RESEARCH ON PERFORMANCE EVALUATION AND OPTIMIZATION OF LIGHTING INDUSTRY SUPPLY CHAIN BASED ON LMBP ALGORITHM

The development and application of such emerging technologies, as 5G, Internet of Things (IoT), and cloud computing have brought great opportunities to the transformation and upgrading of the lighting industry. It is urgent to transform the traditional lighting industry from the traditional supply chain to the lighting era of the digital supply chain. The traditional lighting industry should actively enhance its own market competitiveness and accelerate the improvement of the supply chain performance level. Given this, the supply chain performance of the lighting industry was evaluated and optimized using the Levenberg Marquardt Back Propagation (LMBP) algorithm of feedback neural networks, which provided reference for the lighting industry. Then, a supply chain evaluation model was established, and a total of 19 evaluation indexes were combed based on the model principle and calculation example. Next, the algorithm effectiveness was verified via MATLAB programming, and the corresponding optimization direction was proposed. When applied to supply chain management, this method can help the lighting industry to timely acquire the performance analysis results, establish benign partnerships between suppliers, elevate the order fill rate of the lighting industry, increase the R&D cost, reduce the warehousing cost, improve the reliability of supply chain coordination and units, strengthen the periodic control of supply chain units, and improve the flexibility of supply chain information and operation. Moreover, this method can improve the accuracy of supply chain performance evaluation and point out the optimization direction of the supply chain performance rapidly.