As the pillar industry of our national economy, automobile industry plays an important role in national economy and social development with long industrial chain, high correlation, wide employment and large consumption. According to the Ministry of Industry and Information Technology, the automobile industry contributes more than 16% to GDP growth. Parts industry in automobile aftermarket is a significant part of the automobile industry chain. Compared with the whole vehicle supply chain, the complexity of parts supply chain management is higher, and its existing pain points and potential challenges will affect the path and speed of the overall transformation and upgrading of the automobile industry.

The auto parts refer to the units that constitute the whole auto parts processing and the products that serve the auto parts processing. To some extent, the quality of auto parts determines the reliable and stability of the quality of the entire life cycle of automobile. According to Deloitte’s statistical data, the number and varieties of spare parts under management are huge. On average, an automobile consists of about 6,000 spare parts, and a brand-new model needs 2,500-3,000 new spare parts. Most automobile enterprises need to manage over 30,000 spare parts SKU, and their supply chain management faces many challenges.

Digital China classifies, predicts and improves spare parts as per their life cycle and business attributes and so on. Firstly, machine learning and artificial intelligence algorithms are used to predict the demand for spare parts, and suitable models in the model & algorithm library are selected as per different categories of spare parts, and final results are predicted along with adjustment of business rules. Then, the prediction results of demands are combined with the constraints of business strategies, performance indicators, procurement constraints and other conditions to formulate the inventory strategy of spare parts suitable for the relevant enterprise, and to control the balance between reducing inventory and increasing satisfaction rate; finally, KPI indicators are used to monitor the effectiveness of the model and inventory indicators for timely understanding and adjustment of model strategy.