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Home>Warehouse Storage>Warehouse storage>Take warehouse efficiency higher with machine learning

Take warehouse efficiency higher with machine learning

09 February 2021

Harry Watts says machine learning is under-utilised in the warehouse but can bring considerable insights without a significant price-tag.

One piece of technology with the potential to have an enormous impact on logistics and the warehouse is machine learning; a branch of artificial intelligence that utilises computer algorithms to look for patterns in big datasets and uses the insights to improve its performance or understanding of a particular problem over time. It's a vast area of computer-science with many applications, but it's already all around you, in examples such as virtual personal assistants (e.g. Siri, Alexa, etc.), facial recognition and self-driving technology.

It is, however, essential to note that Machine Learning is an umbrella term for a variety of sub-technologies that vary considerably in complexity, sophistication and accessibility. At one end of the spectrum, technology giants such as Tesla and Google are spending billions on building supercomputer powered, deep neural networks to create next-generation technologies. But at the other end, small businesses are using simple, DIY-models to improve operational decisions and efficiency, without a significant price-tag.

Although currently under-utilised in the warehouse, machine learning's potential impact in distribution centre design and management is virtually limitless. The language of logistics is data, and consequently, there are big-datasets available in most warehouse operations. The promise of machine learning is that it can take this information and not only make connections virtually impossible for humans to identify, but actually to get better at doing so as time goes by and more data becomes available. These insights can then be fed back to either an IT-system or management to help improve operational performance.

We have developed a machine learning platform that allows us to analyse our clients' picking data and throughput information to optimise the selection of pick-faces for new and existing SKUs. Not reliant on fixed logic-based algorithms like traditional WMS, our program instead leverages historical and current data to assess new SKUs and make accurate predictions about the most efficient type and location of pick-face to utilise. In a recent application for a major high street retailer, this system helped increase pick-efficiency by 34% by ensuring that SKUs were located in the most optimally positioned, and sized, pick-faces. Doing so resulted in both reduced travel distances and replenishment frequency, and better still, the system is still learning and improving as more data becomes available to it. 

In reality, given the right data, machine learning is sufficiently flexible that it can tackle most warehouse management problems in virtually any warehouse operation. Other examples of how we are or will use machine learning in warehouses over the coming years include, improving the quality of forecasting for planning and predictive purposes, and identifying SKUs that should be clustered together due to previously unforeseen connections in buying habits.

The widespread accessibility and the vast array of potential applications has led Forbes to predict that the machine learning industry will grow 1200% in seven years. And, in their logistics trend radar report, DHL predicts that machine learning will be the highest-impact technological trend over the next ten years, ranking it above robotics and self-driving vehicles.

Harry Watts, commercial director, SEC Storage

For more information, visit www.sec-storage.co.uk