1. Key Data Dashboard
   Time Matrix Analysis:

python

Example Data Pivot

df.groupby([‘Logistics Provider’, ‘Destination’])[‘Days to Delivery’].mean().unstack()
Cost Anomaly Monitoring: Set a ±15% cost fluctuation alert

2. Intelligent Decision-Making Applications
   Dynamic Inventory Allocation:

Transfer frequently returned items to overseas warehouses

Initiate a elimination mechanism for SKUs with a logistics score <4

Predictive Replenishment Model:

Calculate safety stock by combining logistics time efficiency with sales cycle

Formula: Replenishment Quantity = (Purchasing Cycle + Logistics Days) * Average Daily Sales Volume * 1.2

3. Supplier Collaboration Optimization
   Shared Logistics KPI Data: Require carriers to achieve

96% on-time delivery rate

<3% cargo damage rate

48-hour exception response

Each module can be used independently. It is recommended to regularly review and optimize based on data from Alibaba’s backend “Logistics Analysis Center.” Data-driven decision-making can reduce logistics-related operating costs by 15-20%.