knowledge databases, but also to intelligently analyse further sources of knowledge such as assembly reports and feed the contents in a structured way to the
WDS.gpt. This way, thousands of error and solution possibilities were transformed into a structured database, and the capability of WDS to achieve effective error correction and the transfer of know-how across different projects and over a long period
The
WDS.gpt for internal use makes it possible to ask questions on almost any topic in natural language, making it very easy to operate. The system determines the information available in the saved database and puts it together clearly in a very short time. In addition, the sources of information are indicated, so that the answers can be checked and reproduced. In the next step, the knowledge gained shall not only be able to be used internally by WDS but also by their customers. The existing SweetConnect platform provides an ideal basis for this. Research is currently being done on the meaningfully and securely.
AI in the confectionery production plant A confectionery machine produces a large amount of data. Using a smart measuring mould such as e.g. SmartMould, it is already possible to continually record process data in the methods or simple limit value analyses. However, it is still not currently possible to is where the greatest opportunities offered by AI applications come into the picture. The immense quantity of data from the production plant can be processed almost in be evaluated linked with historic data in order to guarantee automated and continual process monitoring. Error patterns or anomalies can be recognised in good time and can often be optimised before they lead to a real problem. An AI application is thus designed to support trained personnel in making fast and goal- oriented decisions. What it is not designed to do is make the use of autonomous machines without operating personnel possible.
Limits in the use of artificial intelligence Alongside the many conceivable possibilities, some data is less suitable for the use of AI. The observation of rules and regulations when handling sensitive data can make the use of AI moral reasoning, which means that when moral decisions have to be made, human judgement
KennedysConfection.com Fig. 2: User interface of
WDS.gpt
Fig. 3: AI-supported detection of data anomalies
must always be preferred over the AI decision. Ecological aspects also have to be taken into account, since AI solutions require a lot of resources. If the expected ROI is low, traditional or hybrid approaches can offer a more suitable safety requirements, such as in medical technology or aviation, a wrong decision can have far-reaching consequences. Risks should always be considered carefully in such cases.
Examples of shop floor solutions with artificial intelligence
An assistance chat like
WDS.gpt can search for contents in many documents and sources at the same time and clearly summarise only the most important information. The compiled information must be able to be traced back to its source at all times, so that information can
by a depositing assistant, which can answer questions such as “Why is the product weight not OK?”, “What order number does drive xy have?” or “How do I change the pump system?”. Equally, report functions would be easier to implement: AI-based assistants reliably carry out tasks such as “Prepare a daily report on OEE and plant errors” or “Send an e-mail report if you recognise a deviation in comparison to the last 30 days”. One of the greatest talents of a language- based AI is understanding and creating source code. We can make use of this talent when creating dashboards. We just have to explain to the AI in natural language what we would like to see and where the data is to be taken from. The AI can then create a clear Grafana dashboard with the necessary data.
Figure 4: AI-generated Grafana dashboard for plant performance
Kennedy’s Confection May 2025
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