Paolo Pappe

Project engineer
Practice Areas:
Wind energy potential areas, artificial intelligence, data science, factory planning
+49 (0)511 279 76-446


Development of a prototype for automated data quality control at Trivium Packaging GmbH, a manufacturer of metal packaging. The project aimed to ensure the reliability of production data for KPI calculations by implementing plausibility checks for machine and line status to identify faults at an early stage. The use of AI and algorithms to monitor and evaluate data quality in real time led to improved efficiency and performance of the production lines. A guide to ensuring data quality for reliable KPI calculations was also created to help small and medium-sized enterprises increase their competitiveness.

Data quality, production, manufacturing, KPI, metrics

An ageing society and the emergence of new diseases are generating rapid growth in the healthcare industry in many industrialized countries. The use of AI can lead to an increase in performance while at the same time reducing costs. This is why the use of AI in the medical technology is continuously increasing, driven by the numerous benefits it brings, including

  • Diagnosis can be significantly optimized by incorporating extensive experiential knowledge into and retrieving it from AI systems. In this respect, AI enables us to analyze images, laboratory results and patient files can be analyzed and evaluated.
  • Individualized treatment plans can be developed through the use of AI can be developed. These plans holistically take into account all aspects of the patient and thus help to significantly increase the effectiveness significantly increase the effectiveness of the therapy.
  • Predictive analyses are made possible through the use of AI. AI can identify risk factors and predict complications.

AI, Unsupervised Learning, Diagnostics

Energy transition yes, but a wind turbine nearby? No! The expansion of wind energy is urgently needed, but resistance in the neighborhood and from nature conservation associations delays or stops many construction projects. Using artificial intelligence (AI), the interdisciplinary WindGISKI joint project aims to accelerate the expansion of wind energy. Eight companies, associations and research institutions are developing a geo-information system that will predict the prospects of success for wind energy construction projects.

artificial intelligence, AI, energy transition, wind energy, land evaluation, geoinformation system

Supply chain resilience is massively gaining importance for manufacturing companies in times of severe disruption due to crises. Supplier selection is a key aspect of building a resilient supply chain. Currently, however, there is no holistic method for supplier selection that takes resilience into account. This paper therefore presents a research project that aims to develop an assessment measure for resilience in the context of supplier selection. The aim is to consider the existing resilience from the supplier company’s perspective and the required resilience from the selecting company's perspective.

Logistics, Supplier Selection, Resilience, Supply Chain, Supply Chain Management

Volatile markets and increasing product variance lead to more complex internal material flows. In order to cope with this, a significant increase in the flexibility and adaptability of prevailing intralogistics systems is necessary. (Small-scale) modular conveyor systems can be used to make intralogistics more flexible. Obstacles for the practical use are the low distribution as well as the high investment costs. In order to reduce reservations as well as risks, an evaluation and optimization method as well as an applicationoriented planning tool for modular conveyor system layouts were developed in a research project. It enables both planning service providers and users to evaluate modular conveyor systems and to exploit their potential.

Conveyor Technology, Layout Planning, Optimization, Genetic Algorithm, Software

Research projects