# PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
## 5 PDCA 4.0's Framework Dynamics  
As previously stated in Section 3.2.4, a more holistic and direct approach towards the application of I4.0 to CI is needed. Supported by both the previous analyses of the applicability of I4.0's technological concepts and the definition of the requirements for a new CI approach (matching visible in Figure 4), the vision for PDCA 4.0 is defined. Figure 5 visually represents its framework. Figure 4: 14.0 technological concepts for the PDCA 4.0 methodology. Figure 5: PDCA 4.0 framework. In the beginning of the CI process, namely the activities of KPI analysis, mapping, and diagnosis, Big Data collected automatically from the production system (R1) by Sens&Act and MES systems are stored in Cloud databases (Cloud), which also contain data regarding the whole value chain via eVC. The various types of data can then be integrated, forming the heart of a factory digital twin (Sim&Virt) that represents a virtual model of the physical world. The role of IoT is the same throughout this framework, ensuring connectivity and data transmission between machines and software. With these technologies, data coming from the shop floor, as well as from the value chain, can be used by CI teams for data-driven PDCA, avoiding situations where data are insufficient, obsolete, or inexistent. The stored data can then be processed by advanced big data analytics (Big Data) (R2), generating information regarding KPI analysis and mapping activities, through which the current state of the production system is known. A prediction of the future state may also be possible via predictive analytics (Big Data) (R3). For analytics (Big Data) and simulation services (Sim&Virt), cloud computing (Cloud) can be a supporting technology for providing the necessary computing ability. The information can be displayed in real-time to employees (to assist those responsible for the improvement to make decisions) through CPS, MES interfaces, eVC digital platforms, and AR/AsR devices (Sim&Virt) (R4), with its goal being the identification of negative critical aspects existing (or that may exist) in the organization. More specifically towards equipment, machine fault detection and prediction is possible through AutRob and CPS with embedded machine learning algorithms (R10). The PDCA database (R8) allows access to digitalized information regarding already identified problems, supporting the process of finding critical parameters and factors. This can also correspond to the employee's suggestions that are stored in the database (Cloud). All of these advanced data processing systems must have a user-friendly and simple-to-use interface, where the user (who is not an expert in data analytics) simply has to ask for a correlation analysis to the interface, and receives the output of several data analytics algorithms in the form of degree of influence and/or level of correlation (allowing an informed root cause analysis). When the critical aspect is identified, an automatic diagnosis can be achieved via an advanced data analysis tool (Big Data) (R2) that retrieves historical and current data from the factory digital twin (Sim&Virt) in order to construct a diagnosis regarding the critical aspect. Predictions for the future state (Sim&Virt) (R3) can also be used in order to give emphasis to the hindered performance of the production system. This type of information could also be accessed and visualized through the same technologies stated in the previous paragraph (R4). Additionally, for the next stage of the CI process, advanced big data analytics, such as correlation analysis and root cause analysis, or a machine learning algorithm that infers cause-effect relationships based on a historical problem-solving database (R8), can be used to help the CI team determine the root causes of a problem. These functionalities ensure that manual, simple, and inefficient analysis tools are avoided, allowing the CI team to save time in carrying out these tasks.