The sales team responsible for custom synthesis currently uses an Excel-based solution for calculating standard pricing when the standard pricing model is applicable. The current standard pricing process is manually driven and is, therefore, time-consuming, error-prone and labour-intensive per enquiry from a potential customer. For these reasons, the custom synthesis sales team has been looking to optimise or at best automate their current process for some time. The project aims to clarify whether an ML-based solution is applicable or alternatives should be used.

A complete understanding of the requirements was obtained through internal interviews, discussions, and analysis of the current solution. This was followed by extensive literature research to grasp fundamental knowledge about ML and the essential programming language Python. With the knowledge gained, prototypes were developed in sprints and improved to extract significant findings. Several cycles resulted in a situation-dependent solution adapted to the company needs.

The final product of this project is a solution concept based on a self-developed python script that can be implemented as a back-end solution. With the realisation of the developed solution concept, including the integration of the developed python script back-end solution, all sales team requirements can be fulfilled and thus, process optimisation can be achieved. For the manual and time-consuming quoting process, with several control points that must be checked for each incoming custom synthesis request, the back-end solution can carry out everything automatically. A Further added value through automation results from the fact that manual work errors can be ruled out in the future. Furthermore, the back-end solution can notify all the necessary parties with the corresponding information after the processing. To conclude, the high turnaround times of the sales staff could be reduced and made more efficient in the future. From an economic point of view, this solution has the potential to save considerable sums of labour costs and working hours, which in turn can be used to serve customer satisfaction.