AI in logistics: the best use cases for 2024

Stefan Seufert, CTO/Vorstand EIKONA AG
Young woman with black glasses talks to a robot sitting in front of a screen.

It could be described as THE hot topic of 2023: AI. Artificial intelligence wrote and published songs this year, answered questions in chats and even developed entire texts – more people worked with AI in 2023 than ever before. Artificial intelligence has achieved great things in logistics in particular: It has optimised processes millions of times over, performed routine tasks as if by magic, significantly increased work safety and quality – the list could go on and on. This is why AI can no longer be described as a trend, but rather as a useful tool that will fundamentally change processes and workflows, and even entire business areas. This article presents insights on how artificial intelligence will support logistics in 2024.


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Predictive and preventive maintenance: repair before it's too late

It is one of the absolute horror scenarios in every company: An important order has to be completed urgently and the machines are running at full speed. But suddenly, there are hectic flashes, the machine stutters along until it finally breaks down completely. Production is at a standstill for the time being, work cannot continue, the technicians have to get to work, and everyone hopes that the problem can be solved soon. The ability to plan maintenance work regularly and intervene in good time before a problem becomes apparent is therefore worth its weight in gold.

AI makes this possible: Predictive maintenance and preventive maintenance are two approaches to prevent the worst case. In the age of Logistics 4.0, it is easy for computer-aided software to create forecasts for the life cycle of components. This makes it easy to maintain an overview of all systems and maintenance plans without missing anything. Maintenance measures, such as the timely ordering of spare parts, can also be easily coordinated digitally. This ensures that your devices and machines are reliably ready for use.

Preventive maintenance:

Schedule maintenance at times that minimise disruption to the production process. Employees check the condition and performance of the system “condition-based” and decide whether action is required. Data from previous maintenance loops helps to better estimate when a component’s life is likely to be exhausted.

Predictive maintenance:

Thanks to comprehensive data sets, this method eliminates the need for human inspections. Numerous sensor data logs the condition of the system so that information on temperature, humidity etc. can be used to predict with a high degree of probability when a component needs to be replaced. Appropriate measures are then taken in good time.

Warehouse space optimisation with the help of AI: ensure availability and still save space

Keeping just enough goods in stock in the warehouse – a true art for logistics specialists. Where previously only experience could help when it came to deciding what variety of a product should be kept in stock, AI can now do many things in the blink of an eye. Minimising stock levels while ensuring the availability of products at short notice is the balancing act that data-fed software can master.

Thanks to smart technologies, intelligent warehousing looks like this: The system first calculates the sum of the optimum safety stock levels for various degrees of overall delivery capability. This allows users to quickly recognise how high the required stock levels are, depending on the level of delivery readiness to be achieved. This results in different costs. Once the desired level of overall delivery capability has been selected, the system calculates the required safety stock level for each product in the portfolio. Depending on market demand, these calculations are then adjusted over time.

Returns management: artificial intelligence ensures more efficient shipping processes

In Germany, we are European champions in this discipline: yes, we are talking about returns. Conversely, this means that logisticians must also become masters – in returns management. To cope with the mass of parcels that have to be transported, registered in the system, checked for quality and delivered to their final destination would be almost impossible today without the support of AI.

Two approaches help to facilitate these processes: preventive and reactive returns management. As the name suggests, preventive returns management is about avoiding the return request as such. Precise product descriptions, informative images and videos, customer reviews and, above all, a transparent and fast delivery process help to ensure that the recipient receives the product at the right time and keeps it. On-demand delivery is one of the important keywords here (more information on this can be found in our last blog post).

Reactive returns management comes into play when a return does occur: For example, software helps to provide shipping documents and returns forms in an uncomplicated manner so that the shipment can be dispatched without any problems and assigned directly. Quality control results can also be easily integrated into the system so that the software can derive forecasts on frequently occurring problems with a product. In addition, appropriate algorithms can be used to create customer profiles that allow conclusions to be drawn about order frequency, the probability and quantity of returns or frequently occurring product combinations. A company’s inventory planning benefits enormously from this.

AI trend knowledge management: no information gets lost

When long-serving colleagues leave the company, it is not only a loss for social reasons, but also professionally – after all, they have built up expertise over the years that is not so easy to replace. What dimensions a consignment should have in order to be approved for express delivery, how to restart the hand scanner if the display froze and which machine is most susceptible to faults – answers to these questions can quickly be lost due to staff turnover.

How nice would it be if there was an in-house chatbot that was fed with all the knowledge that accumulates in a company? Within seconds, the AI bot could answer questions such as “How heavy can a consignment be if I want to deliver it by 6.30 pm tomorrow?”, making it easier to quickly train new employees. AI knowledge management is therefore a topic that definitely deserves a place on the agenda in 2024!


Conclusion

AI in logistics: from A for autonomous driving to N for delivery notification

Whether it’s increasingly autonomous vehicles, digital document management or process automation: AI now covers the entire alphabet of possible services in logistics, and development progress is coming ever faster. However, this is no reason to be afraid of losing your job. AI should be seen as a valuable tool that can take over unnecessary work steps, simplify processes and make our work safer, for example in the interaction between humans and robots.

It remains to be seen what will happen in 2024 in terms of drone deliveries for remote locations, exoskeletons for safer working in warehouses, voice assistants and autonomous driving. Technologies based on artificial intelligence are currently being developed more than ever before, and the use of AI technology in companies worldwide has more than doubled since 2018. Companies should therefore seize the opportunity to rethink their processes and consider the use of AI-supported software. Artificial intelligence can provide support in almost every area of the logistics industry – whether it’s warehouse optimisation, returns management, voice assistance in driver apps or databases and knowledge transfer. We will keep you up to date on the progress!


Stefan Seufert
Stefan Seufert
CTO

As a design guru, the software developer delves into logistics service providers' requirements like no other. He is passionate about exchanging information securely and efficiently and thus speeding up the physical logistics process.


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