## 1 Introduction  
Digitalisation, artificial intelligence (AI) and cloud computing are changing the way we work, think and act in every aspect of our lives. The last disruptive technological leap of similar magnitude was the invention of the car or electricity. Of course, this development also affects products, transforming them into cyberphysical products or Product Service Systems (PSS) [1]. Furthermore, the established virtual product development process has to change and adapt to the new situation and development goals [1]. Therefore, existing data are part of the new critical assets for business success [2]. Most companies have a large amount of complex data at their disposal [3], representing the company's knowledge over a long time. Integrating methods and tools that enable the evaluation and usage of existing data is crucial to transforming virtual product development into the new paradigm of Digital Engineering (DE). [PERSON] et al. [4] define **Digital Engineering** as the consistent use and evaluation of data generated during design, testing and operation. The evaluation is performed using data-driven methods. Therefore, there is a growing number of techniques that use data to evaluate unknown relationships. With those methods, decision problems are supported. For example, multi-objective supply chain models can help to minimise transportation cost or delivery time [5]. They can also generate or preserve corporate knowledge by making it explicit. When small and medium-sized enterprises (SMEs) try to integrate these Digital Engineering methods, they face the problem of missing knowledge about the methods due to the lack of specialised staff focused on these new methods. This missing knowledge results in a competitive disadvantage for these mostly locally operating companies compared with large, international enterprises. The abovementioned DE methods support or make decisions based on data [6]. They are summarised by the phrases Data Science or AI. Well-known paradigms are Machine Learning (ML) [7] and Data Mining (DM) [8], as well as their subsequent methods and algorithms. In this paper, DE methods are mainly referred to as Data Mining and Machine Learning methods since those cover most use cases of product development. The following gives a short clarification and definition of the terms. Figure 1 shows a categorisation of the relevant terms. **Data Science** is a research field dealing with topics and tasks that work with data, such as data collection, storage, analysis, preparation, visualisation and evaluation. Data Mining, Machine Learning and other methods, such as statistics and business research, are used. Data Science aims to extract knowledge from large amounts of data to develop management recommendations. With these recommendations, companies can optimise production or development processes and improve the quality of business decisions [10]. **Artificial Intelligence (AI)** aims to transfer human intellect capabilities to machines, especially computer programs. This intelligence includes, but is not limited to, the abilities of reasoning, learning, goal setting, problem-solving and adaptability. A key aspect is the provisioning for performing self-correction. Such solutions can use the facilities of human intelligence differing from the observations of living beings, for example, by a higher processing speed of large-scale computations [11; 12]. **Data Mining (DM)** aims to identify and analyse new, previously unrecognised knowledge in data. The main use cases of DM are classification, regression, segmentation and clustering. Therefore, algorithms similar to those from Machine Learning are used [8; 13]. **Machine Learning (ML)** is a subfield of AI that designs algorithms so that systems can learn from previous data sets through experience and optimise themselves. By 'feeding' the algorithms with data, the system can adapt its programming to become more efficient for specific activities and make its own decisions based on unknown data.