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AI Data Ingestion
95% of supply chain professionals think AI will impact logistics but many still use pen and paper to manage operations.
According to a recent survey by Freightos, 95% of supply chain professionals think AI will impact logistics but only 7% are actively using it.
Perhaps the largest barrier to entry to adopting AI is reliance on pen and paper, which 50% of companies still use to manage logistics operations.
AI requires large amounts of digitized data to automate tasks. In machine learning, a common rule of thumb is to have 10 times more data samples than parameters. For example, if a dataset for a logistics use case had 10 columns, it would require at least 100 rows of data. AI learning models require even more than this.
The problem is, most data is kept in filing cabinets and lost through analog communication. Therefore, it cannot be used to train AI learning models.
A company could go through great lengths to digitize the data they already have, but AI requires fresh data as well as historical data to work as intended. So, before adopting AI, a company must adopt processes for real-time data capture in digital formats.
This means digitizing everything from bills of lading and manifests to automatically capturing data on inventory and shipments as it moves through the supply chain.
“The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.” - Bill Gates, March 2023
Without question, AI will transform the supply chain and logistics industry. But before companies become part of this evolution, they must adopt technology that existed before AI. Software like warehouse management systems. Hardware like mobile scanning devices. Even RFID tags and drones for larger operations.
Of course, you don’t have to digitize everything to use AI. It all depends on which use case you want to automate.
For example, warehouse automation requires data about inventory levels, order volumes, order frequencies, product dimensions, and storage capacities while route optimization requires data on traffic conditions, road closures, real-time location data of vehicles, customer locations, and delivery time windows.
And make no mistake, more money spent on data acquisition and AI means less money spent on employees. In the same Freightos survey, 84% of supply chain professionals said AI will lower headcounts.
What do you think?