# Towards Flexible and Cognitive Production--Addressing the Production Challenges
## 6 Cognitive Data-Driven Approach to Improve the Assembly Line
### Causal Discovery  
As predictive maintenance methodologies mitigate the errors occurring in an assembly line, causal discovery, on the other hand, analyses the root cause for this error. Simply stated, these approaches aim to provide similar conclusions as traditional engineering methods such as _Five whys_. However, these methods extend their applicability to the complex production systems where traditional approaches would be time-consuming or  
Figure 9: An overview of EoL testing process, reproduced with the permission of [86], Elsevier, 2021.  
impossible to perform. In our use-case, causal discovery has a dual role. In the first role, a perspective on causal reasoning focused on an assembly process is taken, i.e., discovering causal relationships in the data collected from the worker (IMU sensor, depth sensor and camera) to better understand the root causes of quality properties in the context of a single worker. In the second role, causal discovery takes the global perspective and utilizes data beyond the assembly stations to discover causal interdependence of different production processes and their impact on the overall product quality. In the following subsection, the role of causal discovery in the context of industrial processes is described and compared to other data-driven and traditional, domain-driven approaches. The applicability of data-driven approaches in industrial environments is on the rise due to the increased availability of raw and contextualized data. Compared with the domain-driven approaches for process improvement, data-driven approaches can address three important aspects of industrial data [88]:  
* ML approaches can learn to model nonlinear and complex relationships;
* In addition, once the model is trained, it can capture possible hidden relationships which enable better predictions on unseen data in the future;
* These approaches do not impose any restrictions on the input variables and their distribution. With the increase in their complexity, however, especially with the approaches like deep learning, ML models are becoming extremely difficult to explain and hence are often referred to as black-box models [89]. The increasing complexity, limited explainability, and interpretability of the complex ML models make it difficult to address the emerging requirements for acceptance of these models and hinders their applications in industrial and mission-critical scenarios. Furthermore, a significant amount of these methods are based on associational patterns and correlation, which does not provide any insight into causal relationships, i.e., the underlying driving forces and generative mechanisms [90]. These aspects can be addressed through causal discovery, which goes beyond statistical dependency and focuses on cause and effect relationships. An advantage of having knowledge about causal relationships rather than statistical associations is that the former enables prediction of the effects of actions that perturb the observed system [91] and can drive the knowledge discovery through the detection of underlying driving forces in the assembly process. While the gold standard for identifying causal relationships is controlled experimentation, the experiments these require are very expensive, time-consuming and as a result often impossible to perform. Understanding causal relationships allows generalizations to be made in the absence of test data [92]. This is especially prominent in our assembly line use-case where, due to mass customization and high flexibility of the assembly line, process stability is crucial. There are many possible applications of causal discovery for process improvement in an assembly process. An approach presented by [PERSON] et al., in [93], focuses on the discovery of influencing parameters of the quality in a production scenario. This discovery is done through an approach that incorporates development of the predictive model and its verification through discovery interviews with the domain experts, visual analytics, and predictive model analysis.