A large number of SKUs with unstable demand
The order volume of the automotive aftermarket depends on scattered and random market demand, and there are many types of automotive spare parts. In terms of frequency of use, there are many non-standard and long-tail pieces, with low consumption frequency for individual SKUs, making the demand for products more difficult to predict.
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High pressure on inventory costs
The supply and distribution of spare parts are affected by seasonal, cyclical, and regional factors. If dealers and manufacturers hold a large amount of inventory to cope with possible spare parts demand, it will lead to a large amount of inventory accumulation and high inventory costs; on the contrary, if dealers and manufacturers do not hold spare parts inventory, it will lead to a long customer repair service cycle, causing customer loss.
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The overall supply and distribution network is relatively complex
The supply and distribution network of spare parts involves many entities and information interactions, from dealers and OEM manufacturers issuing order requirements to the actual process of spare parts delivery. The delivery period of the automotive aftermarket is random and the time limit is short, some parts products have relatively complex processes and raw materials, and the product supply capacity is highly affected by production capacity and external interruptions, and the supplier's delivery period is unstable.
Based on the advantages of the full cycle of data value mining such as data governance, data exploration, model training, and strategy application, Digital China has helped the car company build a data solution for the automotive spare parts supply chain by collecting and monitoring data and KPIs of the entire process such as supplier production, supply chain logistics, and inventory turnover.
For a certain automotive parts after-sales service department, model optimization has been conducted on the order forecasting and inventory levels at various levels of the automotive parts distribution warehouse. The accuracy of inventory forecasting has been improved from 92% to 97%, aiding in the optimization of hundreds of thousands of spare parts inventory.
The optimized spare parts inventory level is significantly higher than the current status, ensuring that the first-time fulfillment rate of spare parts is maintained at 95%, markedly enhancing customer satisfaction and service response speed.
A KPI monitoring system has been established for key indicators such as forecast accuracy, inventory levels, WHO, and service levels. In case of anomalies, it triggers timely alarms and conducts root cause analysis, followed by adopting corresponding strategies.
Through model optimization of order forecasting and inventory levels at various levels of the spare parts distribution warehouse, dynamic optimization of safety stock is achieved. A MIN-MAX replenishment strategy is adopted, which automatically triggers replenishment when the stock falls below the minimum value.
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