Enhance your clients’ experience with route optimization, forecast shipping demands, and drive operational excellence by predicting the correct capacity, thereby saving precious time and money.
NexusMax enhances our transportation clients’ experience using data to optimize routes, forecast shipping demands, and drive operational excellence by predicting the correct capacity, which together leads to time and money-saving results.
Ride share apps are growing in popularity, and that wouldn’t be possible without data science. NexusMax helps these companies use data science to create an efficient transportation experience. Data is used to find the most efficient route using predictive analysis. Machine learning collects data from traffic history in relation to time of day, construction locations, and unexpected accidents to determine the most efficient route. Not only does this reduce frustration for drivers by cutting down on time spent on the road, but it also improves the experience for riders by making sure they reach their intended destinations on time. The latest feature on most ride share apps is the ability for user to select a carpool option. The apps overlay data consisting of everything from traffic patterns to nearby users and their own intended destinations to match up the perfect carpool, thereby lowering the cost compared to an individual ride for both the company and customers. Data also helps ensure that employees experience a good work environment by allowing both rider and driver to rate one another with ease. This creates a profile and rating for each person within the app, allowing transparency and improving safety for both parties.
Transportation and shipping companies often use vague approximation methods when it comes to forecasting shipping demands. This usually involves analyzing recent order history, but accurately predicting demand is much more complex. Logistical data including consumer trends, delivery locations, and quantities is collected and fed into an algorithm to help business accurately estimate stock and optimize driver routes and delivery schedules to increase margins and overall profits. As more data becomes available, the machine learning algorithm adjusts to provide the most accurate insights in real-time.