# Towards Flexible and Cognitive Production--Addressing the Production Challenges
## 1 Introduction  
Many industries are currently experiencing a transformation of their production processes, in a development which is often termed the 4 th industrial revolution or 'Industry 4.0' in short [1]. The reasons for this transformation are manifold. On the one hand, globalization often requires decentralized manufacturing processes and consequently the automation which supports and controls such processes. On the other hand, due to the customization of goods and products, manufacturers increasingly need to take their customer needs into account and tailor their products accordingly towards adaptive, flexible and intelligent production [2, 3].  
The Internet of Things [4], data analytics, machine learning (ML) [5], and other emerging technologies can be seen as major drivers behind the transition of traditional manufacturing processes. They enable both the realization of intelligence within automation components in the manufacturing process itself, the collection and provisioning of data in a distributed environment and consequently the realization of higher level services. Examples of references leveraging these technologies in different domains include machine monitoring [6], system security monitoring [7, 8], chemical plant optimization [9, 10], fault diagnosis [11], etc. Amidst this revolution, humans remain integral as the strategic decision makers rather than operators [12] interacting with the system and shaping its final decisions.  
Based on a real industrial use-case (explained in Section 2), this paper demonstrates how cognition resolves the major challenges of real-world assembly systems. These challenges include monitoring the high flexibility of assembly workers, the optimal planning and configuration of assembly lines, managing the large amount of data generated from different sensors and departments and ensuring efficient communication and overall safety on the shopfloor.  
The paper is structured as follows: Section 1 elaborates on the motivation and vision behind this paper. Then, Section 2 describes the use-case this paper is based on with the research expectations. Afterwards, Section 3 illustrates the various specifics involved in building an ideal cognitive assembly line, which is further illustrated in Figure 1. The next chapters indicate a step-by-step procedure to consider while moving towards cognitive production and addressing the relevant challenges in each specific field.  
We begin with Section 4, which describes a solution to assembly line balancing in the case of missing relevant data. The next section, Section 5, describes the use of cognition to support flexible assembly systems through process monitoring and perception systems. The topics of data contextualization, predictive maintenance, visual analytics, and causal discovery have been amplified in Section 6. The process followed here is a solely developed approach, but, to a certain degree, it follows principles similar to those of many data analysis methodologies (e.g., KDD, CRISP DM, SEMMA, etc. [13]). Section 7 expands the horizon into the field of communications and safety on the shop floor. Thus, this paper reviews the existing state of the art and pains a holistic picture of different technologies that have been developed to facilitate an intensified human-machine interaction. Additionally, the article indicates a roadmap for industries as well as researchers for consideration of the addressed topics in a cognitive production system.