By Will Waller
Holiday retail spending is projected to reach its highest level ever, USD 979 billion, in 2023.1 With a forecast like that, the transportation industry is gearing up for one of its biggest seasons in decades. Success will be measured in minutes: how much sooner a shipment arrives, how much time a new trucking route can shave off, or how long a packed palette sits in the loading bay before it’s sent out the door.
Transportation companies are already invested in technology at every stage of their process. Automation and other technology help, but much of it is still limited by its feedback loop, with frontline workers reporting on events within the system.
Even the most advanced touchscreen apps require workers in rail yards and warehouses to move between physical labor and data entry tasks—often having to remove protective gloves in the process. This context-switching creates friction that inadvertently encourages workers to put off data entry until they can do it in batches or to hurry through their reporting, leading to errors. A study by Google found 60% of frontline workers dissatisfied with the technology provided to them.2
Natural Language Processing (NLP) in transportation can remove the friction entirely. With NLP, companies can eliminate numerous wasteful aspects of an under-optimized transportation system, like over-processing complexity, unnecessary movements and gestures, data defects and errors, and delays. Removing these timewasters will free up time so that workers can focus to better use their skills. Those transportation companies that get on board now will continue seeing successful holiday seasons for many years to come.
Harnessing Natural Language Processing in Transportation
The potential uses for NLP within the transportation industry are best thought of in two directions: the flow of information from the user to the system and from the system to the user. Either way, a strong data foundation and good data governance are essential.
The most obvious use case can help the workers switching between physical labor and data entry tasks: speaking inputs directly into the system, in one’s own natural voice. Eliminating the need to remove one’s PPE or break the natural workflow removes friction and encourages more accurate data input, more often. This can enable greater asset optimization and lower the overall costs of transportation.