AI As A Tool In The Problem Area Of ​​Intralogistics

It is not just the retail sector that knows: Huge challenges are waiting in the next few years from the beginning to the end of the supply chain. I want to focus on the end of the supply chain, the fulfillment. Above all, a future staff shortage – the “new unemployment” – is causing headaches there. But fortunately, workable remedies are already available today.

Suppose you ask logistics decision-makers from retail companies what they see as one of the most critical success factors in supply chain management over the next few years. In that case, most of them expect it to be artificial intelligence (AI). The interest of companies in such projects is already huge, but the actual implementation is still pending.

With a clear conscience, we can place safe bets that these projects will be tackled shortly. From an economic point of view, they promise significant positive effects in terms of inventory optimization, product availability, and increased delivery reliability. And retailers know: they have to solve their personnel problems.

The Shortage Of Staff Is Forcing Retailers To Automate

Retailers today want to be closer to their customers again. Dark stores and micro-fulfillment centers within residential areas promise to deliver goods same-day and within ten minutes. Fast and, of course, at a reasonable price – the competition in retail creates an environment in which everyone involved has to optimize every detail. Retailers know that delivery times that are too long are among the most common reasons for a purchase being abandoned.

However, the staff is also needed to keep the delivery promises, given the ever-increasing volumes in e-commerce. Many camps today still function purely with human labor. However, workers are not only becoming more expensive due to their reduced availability of the rising minimum wage; they are also becoming increasingly scarce and increasingly difficult to recruit. The lack of staff is already causing disruptions in more than a quarter of companies with their own logistics operations.

Every work step that can be automated makes life easier for retailers, and there will be no alternative in the future. This is easiest to do in fulfillment so that we will see more and more of a combination of robotics, machine learning, and AI being used in this area.

AI-Supported Software Becomes The Center And Brain Of The Warehouse

As the center of warehouse management in intralogistics, AI-supported software can simplify and accelerate many processes. As the basis for all logical decisions, objective data enables the ideal use of all available resources and the best possible use of the open space. In this case, the perfect means combining cost-efficiency and speed. Every decision should become the best decision.

The system must be able to adapt to the individual circumstances of a warehouse, such as types of goods, turnover, and spatial design. There is no such thing as a cookie-cutter solution that works the same everywhere and with maximum efficiency. Therefore, it is crucial that software as the “brain of the warehouse” continuously uses the available data and becomes more efficient through machine learning. Over time, she develops increasingly intelligent strategies to orchestrate handling in the warehouse. She keeps the department store in an agile flow by constantly re-evaluating and reorganizing processes and priorities. 

Decisions about the flow of goods have to be made in real-time: Which orders and returns are received, when are there peaks in orders for a particular product? Premium customers may also have to be considered. The more data there is on questions like these, the more effectively the system regulates priorities and routes in the warehouse and the better and more flexibly it reacts to peak loads – all in real-time. Thanks to a comprehensive consolidation and visualization of all data in the dashboard, these activities remain comprehensible to the human eye.

The services that such software takes on also include monitoring, diagnosis, and troubleshooting in the event of problems. Here, the learned database helps to recognize them as they arise. System-critical components remain protected and corrective measures are automatically initiated in good time so that operations in the warehouse can continue to run smoothly and as planned.

The Full Potential: Machine Learning And Intelligent Fulfillment Robots

However, the most exciting application of such an AI-supported central warehouse management system arises when it works together with warehouse robots as a supplement to human employees. Only then can it develop its full potential.

With AI and machines in the background, simple robotics becomes intelligent robotics that sets new standards in fulfillment. Suitable robots are available both for transporting goods in the warehouse as a goods-to-person approach and for picking and finally for the final order picking. They are controlled by the AI ​​of the central software, which pulls the strings of the entire warehouse like a puppeteer. It is irrelevant whether this is fully automated or whether robots and humans work in cooperation. The goods-to-person system optimizes the interaction so that the transport routes are optimized and human employees are relieved of the most physically demanding and monotonous work.

The aim of using robots is always to increase flexibility and cope with peak orders for which there are not enough employees available. After all, a fully automated fulfillment center can be operated around the clock without incurring additional and unpredictable costs.

Relevant For Decision-Makers: What About The Existing Infrastructure?

For many decision-makers, a very decisive factor when considering using such systems is the possibility of integration into the existing infrastructure – both regarding the technology and the current premises.

It is no longer the case that only specially designed warehouses with a specific minimum size can be automated. The technology has become so flexible here that there are options for all warehouse types: pure e-commerce fulfillment centers, dark stores, omnichannel storage areas, or in-store micro-fulfillment centers, to name just a few subtypes. The opportunities to integrate automation grow and grow.

Integration also plays a significant role on the technical side. For understandable reasons, decision-makers are often reluctant to replace their entire systems in modernization. Therefore, the ability to access data from all of the company’s existing IT systems reliably and in full via APIs plays a key role. The AI ​​needs this information about incoming orders, deliveries, and inventory, from whatever sources they come from.

The Fulfillment Of The Future Can Only Be Automated

All current developments clarify that ever broader use of ever more AI and machine learning is unavoidable: Demographic change will further fuel the existing shortage of skilled workers. Irrespective of this, parcel volumes and customer expectations continue to rise simultaneously, driven by the current competitive situation. And perhaps most importantly, the rapid advancement of such technologies improves their economics and viability to such an extent that such solutions will soon become affordable for more and smaller retail companies and even become the most profitable option.

Innovative companies that provide a successful example are essential drivers of progress in fulfillment. But the providers of intralogistics services also have a responsibility to present their products to a large number of decision-makers and to show the advantages to promote the spread of the technology.

If retailers decide to reorganize their warehouse management and support it with AI, around six to nine months of work must currently be expected from the conception to go live. This process will likely accelerate, but it’s still better to tackle the issue of fulfillment early rather than late.

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