A good logistics company will plan and operate according to data and computing. If a logistics company gave up on computing, it would mean giving up the future and survival. The logistics in the future will exhibit many characteristics, such as connectivity, data-driven. That said, all elements in logistics would be connected and digitalized. Data will be used to drive insights, decisions and actions. Deep collaboration and highly-efficient execution would form among enterprise groups, enterprises, and organizations. The logistics system as a whole will be optimized based on a smart algorithm and all parties would have their part of the role, which they would fulfill efficiently.

S.F. Express has rich data, such as data on waybills. If we could turn data into information, an immense amount of value can be produced. The data-driven smart logistics will be a deep collaboration on a societal scale. It will not be just about one company. S.F. Express today is more open. Our Hive Box is accepted by the consumers in a short time. This shows the tendency of connectivity, data-driven, deep collaboration and efficient execution.

Four core elements and five transformation strategies of AI

There are four core elements of AI: data, use scene, technology and algorithm. Firstly, you must have data, a very fundamental element. Then you must have a use scene. Without it, you have issues. Thirdly, you need to have the technology to pull it off, which includes hardware, software etc. In the future, the hardware might be designed by algorithm models instead of having only some options of hardware, because every model deals with different issues and data. There isn’t a single hardware system can handle these many complex issues and different data. Fourthly, the algorithm should be constantly studied and improved.

We work with prestigious universities both home and abroad and tech companies as well. S.F. Express wants to gather and connect the most talented minds and companies in the world to study these challenges and bring about solutions. We want to help the Chinese logistics company get to the next level.

There are five strategies for transforming AI, including successful case, data ecology, technical tools, seamless work process accessibility, and an open culture and organization. First you need to have a successful case. You can’t start something that’s unrealistic in the very beginning. Starting from scratch is hard. Besides that, you would need data and seamlessly integrate it into the operation and achieve a terminal-to-terminal data ecology. In addition, you need an open culture to enable the communication between experts, scholars and enterprises from all fields. Otherwise you can’t achieve the transformation of AI and smart logistics.

S.F. Express chose to adopt a diverse strategy because our vision is to provide our clients with more services based on our comprehensive logistics service ability. The services I am talking about here include business service, financial service, data and technology service.

S.F. Express is rich in data, including data on logistics operations like waybill data, logistics center data, IoT data, client sensing data, business data, financial data and data from external operations.

Six business scenes for S.F. Express’s AI

S.F. Express’s AI application scenes include smart logistics, smart service, smart decision-making, smart management, smart map, and smart packaging etc.

S.F. Express has tens of freight planes, tens of thousands of trucks, several thousands of logistics facilities, over 200,000 delivery staff. This is our company’s body. This body requires high collaboration and intelligence. In the upcoming years, we will apply AI and promote AI in every field. I have personally experience what it is like to be in the very forefront of the logistics industry with our R&D staff. We wanted to observe what parts of the job are highly repetitive that can be replaced by technological means so as to free human workers to engage in works that generate more value.

Our company has many excellent employees, but many daily works of theirs are very similar and repetitive. We can use machine learning to train an artificial brain to help make decisions. Eventually a smart brain that evolves itself can distribute tasks and manage the operation centrally, making sure every decision and execution serves to be optimal and that our clients can enjoy the best service.

The greatest challenge on service is maintaining consistency and stability. Our clients felt that logistics companies’ service quality is not stable. Sometimes the service is good, sometimes it’s bad. Sometimes it’s fast, and sometimes it’s slow. It’s just not stable. In the future, smart logistics can insure the consistency and stability of the service.

Let’s talk a little about the forecast on business volume. At present, business peak time is driven by sales promotions and shopping festivals, which is a great waste of social resources. To plan and allocate resources, we need to make forecast on the business volume from different perspective. We would even predict the business volume in the future five years of ten years. We also make forecast on the volume in a few days. We try to use technologies and methods like machine learning and time series analysis to make all kinds of forecast and study them. We look for elements that affect the business volume, such as the weather, the season, industrial structure, government policies, and GDP etc.

Cases of AI application