Following Manhattan Associates’ recent EMEA Exchange conference in Barcelona, Manufacturing & Logistics IT spoke with the company’s UK managing director, Craig Summers, about the current state of play within omnichannel retail and how it is now more important than ever for retailers and warehouse professionals to be able to serve both brick and mortar and direct-to-customer delivery models equally efficiently.
This second part of the interview looks at how artificial intelligence and machine learning in the modern warehouse or distribution centre can play a key role in helping to optimise this type of omnichannel methodology.
Within the retailing sector, distribution centres (DCs) and warehouses historically were designed to replenish brick and mortar stores, where it was all about getting quantity to the outlet on time. However, with the advent of e-commerce warehouses and DCs now need to be able to cope not just with bulk orders to the physical outlets but also one-off parcel deliveries to individual consumers who have ordered something online – having it picked, packed and ready to ship for delivery often by 4 o'clock on the same day.
The traditional methodology has been to rely on ‘wave-based’ picking in the warehouse. However, within today’s omnichannel environment, Craig Summers, Manhattan Associates’ UK managing director, points out that the process of picking, packing and shipping needs to be more dynamic where single orders can be interleaved within the schedule almost in real time as the day progresses. “There is a lot of talk currently around artificial intelligence (AI) and machine learning and how this type of technology can make things more efficient in the warehouse environment,” he explains.
“For example, Manhattan Associates’ Order Streaming capabilities can help companies to manage changing conditions and priorities better in order to be able to improve their operational efficiencies. The key is to ensure that by accommodating next-day or same-day parcel orders direct to online customers the main operations in the warehouse aren’t disrupted, and a high level of overall process efficiency is maintained.”
Summers points out that as warehouses and DCs approach the end of a picking wave they can start to lose some of their operational efficiency. “So, waveless, or more regular injections of workload orders, makes a lot of sense,” he says, “but you need to really understand your operations well in order to be able to do that effectively. This is where AI and machine learning can prove to be a real benefit.”
Growth of awareness
So, are warehouses and DCs currently embracing AI and machine learning technology? Summers considers that, in terms of the importance of interaction between people and machines, there is an encouraging growth of awareness. “The need for this kind of seamless interaction is one reason why more companies are now turning to AI and machine learning,” he says. “Take a conveyor as a simple example; one of the simplest pieces of automation in the materials handling world. If you’re not effectively managing the conveyor and people in harmony you either have too many goods coming in and not enough people ready to manage them, or you have too many people waiting for something to happen.
So, there should be an acceptable balance. Even at highly automated sites people are still absolutely key and our approach is to best ensure the harmonious and efficient working of both human and machine.” Summers added that with the growth in use of collaborative robotics in the warehouse environment, this is another area where machine learning can improve the relationship between human and machine; for example, helping to prioritise co-bots for tasks such as heavy pushing or lifting.
Summers adds that although the benefits of this type of technology can be compelling, there remain certain barriers to greater levels of adoption. “Some of these barriers relate to embedded cultural attitudes,” he says. “If you’re born in the cloud or born in technology then your interest in AI and machine learning is likely to be greater. However, it’s important to stress that simply being willing to invest in this type of technology doesn't necessarily guarantee that you’re going to be more efficient if you're not thinking seriously about the people aspect as well.” Nevertheless, Summers concludes that awareness and uptake of AI and machine learning technology is certainly on a strong upward trajectory.