Predict, plan, save: see how accurate routing reduces time, emissions and cost
Our map of the Port of Long Beach, California, lets you calculate different routes and shows how you can save money, fuel and time.
The Port of Long Beach takes in 31% of imports to the US. From there, goods are taken to fulfillment centers and then distributed around the country.
But while these routes play a critical role in the American supply chain, many delivery companies — some estimates put the figure at 82% — still rely on manual processes.
Using paper maps and fixed routes allows fleets to calculate a trip based on the length of the journey, for example. What it won't do is calibrate for information such as type of vehicle, construction works on a particular road, whether you are traveling during rush hour, and other temporary changes that make a huge difference to trip times.
|Predictive supply chains: fast facts|
Predictive supply chains
Adding real-time location intelligence into the mix also allows for post-trip performance analysis.
However, the ultimate goal for supply chains is to move to a predictive model. As the interactive map shows, seeing how much you can save for many trips over time is useful for business decisions and forecasting.
Predictive supply chains use a deep learning-based predictive algorithm. In this phase, users can create accurate multimodal ETAs and replace time-consuming, manual tasks, such as load and capacity, optimization and risk analytics.
Predictive ETAs require data for training and testing, including substantial historical data covering seasonal patterns. The quality of the data matters as this will make the model more accurate.
However, in uncertain times, it is clear there is much to be gained from understanding your truck's routes and improving on them.
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