Artificial Intelligence and Machine Learning In The Warehouse

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) can be found in every corner of the logistics sector, used in applications designed to enhance productivity, improve inventory management and integrate robotics in operational processes. The widespread accessibility and the vast array of these potential applications has led Forbes to predict that the AI/Machine Learning industry will grow 1200% in seven years, as it becomes increasingly adopted by companies from all sectors, worldwide. 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, self-driving vehicles, and cloud logistics just to name a few.

What is AI / Machine Learning?

AI works by imitating the human brain’s learning process. Rather than utilising traditional, machine friendly, ledger style databases, AI applications instead create large networks of data points (called nodes) and form connections between them by analysing ‘big’ datasets and spotting commonalities.
With a large amount of data, machines’ neural networks are capable of spotting patterns and repeatedly ‘connecting’ nodes. The more times two nodes are connected in the data, the higher the probability there is that some form of causality exists.

Machine Learning is a branch of AI 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. Machine Learning combines low-cost sensor technology, robotics and the principles of deep learning, to create machines that can interact with and learn from the physical world we live in. It’s a vast area of computer-science with many applications, but it’s already highly visible such as virtual personal assistants (e.g. Siri, Alexa, etc.), facial recognition and self-driving technology, all examples of machine learning that are becoming embedded in our day-to-day lives.

Deep Learning, is a specific form of AI that is used to analyse ‘big’ datasets and identify patterns that were previously hidden. This is achieved by imitating the human brain and creating artificial neural networks. These, are similar to our own, in that they are comprised of a large number of data points (nodes) that become associated with one another, and form links, as data is consumed. The more data the AI consumes, the deeper its understanding.

AI and Machine Learning for Warehouse Operations

AI has the potential to integrate every element of warehouse processes, from goods-in and storage, through to picking, dispatch and last-mile logistics to significantly enhance operational efficiency. All these pinch-points can become more agile, able to respond far quicker to changes in logistics operations or customer demands, making operations more streamlined, efficient and in a position to optimise business growth.
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.

Impacts of AI and Machine Learning in a Warehouse setting

5 areas AI is positively impacting warehouse operations:

1. Inventory management

AI offers considerable cost efficiencies for inventory management through technologies such as RFID (Radio Frequency Identification) and bar code scanners. These technologies provide warehouse management with both efficient organisation and accurate, real-time control of inventory. Smart inventory is vital to enhance pick efficiency, order processing and replenishment, all required to meet the ever-changing needs of the customer.

2. Enhanced Communications

AI and machine learning algorithms are able to communicate at a significantly faster rate than human operatives. For warehouse tasks utilising elements of automation, the data available via IoT devices can feed into a warehouse management system providing real-time analysis which can lead to immediate adjustments to further enhance speed and accuracy.

3. Warehouse Logistics

AI offers valuable insights to aid efficiency improvements in warehouse logistics. By optimising the flow of goods on a daily basis through assessing order processing per SKU, and how many operatives and machinery are required to facilitate this, will enable warehouse operations to be streamlined and efficient. Machine learning algorithms provide in-depth analysis with forecasting capabilities not previously possible through mainstream Warehouse Management Systems or ERP and deliver an effective route to overall productivity improvements.

4. Pick Efficiency

One area that AI has proved particularly effective is for pick-and-pack processes. Machine learning enables warehouse managers to leverage crucial operational data which is then processed and fed through by a number of channels to manual operatives. From heads-up displays to Augmented Reality assisted picking, AI provides pickers with the most efficient route and order for picking and continues to evolve with the ongoing analysis of data being assessed using AI technology.

5. Robots

Just one of the many examples of Machine Learning in a logistics setting is the use of the collaborative robotic trolley (CRT). Fully autonomous, CRTs are capable of safely following pickers around the warehouse, before taking picked orders back to a drop-off point (independently). They are then replaced by another CRT in order to keep the picker constantly active. Not only does this enhance productivity, it also decreases the physical effort required by the picker to lift and carry items from their SKU location, to a pick & pack station.
In a recent study carried out by SEC, CRTs were able to generate a 34% increase in productivity, even in relatively small warehouses, and generally returned on investment in less than 18 months.

“Robotic picking and collaborative picking technology have proven very effective and reliable in modern warehousing.” Markus Voss, Global CIO & COO, DHL Supply Chain