Enterprise AI Analysis

As an important means to solve the financing difficulties of small and medium-sized enterprises, manufacturing supply chain finance has long faced core problems such as information asymmetry, diffi- cult credit evaluation, and low transaction transparency [3]. Tradi- tional supply chain finance models rely on core enterprise credit transmission, but information flow efficiency is low and there are risks of data tampering, resulting in high risk control costs for fi-nancial institutions [7]. With the advent of the Industry 4.0 era, digital twin technology can achieve real-time mapping and dy-namic interaction between physical entities and digital space [1], while blockchain technology, with its characteristics of decentraliza- tion, immutability, and traceability, provides a technical foundation for building a transparent and trustworthy supply chain financial ecosystem [6]. This study proposes a transparency mechanism for manufacturing supply chain finance that integrates digital twin and blockchain. By establishing a digital twin model to collect supply chain operation data in real time [2], combined with blockchain technology to ensure trusted data storage and automatic execution of smart contracts [8], a new supply chain financial system with multi-party collaboration, real-time monitoring, and controllable risks has been constructed [4, 5]. The effectiveness of this mech-anism in improving transparency, reducing financing costs, and optimizing credit evaluation has been verified through simulation experiments [9, 10].

The transparency mechanism constructed in this study adopts a three-layer architecture design, including the physical layer, digital twin layer, and blockchain layer. The physical layer contains enti- ties such as manufacturing enterprises, suppliers, logistics service providers, and financial institutions, and collects production, inven- tory, logistics and other data in real time through IoT devices. The digital twin layer establishes a virtual mirror model of the entire supply chain process based on the collected multi-source heteroge- neous data, achieving real-time mapping and predictive analysis of physical entity states. The blockchain layer serves as the trust infrastructure, adopting consortium blockchain architecture to de- ploy smart contracts, ensuring immutable storage of transaction data, credit credentials, and financing records. The overall system architecture is shown in Figure 1, and the three layers achieve data interaction and business collaboration through standardized interfaces. In terms of data collection, the sensor network deployed in the physical layer collects 277 types of key indicators such as equip- ment operating status, product quality parameters, and inventory changes with a cycle of 5 minutes. Data transmission adopts MQTT protocol to ensure real-time performance, and preliminary cleaning and format conversion are completed at edge computing nodes. After receiving the data, the digital twin layer uses time series anal- ysis algorithms to evaluate the status of each link in the supply chain and predicts potential risk points through machine learning models. The blockchain layer designs four types of core smart con- tracts for financing application, credit evaluation, fund allocation, and repayment management, achieving automated execution of business processes. Digital twin model construction adopts a multi-level modeling strategy, decomposing the supply chain into three granularities: en- terprise level, order level, and equipment level. The enterprise-level model describes the basic attributes, historical transaction records, and credit status of each participant; the order-level model tracks the execution progress and quality indicators of specific businesses; the equipment-level model monitors the operating efficiency and fault risks of key production equipment. Model updates adopt an event-driven mechanism, triggering synchronous updates when physical entity state changes exceed preset thresholds. The supply chain state evaluation model is constructed based on weighted comprehensive evaluation method, defining the supply chain health index H as: H = Σi=1 Wi Si (1) where wi represents the weight of the i-th evaluation indicator, Si is the standardized indicator score, and n is the total number of indicators. This study selects 12 key indicators including order fulfillment rate, inventory turnover ratio, capital turnover rate, and quality pass rate, and determines weight allocation through analytic hierarchy process. Table 1 shows the main evaluation indicators and their weight configurations. Based on real-time data from the digital twin model, the system can dynamically calculate supply chain health, providing quantita- tive risk assessment basis for financial institutions. Figure 2 shows the multi-dimensional data fusion process of the digital twin model, forming a closed-loop feedback mechanism from data collection, feature extraction to state evaluation. The credit evaluation model designed at the blockchain layer inte- grates on-chain historical transaction data with off-chain digital twin model output to construct a dynamic credit scoring system. The model defines enterprise credit score C as a weighted com- bination of historical credit Ch and real-time operational credit Cr: C = a· Ch + (1 − a) Cr (2) where a is an adjustment coefficient that is dynamically adjusted according to the enterprise's role in the supply chain. Historical credit Ch is calculated based on historical transaction records stored on the blockchain, including on-time repayment rate, contract ful- fillment rate, and number of credit defaults. Real-time operational credit Cr is converted from the supply chain health H provided by the digital twin model: Cr = β· Η + (1 − β)· P (3) where P represents the predictive risk index, which predicts the potential risk probability within the next 30 days by analyzing time series data through LSTM neural networks. The ẞ parameter controls the weight distribution between current state and future prediction, and is set to 0.650 in experiments. Smart contracts automatically determine financing amounts and interest rate levels based on credit scores. The calculation formula for the maximum financing amount Lmax is: Lmax = M· C· (1 – R) (4) where M is the enterprise's average monthly revenue scale, and R is the industry risk coefficient. Interest rate r is determined by a piecewise function. When the credit score is higher than 0.800, preferential interest rates apply; when it is lower than 0.600, risk alerts are triggered and additional guarantees are required. The blockchain consensus mechanism adopts Practical Byzantine Fault Tolerance (PBFT) algorithm to ensure that credit evaluation results reach consensus among multiple verification nodes, with transac- tion confirmation time controlled within 3 seconds. The simulation experiment builds a consortium blockchain test net- work based on Hyperledger Fabric 2.4, deploying 4 organizational nodes representing core manufacturing enterprises, secondary sup- pliers, logistics service providers, and commercial banks respec- tively. Each organization is configured with 2 Peer nodes and 1 Orderer node, adopting Raft consensus algorithm to ensure data consistency. The digital twin simulation platform is built using AnyLogic software, simulating a three-tier supply chain network containing 1 core enterprise, 15 tier-1 suppliers, and 43 tier-2 sup- pliers, with a simulation time span of 180 days and a time step set to 1 hour. The experiment designs three scenarios for comparative analysis: traditional supply chain finance mode (Baseline), blockchain-only mode (Blockchain-only), and the digital twin and blockchain fusion mode proposed in this paper (DT-Blockchain).

This study proposes and verifies a transparency mechanism for manufacturing supply chain finance based on digital twin and blockchain. Through technology integration, it effectively solves the problems of information asymmetry and trust deficit in traditional supply chain finance. Digital twin technology achieves real-time mapping and state prediction of supply chain physical entities, pro- viding high-quality dynamic data support for financial decisions; blockchain technology builds a decentralized trust infrastructure, ensuring that transaction data is immutable and credit evaluation results are traceable. Simulation experiments show that this mecha- nism reduces financing approval time by 96.808%, reduces financing costs by 3.270 percentage points, controls bad debt rates within 1%, and significantly improves supply chain transparency. Sensi- tivity analysis verifies the robustness of the model, and system performance remains stable when key parameters fluctuate within reasonable ranges. The research results provide theoretical founda- tion and technical paths for building an intelligent and transparent supply chain financial ecosystem, and have important practical value for promoting financing of small and medium-sized enter- prises and optimizing supply chain collaboration. Future research can further explore the application of cross-chain technology in multi-supply chain network interconnection, as well as integration solutions for privacy computing technology in sensitive business data protection, promoting supply chain finance to develop to a higher level.