Our recently held webinar with MM MaschinenMarkt was a great success, and we thank you for your active participation and the many questions you raised.
The discussion ranged from technical aspects of implementing our AI software to training and application of neural networks, to practical scenarios in mechanical and plant engineering. To continue providing you access to this valuable information, we have carefully curated the Q&A session in our knowledge database.
1. How long does the implementation of the software take in a company with approximately 150-200 employees, and what needs to be done beforehand?
The implementation time can vary, but typically we see that an initial benefit becomes visible within 6-8 weeks. Before the implementation, the skills of the employees and their non-project-related activities such as vacation and training should be maintained in the system.
2. Does the training of the neural network consider project success or just the frequency of replacements?
The neural network is primarily trained based on the frequency of replacement relationships. The resulting project success does not directly influence the training, as it depends on a variety of factors that the network cannot isolate.
3. Can the system simulate different prioritization scenarios for projects?
Scenarios are possible but must be decided by the user. Priority indicators such as project score serve as guidelines in this regard.
4. How does the software handle discrimination risks, such as frequent employee illness?
The software treats all employees neutrally and does not consider personal circumstances like sick days. For planning purposes, only employee availability without underlying causes is taken into account. Can Do does not understand what "illness" is, as the machine does not analyze the labels of such objects.
5. Are the experiences of project managers from different industries equally valued in the training of AI?
The training of the AI relies on cross-industry, universally applicable project management principles. Specific industry knowledge is not incorporated into the training to ensure broad applicability.
→ More about this in our knowledge contribution
6. Is it possible to generate some form of Lessons Learned or project overview from the AI after the project is completed or at the end of the year?
The software allows for saving base plans that can serve as a basis for Lessons Learned. Statistical evaluations can also be generated for different projects, although without commercial assessment, but rather as a "run-through assessment.
→ More about this in our knowledge contribution
7. How does the software account for frequent employee absences?
The software plans based on available information and can consider absences such as vacation and training. Regular absences without specified reasons are treated as general unavailability.
8. How is the presentation and clarity ensured with multiple simultaneous development projects with numerous subtasks?
The software provides clear structuring and visualization of projects and their subtasks. Through filtering functions and custom views, you can adjust clarity as needed.
→ More on this in a personal conversation
9. How does the software address the issue of explainability of its decisions?
The software provides transparency in its processes and allows users to understand the logic behind the decisions of the AI, thereby supporting the explainability and acceptance of the system decisions.
→ Here's an explanation of this topic
👉 Here is the recording
For those who could not participate or would like to review the contents again, the recording of the webinar is available. We invite you to watch the session to gain a comprehensive overview of our AI-powered software solution and discover the diverse opportunities it offers for your business.