Unmatched expertise. We combine years of experience in complexity management with the latest methods of data science. For you this results in fast, well-founded analyses and efficient decision-making. Simply smart.
Innovative tools. In order to provide optimal results, we use our own, cutting-edge data science solutions ComplexityStudio and ComplexityManager as well as leading tools from out strategic partners.
Holistic approach. We support you with our analytical-based methods to solve your challenges regarding market, product, process and organizational complexity.
Impressive results. Identifying over 100 loss-makers, reducing lead times by up to 25 %, significantly increasing transparency of the product portfolio - just some examples of what we achieved in a very short time using Smart Complexity Management.
Data Based Methods
A world market leader in the field of explosion protection approached us with this question. The core challenge of our client was the profitable operation of the supply chain despite very small quantities for certain product variants. This applied particularly to control and connection technology products. The expanded product range had an impact on order processing and especially on the production processes.
In close cooperation with the customer's expert teams, we analyzed more than half a million data points and initially established a sustainable database. By applying data-based variant management methods, we quickly were able to generate reliable results and identify opportunities for optimizing the product portfolio. About 23 % of the total costs of the considered area could be addressed by implementing the elaborated optimizations. Concrete fields of action were derived and jointly prioritized for the implementation of this potential worth several millions.
However, these often remain undetected in companies due to concerns about insufficient data quality. Together with our project team a German automotive supplier faced the challenge of visualizing and analyzing the engineering change process of products based on digital footprints within the deployed PLM system.
Initially, the required data for the process under consideration was compiled in cooperation with the customer. In the next step, our project team evaluated the data quality and laid the foundation for a successful application of Process Mining by performing a tool-supported data preparation. The as-is process was then digitally reproduced based on the data and analyzed using process mining methods. Within a short period of time, KPIs were elaborated to benchmark the complexity, performance and resources. It was discovered that there are considerable deviations from the manually described process, leading to a degree of standardization of only 3 % in the considered process. Based on the results, concrete optimization actions were derived to improve the engineering change process.