The number of people using mobile devices in the U.S. is increasing rapidly, and they’re also using more internet than ever before.
That makes it easier for companies to increase the volume of goods that they can deliver to customers, and increase the speed at which they can do so.
The question is, how do you ensure your company can scale to deliver those goods?
A team of economists at the University of Southern California and the University at Buffalo has come up with a new approach that uses data to determine the most efficient ways to deliver goods to the customer.
The result is a framework that can help companies optimize their delivery, according to the researchers.
They developed a framework called SchnelleCke Logistics, which helps companies optimize delivery through the use of predictive modeling, analytics, and optimization techniques.
The framework is based on an approach called predictive analytics.
This type of data collection has been used to develop a predictive modeling tool called predictive logistics, which uses analytics and machine learning to identify the most effective and efficient ways for businesses to deliver products to their customers.
A company could use the framework to evaluate how well they can predict how quickly a product will arrive in a specific customer’s home or on a certain delivery route, and optimize that delivery accordingly.
If a company wants to do more than just predict how much it can deliver, they could look to how it can make the deliveries to those customers more efficient and cost-effective.
For example, the framework could provide insight into how to optimize a customer’s transportation logistics strategy for optimal efficiency and cost.
The researchers found that the model can be used to identify companies with the most successful and efficient delivery strategies, and then to predict how to improve those strategies, using data from over 50,000 deliveries in more than 200 countries, and data from more than 5,000 products and services.
“Our framework allows companies to focus on delivering their customers’ needs, and reduce their costs while maximizing their productivity and efficiency,” said researcher Michael J. Schaffer, associate professor of economics at the USC, who led the research.
The research was published in the Journal of Economic Literature.