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Why an AI solution for logistics operations requires focus

Updated: Jul 30, 2019

While many logistics companies will be able to benefit from a one-size-fits-all-solution, they all have their own, unique challenges. And that’s just the thing with a one-size-fits-all: it fits no-one perfectly. Here is why a solution for logistics operations requires focus, instead of trying to tackle every individual challenge in one go.


To start off with one of the most important reasons: many logistics companies already have their own solutions in place. There are a lot of different vendors, offering many different technologies and solutions that each have their own benefits. Nine out of ten companies have Office365, or something else closely resembling Microsoft’s suite, in place. Many logistics operations are already using tools for supply chain and fleet management. Their fleets of trucks and the people driving them are all using mobile apps for navigation and route planning. Many of these solutions have been in use for a long time, which 1) means organizations have gotten used to them and 2) vendors will have perfected their offerings thank to years of experience and customer feedback. That makes it very hard for new offerings to introduce an all-encompassing solution that replaces what is already there.


Moreover, starting completely anew can be difficult for a number of reasons. As stated above, most companies already have an infrastructure in place, which will have required serious investments. What’s more, migrating data from another system can be very complicated. And then there’s the fact that most employees will have gotten used to certain software and routines. Having them switch to other, new applications may require a lot of time to adjust. Organizations might even have to invest in training sessions. These are costly matters, that require companies to invest a lot of time and effort. So, instead of a one-size-fits-all, companies should therefore be looking for a solution that enables them to do more - but without having to throw the baby out with the bathwater.


Luckily, Technological innovation, specifically in machine learning and evolutionary computing, can be used to help logistics companies address certain challenges head on. This includes predictive route optimization, effective capacity planning, anomaly detection and dynamic delivery management. However, as stated above, it should not replace existing infrastructure. Rather, it should work seamlessly with what is already in place and expand its functionality. Organizations can choose to use its built-in interfaces but can also integrate the solution into their existing infrastructure using an API, preventing their employees from having to learn how to use an application. The same goes for the data: the solution will provide a simple, structured dataset. While it can be analyzed in the interface the solution provides, organizations do not have to: they can also use their own Enterprise Resource Planning software and decide next steps.


Organizations can also decide to use the solution without integrating it into their infrastructure. In that case, they can just run it in the cloud, and pay per use – instead of a monthly fee. Whenever they require computing power, they can just lease it.


In summary, when it comes to making use of new technologies to improve day to day operations with prediction, advanced data analysis and optimization, offerings that enable a seamless integration with existing solutions are critical to make such transitions successful. Instead of radical replacements, AI solutions should be able to help its users achieve more with what they already have, and then step by step improve the way they execute their works.


By adding focus to the solution for logistics operations, organizations will be able to improve what they have and capitalize on the investments they already made. Both the product structure and the pricing model should make this possible.


If you want to learn more, contact us under info@asknorma.ai or visit www.asknorma.ai.


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