AI IN TRANSPORT & LOGISTICS
The sector of transportation and logistics has always been producing a lot of various data and is already to some extent applying AI and Machine learning in its operations. Today the difference is not just only that we have access to big data, computing power, and the algorithms to create new value and competitive edge, it is also very much about being curious in an ever-changing world where the pressure gets higher and higher due to many other emerging technologies.
Today AI can analyze and improve many different areas in logistics and transportation companies, meaning that people can be moved from iterative and low-value activities into more value-creating and customer-facing ones. In the supply chain, there are useful examples of how AI can search among thousands of possible orders, routes, and schedules to find the most optimal selection – all in a couple of seconds. AI can also help with predictive analysis, for example, predict when a customer will be ready to reorder, or when and which vehicle or machine needs preventive maintenance. Cancelling of departures is another very costly area of concern for many transportation companies, and the closer to departure, the higher cost of cancellation is. Here AI can make a significant difference and minimize cost, yet increase NPS as it gets done at the optimal timing, excluding the human factor of good faith.
The area of use is ever-expanding, but most common examples are listed below.
The use of AI in Transportation & Logistics
- Predictive and preventive maintenance.
- Churn for passenger transportation and also B-2-B transportation.
- Predicting sales order management, including internal procurement and planning.
- Optimizing fuel consumption.
- Assisting in customer segmentation.
- Predicting and improving optimized product pricing.
- Suggesting X-sales and Up-sales.
- Improving stand-by predictions and backup costs.
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