Revolutionary Logistics - GenAI in Warehousing and Manufacturing
Targeted optimization of operating processes
Generative Artificial Intelligence (GenAI) is well on the way to fundamentally changing logistics in companies. This technology makes it possible to handle data in a completely new way.
Challenges in Logistics
A major challenge in logistics is data silos that prevent data from being exchanged efficiently within the company or between companies. This is particularly the case when many different data sources and systems are in use in a company.
One of the biggest challenges faced by those responsible for logistics is good reporting, which can be used as a basis for making informed business decisions and controlling processes and workflows. The core of this challenge is to bring together the available data and the requirements of the people involved. Today, several people with different qualifications and many work steps are often required to create a good report from data from different systems. In addition, flexible changes to complex reports are often associated with further effort.
Another challenge in the field of logistics, which goes hand in hand with the use of many different data sources, is the emergence of shadow IT. Shadow IT arises when employees use their own IT solutions without these being officially approved or monitored by the company. In addition to security risks and the fundamental impairment of efficiency, this shadow IT also stands in the way of AI readiness, as the data from these IT solutions is not available to the company.
Achieving AI Readiness
An essential aspect in the implementation of GenAI is what is known as AI readiness. This means that companies must be ready to integrate artificial intelligence into their existing systems. Data protection and security play a crucial role here. In a world where data is becoming increasingly relevant, it is essential to protect sensitive information. Companies must ensure that their AI systems meet the highest security standards to prevent data leaks and cyber attacks. This requires not only technical measures, but also a shared culture of data protection within the company. In addition to data protection, the availability and quality of data are other decisive factors for AI readiness.
If Generative AI is to be used successfully in a company, it must be ensured that the required data is available and comprehensible. Only then can the AI use this data to play to its strengths.
Advantages of Using GenAI
Generative artificial intelligence can create new content based on known data and is therefore also ideal for processing big data and extracting content and conclusions from existing data.
Knowledge management is another area in which GenAI can offer significant added value. In the manufacturing industry, it is crucial that knowledge is passed on quickly and efficiently. Artificial intelligence models can help to structure information and make it accessible. This not only facilitates the exchange of knowledge, but also ensures that important information is not lost.
GenAI solutions for Practice
An example that puts Generative AI into practice as a technology with a very specific use case is offered by platbricks®, the modular solution for logistics from Arvato Systems. With "Chat With Your Data", users have the opportunity to create individual key figures and statistics based on existing data in real time. This works entirely without individual development, but through simple chat input in natural language.
In addition, recurring queries can be saved and compiled on individual dashboards, also completely without individual development.
In addition to the use cases in the area of reporting and analytics, there are other fields in which Generative AI can play to its strengths in logistics. With platbricks®, for example, common logistics documents such as delivery bills or packing lists as well as material and shipping labels can be adapted using Generative AI. This means that small and individual changes can be made quickly and easily. In the future, other use cases such as warehouse invoicing, controlling orders in the warehouse or reading delivery bills using artificial intelligence will further change logistics and optimize processes.
It can therefore be concluded that generative AI in logistics, especially for companies in the manufacturing industry, offers the opportunity to contribute to solving current challenges with various use cases. One prerequisite for this is achieving AI readiness within the company.
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