As the information technology and supply chain management evolve, a major trend has come to view regarding the automated cargo transportation and its efficient management in the logistics industry. However, based on the status quo of China's logistics industry, we are still facing the following major challenges:
1. Logistics companies use disparate systems from their partner, which are hard to interoperate. So some data a cannot be easily handled.
2. During shopping carnivals like Double Eleven and June 18, a massive amount of data may flow in Logistics staff may be stressed with handling such soaring data volume manually. And human errors may be more likely to occur for this process.
And RPA robots can not only help enterprises reduce costs and increase efficiency, and quickly build a bridge to transmit data across systems with their partners, but also add business data processing logics as per business scenarios.
Scenarios in the logistics industry:
1. Automatic delivery
RPA robots can search for goods to be delivered from the ERP system by time period, and automatically generate logistics orders. Then the waybills or orders can also be automatically generated in the logistics provider's system. What the logistics operators should do is simply to check for abnormal situations and adjust orders accordingly. In this way, the workload can be significantly reduced, while data accuracy can be ensured.
2. Logistics status update
The RPA robot can automatically check the logistics status updates by the logistics order IDs from the Web portal or system external to the logistics provider, and copy the latest information to the designated system, which can not only reduce the workload, but also update the data in a timely manner, improving customers' experience.
3. Automatic booking robot
There are dozens of fields available in the booking documents. Due to the diversification of their sources, they usually follow inconsistent formats, so are hard to be automatically imported into the system. And the RPA robot can automatically extract the booking information, log in to the internal system to fill them in and submit. Only when any abnormal data is detected will the human labors be involved to verify, which improves the productivity of the customer service.
