Artificial Intelligence

Techmakers artificial intelligence can be useful for automating and optimizing heavy data processing processes

Once upon a time there was a customer who accidentally ordered 7 tons of washers.
This seems to have nothing to do with artificial intelligence but due to this human error the idea of making a warehouse intelligent or even better of having an intelligent management of the stock according to the incoming orders was born: the stock management was connected to the techniques of Artificial Intelligence which can also be used in the IoT world.
In this warehouse we find more or less 40.000 different types of replacements most of which were produced for every single incoming order.
This supplying system has brought to an immobilization of stocked products of more or less 7 million euros.
Our client knew we had management experience and thus asked us what he could do to avoid errors and optimize his warehouse and always have the needed articles in stock.
By logic you can predict future orders by looking at the historical sales data and thus decide when to produce the single articles.
It is not very difficult to collect these informations but it takes much time if you think that there are 40.000 articles in stock!
This is when our Artificial Intelligence comes to turn.
Techmakers automatises this process by applying AI (Artificial Intelligence) techniques such as neuronal networks which can be trained and programmed to make previsions or to identify patterns of repeating data.
This task is often difficult as the results are needed as quickly as possible and thus a network of connected servers is necessary so that the master gives different tasks to the slaves to optimize time. Every server has a copy of the installed neuronal network and receives selling data which can be analized and put in a preview of the following 12 months.
Only the first six months of the report are then considered which are being updated week after week by the system and thus give constantly a current preview.
The resulting data are then divided in different groups and represented in graphics so that one can easily see the results and product trends.
In this way the operator can decide with a quick glance whether to allow the system to make the single orders according to the preview or to order the products in the traditional way (e.g. in case of order with little historical data).

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