chain optimization. To assess risk, fuzzy logic models analyze input variables such as supply chain risks, financial risks, and operational risks. An adaptive resource management system is characterized by the ability to respond to changes in the external environment and internal processes. This system should integrate advanced technologies for effective resource management and ensure strategic stability. The system utilizes blockchain’s immutable ledger and smart contracts to automate key processes such as manufacturing processes, inventory management, and regulatory compliance, thus addressing issues like communication gaps, delays, and counterfeit risks. However, the inherent rigidity of blockchain systems in adapting to dynamic manufacturing environments prompts the incorporation of fuzzy logic. Fuzzy logic offers a solution to this limitation by enabling more nuanced decision-making through the processing of uncertain or imprecise data. The article details the integration of fuzzy logic with blockchain, wherein fuzzy inference systems (FIS) are employed to evaluate and interpret operational data under variable conditions. This combination allows for adaptive responses to supply chain disruptions, such as supplier delays or inventory shortages. The fuzzy logic system applies rules to determine the optimal course of action, which is then executed through blockchain-based smart contracts. Key advancements include the development of a modified smart contract framework that uses fuzzy logic to adjust supply chain parameters dynamically. For example, supplier reliability is assessed using fuzzy membership functions, leading to adjustments in pricing and supply quantities based on real-time evaluations. This approach enhances the flexibility and responsiveness of manufacturing operations, ensuring that decisions are based on comprehensive data analysis rather than static rules. A fuzzy logic system processes ambiguous information using linguistic variables and fuzzy sets that help interpret uncertainties in operational data. The key element of the system is the fuzzy inference system, which performs basic steps such as fuzzification, rule evaluation, aggregation, and defuzzification. This results in more refined decision outputs based on fuzzy rules that can take into account different conditions such as supply quantity and supplier reliability. Combining fuzzy logic with smart contracts facilitates dynamic adjustments in supply management, such as pricing and modification of supply quantity based on supplier reliability. It is evaluated how residual networks and deep multi-level transformations can be used in combination with a fuzzy logic system to improve performance. The concept of global mean pooling and fully connected levels is applied to classification tasks, and cross-entropy loss functions improve model accuracy. Additionally, the use of membership functions such as trapezoidal and triangular sets allows for accurate modeling of factors such as delivery timeliness and product quality.The proposed system provides a robust solution for managing production processes amidst fluctuating conditions, combining the transparency and security of blockchain with the adaptive capabilities of fuzzy logic. This integration aims to optimize production efficiency and maintain operational continuity in the face of unpredictable challenges.