Xi'an Fanyi University, Xi'an City, China.
PLoS One. 2021 Nov 3;16(11):e0259284. doi: 10.1371/journal.pone.0259284. eCollection 2021.
The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.
本工作旨在增强供应链环境下工业企业的核心竞争力，提高库存管理效率和库存资源利用率。首先，分析了供应链环境下供应商与制造商之间的供需关系以及基于云制造的智能工厂的生产模式，发现高效管理备件库存可以有效降低成本，提高服务水平。在此基础上，针对备件需求的不同数据类型提出了不同的预测方法，并进行了验证。最后，构建了基于云边协同计算的库存管理系统，并选择遗传算法进行对比，验证所提出系统的性能。实验结果表明，所提出的基于特征值加权求和与拟合的预测方法在机床备件需求预测中具有最小的误差和最佳的拟合效果，拟合后的最小误差仅为 2.2%。此外，该备件需求预测方法可以很好地完成三种不同类型的备件需求时间序列数据的预测，预测误差的相对值保持在 10%左右。该预测系统可以满足备件需求预测的基本要求，实现比周期预测方法更高的预测精度。而且，基于云边协同计算的库存管理系统比遗传算法具有更短的处理时间、更高的效率、更好的稳定性和更优的整体性能。研究结果为边缘计算在库存管理中的应用提供了参考和思路，具有一定的参考意义和应用价值。
