# Integrated Multilevel Production Planning Solution According to Industry 5.0 Principles
## 2 Related Works--Literature Review
### Challenges for the Production Planning in Industry 5.0  
Based on the available literature, a few conclusions regarding state-of-the-art production planning systems in Industry 4.0 and 5.0 can be considered. Production planning faces similar challenges to the overall implementation of Industry 4.0 [9]. Although the concept has existed since 2011, there is very little evidence of implementation to a high degree, especially for process planning and scheduling. Integrated production planning systems that should be implemented have a theoretical framework available, but with respect to their complexity, they were not implemented at full capacity. One of the main characteristics of smart production planning is the collection and analysis of real-time data [53; 54; 55]. It can be used to optimize the production plans and therefore plays a very important role. The data should be of high quality and lead to data-driven decisions while retaining the importance of humans in this kind of process [56; 57; 58]. The importance of decision support systems within Industry 4.0 is very high, as well as that of decentralized decision-making. It would improve the time-consuming, non-value-added traditional centralized decision-making activities and enable accurate and efficient production planning [59]. The use of certain optimization algorithms has proven to be efficient in production planning operations, but the complex use of artificial intelligence has not been described and there is no clear evidence of its implementation at a higher level [47; 60; 61]. Internet technologies are one of the most common and most important parts of Industry 4.0 and 5.0. Also, they are some of the technologies with the highest availability and therefore have an easier implementation at various levels. The use of cloud technologies in production planning and scheduling is also one of the most common segments of the new system both in theory (on a more complex level, related to the Internet of things) and in practice (at a simpler level) [21; 37; 38]. Therefore, the combination of traditional production planning and scheduling methods, which include certain optimization algorithms, can improve the process, but the further use of the AI concept has not yet been proven [35]. In design-to-order (or engineer-to-order) production, due to its high degree of complexity in the planning and coordination, the goal is to achieve complexity reduction through near real-time feedback, which can be provided by digitally automated continuous (re-)planning to avoid the system range shifting outside the design range. The scheduling phase is based on the idea that a schedule should entail the following functionalities: (a) short-term based replanning; (b) capacity planning; and (c) real-time planning, which means considering the actual progress of the different tasks on site [39]. The requirements of such systems are the following: (1) plan capacity at the customer inquiry stage to establish the due date of the project; (2) deal with uncertainty in the process and product specification by establishing confirmation points for delivering products and information; (3) deal with customer-oriented, nonrepetitive production and variable routings; (4) adopt different levels of planning and control, distinguishing project flow control from production unit control; and (5) manage the interface between production and sales [62]. The main tactical planning activities that should be covered by production planning systems are: (1) defining preliminary product specifications; (2) determining detailed engineering activities and resources; (3) estimating lead times and costs and setting due dates; and (4) identifying needs for external capabilities and additional capacity [63]. Even with relatively high levels of digitalization, the role of humans within the production planning system remains high [42; 64]. The control and development functions must remain under human supervision, and teamwork is required for the production planning process optimization.