A post by Nexocode on June 29, 2023
Nexocode is an AI development company that specializes in developing machine learning solutions, including predictive analytics, anomaly detection, and dynamic pricing engines for the shipping and logistics industry. Apart from supporting the full life-cycle of AI product development, the Nexocode team supports clients with building robust, scalable cloud infrastructure and developing integrations with other solutions.
The maritime industry is currently undergoing significant changes, driven by the emergence of new technologies. Predictive analytics has become increasingly vital in the shipping process. The article explores the impact of predictive analytics in the maritime industry and highlights its importance as a potentially valuable tool for shipping companies.
We picked out 3 particularly interesting use cases:
For the container carriers, having access to precise forecasts on supply and demand is a priority as it conditions their financial safety and efficiency. Particularly nowadays, when the global supply chains are getting increasingly affected by the changing geopolitical situation, predictive analytics becomes a powerful weapon against business trouble.
Using socio-economic data (GDP, population, median income, employment rate, etc.) as input and pairing them with detected trends and seasonality in the time series data as well as geopolitical factors, shipping companies estimate the realistic container demand instead of relying on historical data.
For ports and shipping companies, predictive modeling is a win-win. Having accurate predictions, the carriers can reduce the number of empty containers and prepare for the increased demand with additional investments or routes. The ports, on the other hand, end up being at lower risk of congestion since the shipping companies feel safe enough to rely on just-in-time management.
Everyone – the on-land delivery companies, wholesales, and e-commerce shops, and, of course, the final customer – wants to know when and where the shipped goods will arrive. A big part of the consumers may even abandon their shopping cart if they do not get the shipping time estimation right away. Introducing predictive analysis for timing and locations is thus in everyone’s interest.
After identifying the relevant inputs (which may include such features as gross weight, route, the destination port, and so on), the model trained for time or location prediction can come up with accurate output that help the carriers plan their routes, and the ports – effectively manage the offloading process. There are quite some machine learning models that can serve that purpose. Random forest and linear regression seem to be the most popular ones with their relatively fast training.
Even though dynamic pricing model isn’t favored by the customers, it’s becoming increasingly common. With so many dynamically changing factors influencing the final cost of the shipping, the dynamic pricing models save the shipment carriers from operational inefficiency. In such competitive times, they cannot just preventively overcharge, and charging too little compromises their financial safety.
How dynamic pricing strategy works? The model estimates how changing variables impact the price and possible demand for profit maximization.
With dynamic pricing, shipping companies can adjust to the demand-supply dynamic. Such models estimate the cost of the shipping service in real-time, updating the prices at least a few times per day. For example, when the cost of oil suddenly peaks – what has happened a few times in recent years – the model adjusts the estimation, preventing the customers from ordering service at an irrelevant price.
Machine learning algorithms get trained with structured and unstructured data (including the time-oriented and location-specific data) to find the correlations between prices and different variables (like route, distance, fuel cost, demand, season, etc.). This way, they learn to predict relevant prices that fuel sales while sustaining financial security and growth.
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