Can predictive analytics help the logistics industry?

One of the biggest challenges logistics companies face today is knowing exactly how the global business analyticssupply chain is going to be affected during the course of the year.

That said, it is impossible to pinpoint exactly how the flow of products being transported across the world will work at any given time, but there is a way of accurately predicting trends in the supply chain, and it’s called predictive analytics.

Predictive analytics is a tool used in a variety of different industries, from pharmaceuticals to retail, and can help not only to make fairly accurate calculations about what will happen in the supply chain, but also the best rates and times for distribution, where the highest amount of customer orders are coming from and which items sell well together and which don’t.

So how can predictive analytics work for logistics companies?

It’s not just clever guesswork – predictive analytics uses multiple data streams from a variety of internal and external sources, and even data gathered over the course of a few years. Analysts can then begin to find patterns which could indicate certain trends.

The data used can come from almost anywhere; past customer orders, current customer orders, trends in overall market purchases, how many products were bought online as opposed to any other point of sale, and so on.

Since the arrival of Cloud based networking, data sharing has become easier than ever and has allowed the logistics companies that use predictive analytics to present clients with more accurate estimates.

With almost scarily accurate information on the global supply chain, it becomes easier to anticipate future trends in the market, which in turn can help businesses better prepare themselves and keep up with the demand, producing products that have proven to be popular in the past, or increasing transportation to high impact areas.

Although not by any means a new idea, predictive analytics is the driving force behind logistics. By taking information and applying statistical analysis, logistics companies can take a step back and see where the peaks and troughs in the supply chain will be, leading to better use of people and materials.



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