- Major logistics providers have long relied on analytics and research teams to make sense of the data they generate from their operations. But with volumes of data growing, and the insights that can be gleaned increasingly varied and granular, these strategies are becoming outdated.
- Companies have therefore started turning to artificial intelligence (AI) computing techniques, like machine learning, deep learning, and natural language processing, to streamline and automate various processes. The unique abilities of AI systems to identify patterns in massive data sets and quickly deliver insights based on new information make them more effective for certain functions in supply chain and logistics than more traditional forecasting and analytics tools.
- The current interest in and early adoption of AI systems is being driven by a few key factors. These include increased demands from shippers, recent technological breakthroughs, and significant investments in data visibility by the largest players in supply chain and logistics.
- AI has a wide breadth of potential applications in the supply chain and logistics field. It can be used to create more optimized freight and delivery routes, improve forecasting and asset utilization, mitigate the impact of risks like supply shortages and natural disasters, and provide customers with a more convenient delivery experience.
- However, there are several obstacles keeping organizations from deploying AI at scale. Finding the right data to train AI systems to deliver actionable insights is a common challenge in the industry, and companies are struggling to find skilled employees who can launch AI projects. Additionally, these organizations must figure out new workforce strategies as AI systems start to take on specific roles formerly performed by humans.
The supply chain and logistics space is constantly flooded with data. That includes information around individual shipments, such as pricing, routing, storage location, and destination, and the entities involved with those shipments, like the shipper, carrier, insurer, and customs and regulatory agencies. It also encompasses information about the vast number of variables including weather, traffic, and consumer buying habits that can impact any individual shipment. The growth of IoT technologies like connected sensors and vehicle telematics systems is only adding to the volume and complexity of this data with real-time information on the status of shipping containers, delivery vehicles, and warehouse [...]