The field of predictive technology has been making astounding advances over the past few years. With the general increase in computer processing power, computer programs and algorithms are now able to make advanced predictions in a variety of new fields. Analytics is one such field where new predictive algorithms can help use historical statistics to make accurate predictions regarding future events.
Predictive analytics can help businesses run more efficiently, as it can give them accurate predictions for different business decisions, which will then allow the company to select the option with the best output. Historically, businesses would have to make “best guesses” as to how to move forward with their business models. If they had a choice between two, it would come down to an executive decision, and if the selected option didn’t turn out as planned, they may try the second option down the road to test its efficiency. With predictive analytics, you can run a simulation of both strategies at the same time and use the results to make a more informed decision.
Predictive analytics works by using computer algorithms from predictive modeling, machine learning, and data mining to run simulations based on the input data. The more historical data available, the more accurate the prediction can be.
This can obviously have huge implications in the freight industry, as it can help brokers and carriers with future shipment planning, inventory management, driver/freight matching and more. With this, carriers and brokers can become more proactive with their management. Instead of waiting for clients to come to them with shipment needs, they can better anticipate the needs of their clients and have freight trucks and drivers at the ready.