The latest buzz word in the tech industry is Machine Learning (Hereafter we will call it ML). ML comes under artificial intelligence. ML gives computer software the ability to learn, correct themselves and improve their task performance. In simple words, ML enables the software to take own decision and be intelligent. In the truck industry, every process can be made smart if we use ML. Truck fleet management industry is poised to reach $16 trillion by 2025.
Below are some ways on how ML can help truck fleet management
Machine Learning helps monitor both inventory and truck loading at the same time in a balanced way so that one is not affected by the other. Machine Learning can even manage the suppliers and the number of trucks available for delivery. Smart learning algorithms in ML offer all these information ahead of time so clients know the exact price and availability of inventory for future delivery. Machine learning also offers the best strategy to optimize inventory using data analysis.
One of the important reason for using Ml is cost reduction. Since all the stakeholders involved in truck logistics use apps loaded with machine learning, all the process make wiser decisions and help reduce overall costs, and improve delivery systems. Overall costs including inventory costs are reduced because of ML. ML also aids in quick customers redressal and response.
Visual Pattern Recognition
Artificial Intelligence coupled with ML can recognize patterns and images, which can be used to inspection and maintenance in supply chain network.
Load Cost Estimation
Since the price of the product is volatile, it is difficult to estimate the cost quickly. Machine learning features help monitor these conditions and choose the right price based on delivery time. Machine learning uses previous data to predict future stats and figures. Machine Learning algorithms also monitor a series of factors such as traffic, weather, socio-economic challenges that help companies reach a fair price.
Already there is a route optimization tool used by many truck logistics firms. But ML-based route optimization is more efficient since it takes lots of factors into account before giving the optimum route.
Machine Learning works with an enormous amount of data to make the system smarter and better. Apart from that, this data can be used to extract some meaningful information using data analytics. Forecasting demand for new products, inventory requirement, distance traveled by trucks, costs incurred per delivery can be ascertained using the report from data analytics. Companies are now coming forward to keep their ML systems updated to have an efficient data analytics process.
Technology innovation is the important driver in any industry, as the logistics industry stresses under the pressure to deliver goods faster and cheaper. The supply chain system should be constantly innovated. The on-demand apps incorporated with machine learning offer a convenient solution for this dynamic industry.
More can be done using machine learning apart from the above uses. The data we get from ML can be used to revolutionize the truck dispatch business. It is important to make use of these data properly and it is complete with the user. ML combined with artificial intelligence and the Internet of Things will always position your business ahead of the competitors.
Since, truck industry is working with various other fields like manufacturing, oil & gas, and energy, adopting ML indirectly benefits these industries also. This is an innovation that is helping reshape the logistics management and supply chain industry.