## 1 Introduction  
Since the fourth industrial revolution, the majority of industry sectors are compelled to undergo a digital transformation. Besides industries that have the ability to accommodate more rapidly, especially the heavy industry, and within this sector, metal processing facilities have additional obstacles to overcome. This applies especially to small and medium-sized enterprises (SMEs) in this specific industry segment. The required skills, as well as the necessary budget for the implementation of a state-of the-art digitalization solution are conditions that these companies often cannot fulfill, resulting in a reduction of the velocity of the digital transformation [1, 2, 3, 4]. Additionally, in-use machine systems within this environment tend to have significantly longer life spans than other industry segments, resulting in a higher amount of mostly more complex brownfield digitization and digitalization approaches [4, 5, 6].  
To utilize the full potential of the fourth industrial revolution, all enterprises within a supply chain must be able to communicate with other involved entities. Therefore, a common basis for communication is imperative, as this results in a holistic compatibility and extensibility of such. For this purpose, the Reference Architecture Model Industry 4.0 (RAMI 4.0) supports a standardized technical communication within and between different layers which is necessary for a successful digital transformation [7; 8; 9]. Although the utilization of such a methodological approach should result in a reduction of complexity, the integration of relevant data into the value chain results in a significant increase of required data processing technologies and database management systems (DBMS). Additionally, the ongoing approaches to make internal data available to other stakeholders within a value chain, Big Data and correlating technologies gain significant importance in the manufacturing environment, as state-of-the-art research implies [10; 11; 12; 13; 14].  
This paper aims to concretize the role of Big Data within this increasingly connected environment by utilizing the RAMI 4.0 framework for the digitalization of a small-scale value chain at the Montanuniversitat Leoben, which emulates the integration of SMEs within such a connected supply chain.  
The paper is structured as follows: A state of the art literature research of the key enablers of the fourth industrial revolution, namely, Smart Factories, Big Data, Cyber-Physical Production Systems (CPPS), Industrial Internet of Things (IIoT), Digital Twins (DT), and Cloud Computing is presented in Section 2. In Section 3, the integration of SMEs within such an environment is discussed in more detail. Furthermore, as part of the MUL 4.0 project at the Montanuniversitat Leoben, a small-scale digitalized value chain that serves as a practical demonstration for the theoretical implications resulting from Section 2, is discussed. In addition, machine learning modeling approaches for SMEs and big enterprises with special emphasis on the process level and therefore CPPS are addressed. The resulting value chain consists of several enterprises of different sizes and with different equipment and processes. Based on these chapters, Section 4 demonstrates and critically discusses the concretization of the RAMI 4.0 framework and the role of Big Data for the MUL 4.0 value chain. Based on this analysis, generalizations for the metal processing manufacturing environment are drawn. In the closing section, a brief conclusion and outlook regarding this topic are given.