Machine learning is definitely an important topic in the technology sector. There are plenty of industries who are trying to develop strategies around their growth plan on the matter, given the fact that there are proven results of how machine learning applied to productivity works. Let's analyse some big examples that are leading the industry at the moment.
Alibaba Production Chain: Automated
Alibaba has recently fully automated its stocking and shipping centres in China by using robots controlled by a sophisticated machine learning algorithm. The video of these "workers" went viral all over the world, significantly raising the topic's awareness. Amazon, for example, announced that they plan to automate 45% of their entire warehousing workflow, by utilising both machine learning, robotics and deep learning technologies.
How Does It Work?
Although is still considered machine learning, these applications are not properly related to the original matter's concept: while machine learning was born and raised around the fact that a particular tool should have learned how to optimise itself and its processes without any manual input, these ones are more related to an "automated management" system. In fact, still taking Alibaba's one as an example, the management side of these autonomous learning tools focuses on dealing with SKUs, sorting and storage places and such. Generally coded in Python, which is the universal Machine Learning coding language, these management applications will definitely become the industry standard in the nearest future.
Costs And Maintenance
Alibaba's move in order to automate their warehouses was something that was estimated in hundreds of millions of dollars. This is due to the fact that the entire system was tailored and developed around their requirements, even from a hardware point of view (with their robots). Although, given the market's demand, the price is more likely to become more accessible to smaller companies, moving and automating an entire warehouse isn't the cheapest move. The next couple of years will be crucial from a market point of view, given the fact that (as said above) the matter has become something that many companies are actively looking after.
Dissecting machine learning from a warehouse management point of view isn't that easy at the moment, given the fact that the entire matter involves costs, development requirements and many more variables. What is safe to say, at least at the present moment, is the fact that automation is coming and it's more likely to impact the entire warehousing workflow (which is up to you to decide if it's exciting or scary).
Article by Paul Matthews. Paul is a Manchester based business writer who writes in order to better inform business owners on how to run a successful business. He collaborates with a mobile app development company on a daily basis. You can usually find him at the local library or browsing Forbes' latest pieces.